Podcasts about aws lambda

Send us Fan MailThe breach that takes down a company often does not kick in the front door. It walks in through a “simple” integration you set up months ago, powered by a token no one remembered to rotate. We start with a real-world Zapier-style scenario and unpack how researchers chained together a harmless-looking code block, an AWS Lambda environment, and a misconfigured IAM role to reach private repository files and ultimately an NPM token that could enable a supply chain attack.From there, we zoom out to the bigger cloud security problem: non-human identities. Service accounts, API keys, and OAuth tokens multiply fast, and they are frequently overprivileged, poorly tracked, and left active long after an integration is retired. We also talk about why SaaS-to-SaaS connections are so hard to secure, and why agentic AI makes visibility even more urgent. If you do not know what systems are connected, what data crosses those links, and who owns the risk, you are effectively trusting an invisible tunnel into your environment.To make this actionable, we lay out a four-phase third-party risk management (TPRM) framework you can apply immediately: build a vendor and integration inventory with tiering, run real due diligence (SOC 2 Type II, ISO 27001, data access scope, subprocessors and fourth parties), lock protections into contracts (DPA language, right to audit, breach notification expectations), then enforce ongoing monitoring and governance with quarterly token reviews, logging, and incident response playbooks. If you are studying for the CISSP, you will also see exactly how this maps to Domain 1, Domain 3, Domain 4, and Domain 5.Subscribe for more practical CISSP training, share this with a teammate who owns vendor approvals, and leave a review so more security pros can find it. What is the one integration you would audit first?Gain exclusive access to 360 FREE CISSP Practice Questions at FreeCISSPQuestions.com and have them delivered directly to your inbox!  Don't miss this valuable opportunity to strengthen your CISSP exam preparation and boost your chances of certification success. Join now and start your journey toward CISSP mastery today!

BoxLang is a modern dynamic JVM language built for rapid application development. It's 100% Java-interoperable, compiles to JVM bytecode, and deployable anywhere from OS to AWS Lambda to Spring Boot. In this episode, we sit down with Luis Majano (CEO of Ortus Solutions and creator of BoxLang) and Cristobal Escobar (BoxLang community manager) to dig into the wave of innovation that has hit the platform over the past few months.We cover the BoxLang AI v3 release, a major overhaul that ships multi-agent orchestration with parent-child hierarchies, an AI Skills system based on Anthropic's open standard, MCP server integration (both consuming and serving), a composable middleware layer with six built-in classes including a FlightRecorder for deterministic CI testing, and a unified API spanning 17 AI providers. Luis and Cristobal walk us through the highlights of a 7-part BoxLang AI deep dive series, covering tools, memory systems & RAG, streaming, middleware, and MCP. We also touch on the BoxLang Spring Boot Starter, BoxLings (an interactive TDD/BDD learning platform), and TestBox 7's real-time streaming test runner.Whether you're a Java developer curious about dynamic JVM languages, an AI engineer looking for a productive alternative to Python-based agent frameworks, or just want to see what the JVM ecosystem can do in 2026, this episode is for you.GuestsLuis MajanoFoojay author pageLinkedInCristobal EscobarFoojay author pageLinkedInLinksOn the BoxLang website:BoxLang docsBoxLang AI docsBoxLang AcademyBoxLang for desktop applicationsBoxLang Spring Boot StarterBoxLingsAnnouncing MatchBox Open Beta: BoxLang, Now Running in New PlacesTry BoxLangOn Foojay:Overview of all recent BoxLang AI articles: Complete Guide to Building AI AgentsBoxLang AI v3 Has LandedBoxLang AI Deep Dive series, Parts 1–7How to Develop AI Agents Using BoxLang AI: A Practical GuideIntroducing the BoxLang Spring Boot StarterIntroducing BoxLings!Introducing skills.boxlang.io — The Open Agent Skills Ecosystem for BoxLang & the Ortus WorldContent00:00 Introduction of topic and guests01:17 What is BoxLang and how to use it05:25 Multi-runtime (WASM) with MatchBox, based on Rust07:00 Combining BoxLang with Spring Boot10:40 The abstraction approach in BoxLang AI, compared with LangChain4j and others14:18 Markdown skill files similar to Claude are also used in BoxLang AI15:21 About the 7-part Foojay BoxLang Deep Dive posts series, agents, event-driven,...19:28 BoxLang can be used for MCP server and client23:01 Premium features in BoxLang and building a company on an open-source project27:52 BoxLings, an interactive learning tool for BoxLang that teaches TDD and BDD30:25 TestBox 7, real-time streaming test execution and a browser-based IDE32:58 How to get started with BoxLang?34:14 How the evolutions in the JVM and Java language influence BoxLang development39:33 Which article to read first on Foojay about BoxLang?43:27 More learning resources and ideas for the future and desktop development48:05 Conclusions

In this episode, Corey Quinn sits down with AWS Senior Principal Engineer David Yanacek to explore the next evolution of DevOps.After two decades of building systems to reduce operational pain, David shares how AWS's new DevOps Agent is pushing automation to a whole new level, autonomously diagnosing incidents, suggesting fixes, and proactively improving systems before engineers even log in.From pager overload to autonomous remediation, this conversation is a glimpse into a world where software isn't the bottleneck anymore, operations are evolving into something entirely new.If you care about DevOps, SRE, platform engineering, or just want fewer 3 a.m. alerts, this episode is for you.Show highlights: (00:00) DevOps Meets Agents(00:13) Welcome and Sponsor Break(01:29) David Yanacek Backstory(02:34) DevOps Roots at Amazon(04:22) DevOps Agent GA Overview(05:32) LLMs MCP and Any Cloud(08:32) Guardrails and Safe Changes(11:47) Beta Results and Consistency(14:13) Troubleshooting Theory and On Demand(17:29) Future of DevOps and ClosingAbout David: David Yanacek is a Senior Principal Engineer at AWS and a lead advisor on the Agentic AI team. His current work focuses on Kiro, Amazon Bedrock AgentCore, and AWS's operational agents, where he helps shape the future of intelligent, autonomous systems.Over a 19+ year career at Amazon and AWS, David has been at the forefront of building services that simplify life for developers and operators. His experience spans serverless, DevOps, and CloudOps, including launching Amazon DynamoDB and AWS IoT Core, and contributing to the direction of cornerstone services like AWS Lambda, Amazon API Gateway, and Amazon CloudWatch.David also served as the lead publisher for the Amazon Builders' Library, helping customers apply Amazon's hard-earned architectural and operational lessons to their own systems.Outside of engineering, David plays the French horn in a local Seattle ensemble.Links:LinkedIn: https://www.linkedin.com/in/david-yanacek/Website: https://aws.amazon.com/builders-library/authors/david-yanacek/Sponsored by: duckbillhq.com

An airhacks.fm conversation with Holly Cummins (@holly_cummins) about: discussion about Quarkus energy efficiency and performance benchmarks, comparing Quarkus throughput and energy consumption to Spring Boot, the Quarkus Benchmarks repository and Spring-Quarkus performance comparison repository on GitHub, three times throughput and half the energy consumption with Quarkus, Quarkus build-time optimization and tree shaking, monomorphic vs megamorphic dispatching in the JVM, removing reflection at build time, the reactive core built on Vert.x enabling blocking APIs with reactive scalability, Quarkus dev experience and fast reload, build duration comparison between Quarkus and Spring Boot, the Writing Greener Java Applications white paper, the Energy Efficiency across Programming Languages study, Java ranking among the most energy-efficient languages, carbon-aware dispatching and Electricity Maps, zombie deployments and kubernetes cluster waste, serverless architecture with Quarkus on AWS Lambda, SnapStart for sub-second cold starts, Provisioned Concurrency cost savings, GraalVM native binaries vs JVM mode in serverless environments, CycloneDX SBOM generation in Quarkus, build-time vs runtime configuration for ISO 27001 security certification, Kruize Autotune for JVM hyperparameter optimization, JVM tuning folk wisdom and the copy-paste typo anecdote, Francesco Nigro's performance optimization work across the stack from assembly to JVM, Jeff Mesnil leading JBoss energy efficiency efforts, cheese fondue recipe, UK chocolate and Cadbury Roses Holly Cummins on twitter: @holly_cummins

Running Oracle Database@AWS is most effective when you have full visibility and control over your environment.   In this episode, hosts Lois Houston and Nikita Abraham are joined by Rashmi Panda, who explains how to monitor performance, track key metrics, and catch issues before they become problems. Later, Samvit Mishra shares key best practices for securing, optimizing, and maintaining a resilient Oracle Database@AWS deployment.   Oracle Database@AWS Architect Professional: https://mylearn.oracle.com/ou/course/oracle-databaseaws-architect-professional/155574 Oracle University Learning Community: https://education.oracle.com/ou-community LinkedIn: https://www.linkedin.com/showcase/oracle-university/ X: https://x.com/Oracle_Edu   Special thanks to Arijit Ghosh, Anna Hulkower, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode. ------------------------------------------------------ Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we'll bring you foundational training on the most popular Oracle technologies. Let's get started! 00:26 Nikita: Welcome to the Oracle University Podcast! I'm Nikita Abraham, Team Lead: Editorial Services with Oracle University, and with me is Lois Houston, Director of Communications and Adoption with Customer Success Services  Lois: Hello again! Last week's discussion was all about how Oracle Database@AWS stays secure and available. Today, we're joined by two experts from Oracle University. First, we'll hear from Rashmi Panda, Senior Principal Database Instructor, who will tell you how to monitor and log Oracle Database@AWS so your environment stays healthy and reliable. Nikita: And then we're bringing in Samvit Mishra, Senior Manager, CSS OU Cloud Delivery, who will break down the best practices that help you secure and strengthen your Oracle Database@AWS deployment. Let's start with you, Rashmi. Is there a service that allows you to monitor the different AWS resources in real time? Rashmi: Amazon CloudWatch is the cloud-native AWS monitoring service that can monitor the different AWS resources in real time. It allows you to collect the resource metrics and create customized dashboards, and even take action when certain criteria is met. Integration of Oracle Database@AWS with Amazon CloudWatch enables monitoring the metrics of the different database resources that are provisioned in Oracle Database@AWS. Amazon CloudWatch collects raw data and processes it to produce near real-time metrics data. Metrics collected for the resources are retained for 15 months. This facilitates analyzing the historical data to understand and compare the performance, trends, and utilization of the database service resources at different time intervals. You can set up alarms that continuously monitor the resource metrics for breach of user-defined thresholds and configure alert notification or take automated action in response to that metric threshold being reached. 02:19 Lois: What monitoring features stand out the most in Amazon CloudWatch? Rashmi: With Amazon CloudWatch, you can monitor Exadata VM Cluster, container database, and Autonomous database resources in Oracle Database@AWS. Oracle Database@AWS reports metrics data specific to the resource in AWS/ODB namespace of Amazon CloudWatch. Metrics can be collected only when the database resource is an available state in Oracle Database@AWS. Each of the resource types have their own metrics defined in AWS/ODB namespace, for which the metrics data get collected.  02:54 Nikita: Rashmi, can you take us through a few metrics? Rashmi: At Exadata database VM Cluster, there is CPU utilization, memory utilization, swap space storage file system utilization metric. Then there is load average on the server, what is the node status, and the number of allocated CPUs, et cetera. Then for container database, there is CPU utilization, storage utilization, block changes, parse count, execute count, user calls, which are important elements that can provide metrics data on database load. And for Autonomous Database metrics data include DB time, CPU utilization, logins, IOPS and IO throughput, RedoSize, parse, execute, transaction count, and few others. 03:32 Nikita: Once you've collected these metrics and analyzed database performance, what tools or services can you use to automate responses or handle specific events in your Oracle Database@AWS environment? Rashmi: Then there is Amazon EventBridge, which can monitor events from AWS services and respond automatically with certain actions that may be defined. You can monitor events from Oracle Database@AWS in EventBridge, which sends events data continuously to EventBridge at real time. Eventbridge forwards these events data to target AWS Lambda and Amazon Simple Notification Service to perform any actions on occurrence of certain events. Oracle Database@AWS events are structured messages that indicate changes in the life cycle of the database service resource. Eventbridge can filter events based on your defined rules, process them, and deliver to one or more targets. Event Bus is the router that receives the events, optionally transform them, and then delivers the events to the targets. Events from Oracle Database@AWS can be generated by two means: they can be generated from Oracle Database@AWS in AWS, and they can also be generated directly from OCI and received by EventBridge in AWS. You can monitor Exadata Database and Autonomous Database resource events. Ensure that the Exadata infrastructure status is an available state. You can configure how the events are handled for these resources. You can define rules in EventBridge to filter the events of interest and the target, who is going to receive and process those events. You can filter events based on a pattern depending on the event type, and apply this pattern using Amazon EventBridge put-rule API, with the default event bus to route only those matching events to targets. 05:13 Lois: And what about events that AWS itself generates? Rashmi: Events that are generated in AWS for the Oracle Database@AWS resources are delivered to the default event bus of your AWS account. These events that are generated in AWS for Oracle Database@AWS resources include lifecycle changes of the ODB network. The different network events are successful creation or failure of the creation of the ODB network, and successful deletion or failure in deletion of the ODB network. When you subscribe to Oracle Database@AWS, then an event bus with prefix aws.partner/odb is created in your AWS account. All events generated in OCI for the Oracle Database@AWS resources are then received in this event bus. When you are creating filter pattern using Amazon EventBridge put-rule API, you must set the event bus name to this event bus. Make sure you do not delete this event bus. Events generated in OCI and received into event bus are extensive. They include events of Oracle Exadata infrastructure, VM Cluster, container, and pluggable databases. 06:14 Lois: If you want to look back at what's happened in your environment, like who made the changes or accessed resources, what's the best AWS service for logging and auditing all that activity? Rashmi: Amazon CloudTrail is a logging service in AWS that records the different actions taken by a user or roles, or an AWS service. Oracle Database@AWS is integrated with Amazon Cloud Trail. This enables logging of all the different events on Oracle Database@AWS resources.  Amazon Cloud Trail captures all the API calls to Oracle Database@AWS as events. These API calls include calls from the Oracle Database@AWS console, and code calls to Oracle Database@AWS API operations. These log files are delivered to Amazon S3 bucket that you specify. These logs determine the identity of the caller who made the call request to Oracle Database@AWS, their IP from which the call originated, the time of the call, and some additional details.  CloudTrail event history stores immutable record of the past 90 days of management events in an AWS region. You can view, search, and download these records from CloudTrail Event History. You can access CloudTrail when you create an AWS account that automatically gives you the access to CloudTrail. Event history. If you would like to retain the logs for a longer period of time beyond 90 days, you can create CloudTrail trails or CloudTrail Lake event data store.  Management events in AWS provide information about management operations that are performed on the resources in your AWS account. Management operations are also called control plane operations. Thus, the control plane operations in Oracle Database@AWS are logged as management events in CloudTrail logs.  07:59 Are you a MyLearn subscriber? If so, you're automatically a member of the Oracle University Learning Community! Join millions of learners, attend exclusive live events, and connect directly with Oracle subject matter experts. Enjoy the latest news, join challenges, and share your ideas. Don't miss out! Become an active member today by visiting mylearn.oracle.com. 08:25 Nikita: Welcome back! Samvit, let's talk best practices. What should teams keep in mind when they're setting up and securing their Oracle Database@AWS environment?  Samvit: Use IAM roles and policies with least privilege to manage Oracle Database@AWS resources. This ensures only authorized users can provision or modify DB resources, reducing the risk of accidental or malicious changes.  Oracle Data Safe monitors database activity, user risk, and sensitive data, while AWS CloudTrail records all AWS API calls. Together, they give full visibility across the database and cloud layers. Autonomous Database supports Oracle Database Vault for enforcing separation of duties. Exadata Database Service can integrate with Audit Vault and Database Firewall to prevent privileged users from bypassing security controls. Enable multifactor authentication for AWS IAM users managing Oracle Database@AWS. This adds a strong second layer of protection against stolen credentials.  Always deploy your Oracle Database@AWS in private subnets without public IPs. Use AWS security groups and NACLs to strictly limit inbound and outbound traffic, allowing access only from trusted applications. Exadata Database Service supports integration with Oracle Vault for key lifecycle management. And in case of Autonomous Database, the transparent data encryption keys are automatically managed. But you can bring your own keys with OCI Vault. Key rotation ensures compliance and reduces risk of key compromise. Oracle Database@AWS enforces encrypted connections by default. Ensure clients connect with TLS 1.2 or 1.3 to protect data in transit from interception or tampering.  Use Oracle Data Safe's user assessment features to detect dormant users or excessive privileges. Disable unused accounts and rightsize permissions to reduce insider threats and security gap. Export database audit logs to Oracle Data Safe Audit Vault or AWS S3 with object lock for immutability. This prevents lock tampering and ensures audit evidence is preserved for compliance.  11:25 Lois: OK, that covers security. Do you have any tips for making sure your Oracle Database@AWS setup is reliable and resilient? Samvit: Start with clear recovery objectives. Define how much downtime and data loss each workload can tolerate. These targets drive your HADR architecture and backup strategy.  Implement business continuity measures to deliver maximum uptime for your databases. As a best practice, you must configure disaster recovery environment for your critical databases so that, in the event of any disaster affecting the primary database, applications can be immediately failed over to the DR environment, ensuring least application downtime and zero or minimal data loss. With Oracle Database@AWS, you can automate the creation and management of DR environment for your database services using different deployment capabilities. You can opt to configure either cross-availability zone DR in the same region or configure cross-region DR. Since cross-availability zone can only provide site failure protection, you must also configure a cross-region DR to protect against regional failure. A DR plan is only effective if tested. Regular failover and switchover drills validate that people, processes, and systems can recover as designed.  For Exadata Database, Autonomous Recovery Service provides automated backup validation, recovery guarantees, and protection against accidental data loss or corruption.  Oracle-managed backups are fully managed by OCI. When you create your Oracle Exadata Database, you can enable automatic backups by choosing Enable Automatic Backups in the OCI Console. When you do that, you can select Amazon S3 or OCI Object Storage or Autonomous Recovery Service as the backup destination. Don't just take backups. You also need to test them. Regularly restore backups into non-production environment to validate integrity and recovery time.  Plan beyond just the database. Map application and middleware dependencies to ensure end-to-end business resilience. A database failover is useless if dependent apps can't reconnect. 14:09 Nikita: Another area of interest is performance and cost. What practices help teams balance the two? Samvit: Autonomous Database automatically scales CPU and storage as workloads grow. This ensures performance during peaks while avoiding overprovisioning. So you should enable ADB auto-scaling.  Monitor CPU, memory, and IO metrics with AWS CloudWatch to rightsize your compute. Scale up or down based on actual utilization instead of static provisioning. Autonomous databases continuously evaluate and creates indexes automatically. This improves query performance without requiring manual tuning.  Use connection pooling in your applications to optimize database connections. Minimizing round-trip reduces latency and improves throughput. Apply AWS tags to database and related resources for cost allocation and chargeback. Tagging also helps with governance and cost visibility.  Choose between bring your own license and license-included models for Oracle Database@AWS. The right model depends on your existing license portfolio and cost strategy. Not all workloads need long backup retention. Adjust retention policies based on business needs to balance compliance with storage costs.  Exadata Database supports Oracle multitenant with pluggable databases. Consolidating databases reduces infrastructure footprint and licensing costs. Performance tuning isn't just technical. Align metrics with business KPIs. correlating DB performance to user experience and revenue impact helps prioritize optimizations.  16:20 Lois: Before we wrap up, Samvit, let's look at operational efficiency. What advice do you have for making day-to-day operations more efficient? Samvit: Use infrastructure as code tools like Terraform or AWS CloudFormation to automate provisioning. This ensures consistent, repeatable deployments with minimal manual errors.  For Autonomous Database, enable auto-start/stop to optimize costs by running databases only when needed. This is ideal for dev test or seasonal workloads. Exadata Database Service provides fleet maintenance to patch multiple systems consistently. This reduces downtime and simplifies lifecycle management.  Integrate AWS CloudWatch for performance monitoring and EventBridge for event-driven automation. This helps detect issues early and trigger automated workflows. Oracle Data Safe provides ready-to-use audit and compliance reports. Use these to streamline governance and reduce the effort of manual compliance tracking.  For Autonomous databases, Performance Hub simplifies monitoring while Exadata users benefit from AWR and ASH reports. Together, they give deep insights into performance trends. Automated tagging policies and change management workflows help maintain governance. They ensure resources are tracked properly and changes are auditable.  Monitor storage consumption and growth patterns using AWS CloudWatch and the ADB Console. Proactive tracking helps avoid capacity issues and unexpected costs. Send CloudTrail logs into EventBridge to trigger automated incident responses. This shortens response time and builds operational resilience.  18:36 Nikita: Samvit and Rashmi, thanks for spending time with us today. Your insights always help bring the bigger picture into focus. Lois: They definitely do. And if you'd like to go deeper into everything we covered, head over to mylearn.oracle.com and look up the Oracle Database@AWS Architect Professional course. Until next time, this is Lois Houston… Nikita: And Nikita Abraham, signing off! 19:03 That's all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We'd also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.

An airhacks.fm conversation with Thorsten Hoeger (@hoegertn) about: first computer experience with an IBM 8086 and learning programming by modifying the QBasic Gorilla game, early programming journey from QBasic to Visual Basic and the discovery of event-driven programming, building a password security script for autoexec.bat as a childhood project, transition from Visual Basic to Java around 2005 starting with Java 1.4.2, working at a small bank in Stuttgart building a core banking system, experience with Eclipse RCP rich client platform and the overhead of plugin architecture in business software, migration from Swing to Eclipse RCP frontend with JBoss application server backend, building a custom Spring-based microservice framework called Dwallin (Icelandic for dwarf) before Spring Boot existed, using Apache CXF for REST and RPC over messaging with ActiveMQ, comparison of Java development trajectories between annotation-based and XML-heavy approaches, discussion of the infamous Java and XML O'Reilly book that popularized XML configuration, xdoclet as a precursor to Java annotations, contrasting approaches of JBoss-based thin WAR deployments versus Spring-based embedded server microservices, university experience learning Ada programming language and its strict compiler as excellent for learning programming, PL/SQL's Ada-based origins, brief experience with OSGi and strong criticism of its complexity and poor developer experience, comparison of OSGi with Java Platform Module System (JPMS), founding Taimos consulting company 10 years ago originally building BlackBerry enterprise software, pivoting to AWS migration consulting for regulated industries including banks and insurance companies, strong preference for serverless architecture with lambda Step Functions API Gateway and DynamoDB, criticism of running kubernetes on AWS versus using native services like ECS Fargate, the distinction between running "in the cloud" versus "on the cloud", detailed discussion of why GraalVM native images are unnecessary on AWS Lambda due to compliance overhead and memory allocation model, quarkus and SnapStart as solutions for Lambda cold start problems, Java's cost efficiency on Lambda due to fast execution times, involvement with AWS CDK since 2018-2019 including building L2 constructs for EC2 and AppSync, shift from code contributions to community organizing and prioritization work with the CDK team, launching CDK Terrain as successor to CDK for Terraform, nuanced discussion of open source economics when the project primarily benefits a paid cloud provider, using GitHub as a personal index and dashboard for reusable project templates, consulting perspective on contributing to open source for code reuse across multiple clients, teaser for a future deep-dive episode on CDK internals and promoting Java usage with CDK Thorsten Hoeger on twitter: @hoegertn

AWS Morning Brief for the week of February 17th, with Corey Quinn. Links:Amazon Aurora DSQL is now available in additional AWS RegionsAmazon Bedrock adds support for six fully-managed open weights modelsAWS Config now supports 30 new resource typesAnnouncing new Amazon EC2 general purpose M8azn instancesAWS Network Firewall announces new price reductionsAmazon S3 Tables add partition and sort order definition in the CreateTable APIAmazon Athena adds 1-minute reservations and new capacity control featuresBuilding fault-tolerant applications with AWS Lambda durable functions Simplify cross-account stream processing with AWS Lambda and Amazon DynamoDBAutomated Reasoning checks rewriting chatbot reference implementationMastering Amazon Bedrock throttling and service availability: A comprehensive guideReservoir computing on an analog Rydberg-atom quantum computer

AWS Lambda is fantastic for small, stateless code on demand. But when your “function” starts looking like a workflow (retries, backoff, long waits, human approvals, callbacks), classic Lambda patterns can feel like a fight: 15-minute max runtime, no built-in state, and orchestration glue everywhere (Step Functions, queues, schedules, and state you did not want to own). In this episode of AWS Bites, Eoin and Luciano explore AWS Lambda Durable Functions, announced at re:Invent 2025. It's still Lambda (same runtimes and scaling), but with durable execution superpowers: named steps, automatic checkpointing, and the ability to suspend and resume from a safe point without redoing completed work. We unpack the replay/resume model under the hood, when this approach shines, and the gotchas (determinism, idempotency, replay-aware logging, debugging resumed runs). To make it real, we share how we rebuilt PodWhisperer v2 using Durable Functions to orchestrate a GPU-powered WhisperX pipeline, LLM refinement, speaker naming, and caption generation.In this episode, we mentioned the following resources: AWS announcement blog post: https://aws.amazon.com/blogs/aws/build-multi-step-applications-and-ai-workflows-with-aws-lambda-durable-functions/ Durable Functions best practices: https://docs.aws.amazon.com/lambda/latest/dg/durable-best-practices.html The replay model deep dive (Dev.to): https://dev.to/aws/the-replay-model-how-aws-lambda-durable-functions-actually-work-2a79 Build workflows that last (Dev.to): https://dev.to/aws/aws-lambda-durable-functions-build-workflows-that-last-3ac7 Testing Durable Functions in TypeScript (Dev.to): https://dev.to/aws/testing-aws-lambda-durable-functions-in-typescript-5bj2 Developing Durable Functions with AWS SAM (Dev.to): https://dev.to/aws/developing-aws-lambda-durable-functions-with-aws-sam-ga9 Hands-on notes: https://www.andmore.dev/blog/lambda_durable_functions/ PodWhisperer (open source): https://github.com/fourTheorem/podwhisperer/ WhisperX: https://github.com/m-bain/whisperX Do you have any AWS questions you would like us to address?Leave a comment here or connect with us on X/Twitter, BlueSky or LinkedIn:- https://twitter.com/eoins | https://bsky.app/profile/eoin.sh | https://www.linkedin.com/in/eoins/- https://twitter.com/loige | https://bsky.app/profile/loige.co | https://www.linkedin.com/in/lucianomammino/

Produktmanagement wird dauernd erwähnt, aber selten wirklich erklärt. Und genau da entsteht oft der Frust: Feature Requests prasseln rein, das Jira Backlog wächst wie Unkraut, Stakeholder eskalieren, und am Ende fragt sich jede:r im Team, wer hier eigentlich was entscheidet. Klingt bekannt? Dann ist diese Episode für dich.In dieser Episode schließen wir eine längst überfällige Lücke und steigen tief in das Thema Produktmanagement ein. Zu Gast ist Michael Gasch, Product Manager bei AWS im Serverless Umfeld. Mit ihm schauen wir uns an, was Produktmanagement wirklich ist, warum es nicht einfach Projektmanagement mit neuem Label ist und wie AWS Rollen wie PMT, SDM und TPM trennt, um Delivery, Priorisierung und Ownership sauber zu verzahnen.Wir sprechen über Working Backwards und PR/FAQ Dokumente, datengetriebene Priorisierung unter Dauerbeschuss, Paper Cuts vs. große Launches, Disagree and Commit, Bias for Action und wie Erfolg nach einem GA Launch über Metriken, Telemetrie und Kundenfeedback messbar wird. Als Praxisbeispiel nehmen wir ein echtes AWS Feature: Durable Functions in AWS Lambda, von der Idee im Kopf bis zur AWS re:Invent Bühne.Zum Schluss gibt es noch ein paar Tips:Wie kannst du proaktiver in Produktentscheidungen werden, bessere Inputs liefern und vielleicht sogar selbst Richtung Produktmanagement wechseln?Spoiler: Anforderungsanalyse, Ownership und ein bisschen STAR Methode können viel bewegen.Bonus: Wenn du dachtest, AI macht Produktmanager:innen überflüssig, warten hier ein paar ziemlich gute Gegenargumente auf dich.Unsere aktuellen Werbepartner findest du auf https://engineeringkiosk.dev/partnersDas schnelle Feedback zur Episode:

Chris McHenry, Chief Product Officer at Aviatrix, joined Doug Green, Publisher of Technology Reseller News, to discuss the launch of Aviatrix 8.2 and how the company is redefining zero trust security for modern cloud-native environments. McHenry explained that as critical business data and AI workloads increasingly reside in public clouds such as AWS, Azure, and Google Cloud, traditional perimeter-based security models are no longer sufficient. Aviatrix has spent the last decade building its Cloud Native Security Fabric, a platform designed specifically for cloud operational models rather than retrofitted on-premises approaches. With release 8.2, Aviatrix significantly expands its “zero trust for workloads” capabilities, focusing on Kubernetes, serverless environments, and AI-driven applications. A central theme of the conversation was the evolution of zero trust from a networking concept into a workload-centric security strategy. McHenry noted that recent supply-chain attacks have shown how quickly cloud-native environments can be compromised if basic network controls are missing. Aviatrix 8.2 introduces deeper Kubernetes awareness, policy-as-code integration, and initial native support for securing AWS Lambda, allowing organizations to apply micro-segmentation and least-privilege access directly to modern workloads. McHenry emphasized that cloud security must also evolve operationally. Security teams can no longer rely on slow, ticket-based firewall processes while developers deploy infrastructure at machine speed. Aviatrix 8.2 supports a DevSecOps-friendly model that enables developers to manage zero trust policies within guardrails defined by security teams. As McHenry put it, “If your workloads get more modern but your controls don't, security gets worse without you touching anything.” The discussion concluded with guidance for CIOs and CISOs preparing for the next wave of cloud and AI-driven threats: assess whether existing network security tools truly understand cloud-native workloads, modernize security operations alongside development practices, and prioritize platforms that unify cloud, network, and security teams. More information on Aviatrix 8.2 and the Cloud Native Security Fabric is available at https://aviatrix.ai/.

RexIDE - https://rex.mindmeld360.com⁠

When AWS has a major outage, what actually happens behind the scenes? Ben Hartshorne, a principal engineer at Honeycomb, joins Corey Quinn to discuss a recent AWS outage and how they kept customer data safe even when their systems couldn't fully work. Ben explains why building services that expect things to break is the only way to survive these outages. Ben also shares how Honeycomb used its own tools to cut their AWS Lambda costs in half by tracking five different things in a spreadsheet and making small changes to all of them.About Ben Hartshorne: Ben has spent much of his career setting up monitoring systems for startups and now is thrilled to help the industry see a better way. He is always eager to find the right graph to understand a service and will look for every excuse to include a whiteboard in the discussion.Show highlights: (02:41)Two Stories About Cost Optimization(04:20) Cutting Lambda Costs by 50%(08:01) Surviving the AWS Outage(09:20) Preserving Customer Data During the Outage(13:08) Should You Leave AWS After an Outage?(15:09) Multi-Region Costs 10x More(18:10) Vendor Dependencies(22:06) How LaunchDarkly's SDK Handles Outages(24:40) Rate Limiting Yourself(29:00) How Much Instrumentation Is Too Much?(34:28) Where to Find BenLinks: Linkedin: https://www.linkedin.com/in/benhartshorne/GitHub: https://github.com/maplebedSponsored by: duckbillhq.com

AWS Morning Brief for the week of December 8th, with Corey Quinn. Links:Introducing Amazon Route 53 Global Resolver for secure anycast DNS resolution (preview)Introducing AWS Lambda Managed Instances: Serverless simplicity with EC2 flexibilityAWS announces preview of AWS Interconnect - multicloudIntroducing AWS Transform custom: Crush tech debt with AI-powered code modernizationAmazon CloudWatch introduces unified data management and analytics for operations, security, and complianceAmazon EC2 P6e-GB300 UltraServers accelerated by NVIDIA GB300 NVL72 are now generally availableIntroducing AWS AI FactoriesIntroducing AWS DevOps Agent (preview), frontier agent for operational excellenceAmazon S3 Storage Lens adds performance metrics, support for billions of prefixes, and export to S3 TablesBuild multi-step applications and AI workflows with AWS Lambda durable functionsAmazon S3 increases the maximum object size to 50 TBAmazon S3 Tables now offer the Intelligent-Tiering storage classChina-nexus cyber threat groups rapidly exploit React2Shell vulnerability (CVE-2025-55182)Introducing Database Savings Plans for AWS Databases

In dieser Episode von AWS Cloud Horizonte spricht Oliver Steenbuck mit Malte Polley (Teamleader Data Analytics & AI) und Ahmet Akduman (Head of Business Organization) von MRH Trowe, einem der größten inhabergeführten Versicherungsmakler in Deutschland, über den Aufbau einer modernen Daten- und KI-Plattform auf AWS. Im Mittelpunkt steht die Frage, wie man eine stetig wachsende Flut an Dokumenten beherrschbar macht – und generative KI nicht als Buzzword, sondern als produktives Werkzeug einsetzt. Kernthemen der Episode: Klassifizierung und Routing eingehender Dokumente (Post, E-Mail, Scans) in einem schnell wachsenden Maklerunternehmen Aufbau einer skalierbaren Dokumenten- und Datenpipeline mit AWS-Services wie Amazon S3, AWS Lambda, AWS Step Functions, Amazon Textract und Amazon Bedrock Einsatz von generativer KI zur Dokumentenklassifizierung und -anreicherung: vom unstrukturierten PDF zum verwertbaren Datensatz Technische Stellschrauben: Kontextfenster, Tokenanzahl, Latenz, Kosten – und warum Vorverarbeitung oft wichtiger ist als „das größte Modell" Organisatorische Herausforderungen des Buy-and-Build-Ansatzes: Integration neuer Maklerhäuser, Harmonisierung von Prozessen und Systemen Lessons Learned auf dem Weg von der Idee zu belastbaren, produktiven Workflows Highlights: Ein konkreter End-to-End-Use-Case: vom Papierbrief mit QR-Code über den AWS Simple Email Service, S3, Textract und Bedrock bis ins Bestandssystem Wie MRH Trowe mit einem schlanken Team produktive KI-Workflows baut – ohne großes Data-Science-Lab Praktische Erfahrungen mit Prompting, Modellwahl (z. B. Anthropic-Modelle auf Bedrock) und Qualitätskontrolle Welche Dokumenten-Use-Cases heute schon gut funktionieren – und wo die Grenzen aktuell noch liegen Wie sich die Arbeit der Sachbearbeitung verändert, wenn KI Routineaufgaben übernimmt Über die Gäste: Malte Polley – Teamleader Data Analytics & AI bei MRH Trowe und verantwortlich für den Aufbau der Cloud-Infrastruktur und Plattform-Services Ahmet Akduman – Head of Business Organization bei MRH Trowe und Brückenbauer zwischen Fachbereichen, Prozessen und datengetriebenen Lösungen Host: Oliver Steenbuck (AWS)  

# 2025-11-18 - News - Episode 245# Hosts: - Daniel Garcia - Senior Developer at Ortus Solutions- Jacob Beers - Senior Developer at Ortus Solutions# summaryIn this episode of the Modernize or Die Podcast, hosts Daniel Garcia and Jacob Beers discuss the latest updates from Ortus Solutions, including the release of ColdBox 8 and BoxLang 1.7. They delve into new features such as server-sent events, serverless capabilities with AWS Lambda, and the introduction of SocketBox for WebSocket integration. The conversation also covers upcoming events, training opportunities, and important updates regarding CFML, including the end of life for ColdFusion 2021. The hosts emphasize the growing impact of BoxLang within the Java community and its new PDF handling capabilities.# TakeawaysColdBox 8 introduces groundbreaking capabilities for web development.The upgrade path from ColdBox 7 to 8 is smooth and efficient.Server-sent events allow real-time data streaming from server to client.BoxLang is making strides in serverless architecture with AWS Lambda.SocketBox simplifies WebSocket integration in ColdFusion applications.BXCompatUI facilitates easy migration from CFML to BoxLang.BoxLang is gaining recognition in the broader Java community.The new PDF handling features in BoxLang enhance document manipulation.ColdFusion 2021 has reached its end of life, with no further updates.Into the Box 2026 is a must-attend conference for developers.# Chapters00:00 Welcome00:18 Ortus News & BoxLang Updates14:24 CFML Updates17:30 Upcoming Events and Conferences20:31 Thank You# Join the Ortus CommunityBe part of the movement shaping the future of web development. Stay connected and receive the latest updates on, **product launches, tool updates, promo services and much more.**Follow Us on Social media and don't miss any news and updates:-  https://twitter.com/ortussolutions-  https://www.facebook.com/OrtusSolutions-  https://www.linkedin.com/company/ortus-solutions-corp-  https://www.youtube.com/OrtusSolutions- https://github.com/Ortus-Solutions# KeywordsColdBox, BoxLang, Ortus Solutions, serverless, WebSockets, CFML, Java, PDF handling, cloud deployment, software development ★ Support this podcast on Patreon ★

An airhacks.fm conversation with Philipp Page (@PagePhilipp) about: Discussion about refactoring AWS Lambda Power Tools to remove AspectJ dependency and introduce functional interfaces, comparison between AspectJ and lombok for code generation, benefits of offloading work to build time for AWS Lambda performance, using quarkus build-time optimizations with Jandex and gizmo utilities, replacing slf4j with Java System Logger to reduce dependencies, implementing log buffering feature that flushes debug logs only on errors for proactive debugging, thread safety considerations in multi-threaded AWS Lambda executions, using Embedded Metrics Format (EMF) for CloudWatch metrics without prometheus, caching Parameter Store values to avoid throttling limits, structured logging benefits for nested JSON queries in CloudWatch Insights, detecting cold starts without reflection using class initialization tricks, future support for Java 25 and modern Java features like Scoped Values, Maven and Gradle plugin development for annotation processing, custom serializers for Kafka and Avro messages, potential java.util.json support for lightweight JSON parsing, middleware chain pattern implementation for Power Tools utilities, differences between reactive and proactive debugging approaches, cost optimization through EMF metrics instead of Prometheus scraping, BCE (Boundary Control Entity) architecture pattern for business metrics, performance benefits of removing reflection from metrics utility, CDK integration considerations for generated classes, request stream handlers as reflection-free alternatives Philipp Page on twitter: @PagePhilipp

Send us a textIn this episode of What's New in Cloud FinOps, Stephen Old and Frank discuss the latest updates in cloud computing, including AWS Outposts' integration with third-party storage, new Amazon EC2 Mac instances, Azure's managed services, and Google Cloud VM Engine updates. They also explore pricing changes in Azure, the deprecation of Azure Machine Learning data labeling, and the introduction of new metrics in software development. The conversation highlights the importance of sustainability in cloud services and concludes with reflections on the podcast's five-year anniversary.TakeawaysAWS Outposts now supports third-party storage integration with Dell and HPE.Amazon EC2 introduces new Mac instances for developers.Azure managed services now include Grafana dashboards at no extra cost.Google Cloud VM Engine V1 SKUs are now end of sale.Azure UltraDisk pricing has been reduced significantly in specific regions.Azure Machine Learning data labeling will be deprecated by 2026.AWS Transform Assessment helps visualize storage migration benefits.New cost to serve software metric introduced by AWS.Cortex Framework now deploys sustainability modules for SAP.AWS Lambda cold start billing changes will take effect in 2025.

An airhacks.fm conversation with Philipp Page (@PagePhilipp) about: early computing experiences with Windows XP and Intel Pentium systems, playing rally car games like Dirt with split-screen multiplayer, transitioning from gaming to server administration through Minecraft, running Minecraft servers at age 13 with memory limitations and out-of-memory exceptions, implementing caching mechanisms with cron jobs and MySQL databases, learning about SQL injection attacks and prepared statements, discovering connection pooling advantages over PHP approaches, appreciating type safety and Object-oriented programming principles in Java, the tendency to over-abstract and create unnecessary abstractions as junior developers, obsession with avoiding dependencies and implementing frameworks from scratch, building custom Model-View-Controller patterns and dependency injection systems, developing e-learning platform for aerospace industry using PHP Symfony framework, implementing time series forecasting in pure Java without external dependencies, internship and employment at AWS Dublin in Frontier Networking team, working on AWS Outposts and Ground Station hybrid cloud offerings, using python and rust for networking control plane development, learning to appreciate Python despite initial resistance to dynamically typed languages, joining AWS Lambda Powertools team as Java tech lead, maintaining open-source serverless development toolkit, providing utilities for observability including structured JSON logging with Lambda-specific information, implementing metrics and tracing for distributed event-driven architectures, mapping utilities to AWS Well-Architected Framework serverless lens recommendations, caching parameters and secrets to improve scalability and reduce costs, debate about AspectJ dependency and alternatives like Micronaut and quarkus approaches, providing both annotation-based and programmatic interfaces for utilities, newer utilities like Kafka consumer avoiding AspectJ dependency, comparing Micronaut's compiler-based approach and Quarkus extensions for bytecode generation, AspectJ losing popularity in enterprise Java projects, preferring Java standards over external dependencies for long-term maintainability, agents in electricity trading simulations for renewable energy scenarios, comparing on-premise Java capabilities versus cloud-native AWS features, default architecture pattern of Lambda with S3 for persistent storage, using AWS Calculator for cost analysis before architecture decisions, event-driven architectures being native to AWS versus artificially created in traditional Java projects, everything in AWS emitting events naturally through services like EventBridge, filtering events rather than creating them artificially, avoiding unnecessary microservices complexity when simple method calls suffice, directly wiring API Gateway to DynamoDB without Lambda for no-code solutions, using Java for CDK infrastructure as code while minimizing runtime dependencies, maximizing cloud-native features when in cloud versus on-premise optimization strategies, starting with simplest possible architecture and justifying complexity, blue-green deployments and load balancing handled automatically by Lambda, internal AWS teams using Lambda for orchestration and event interception, Lambda as foundational zero-level service across AWS infrastructure, preferring highest abstraction level services like Lambda and ECS Fargate, only dropping to EC2 when specific requirements demand lower-level control, contributing to Powertools for AWS Lambda Python repository before joining team, compile-time weaving avoiding Lambda cold start performance impacts, GraalVM compilation considerations for Quarkus and Micronaut approaches, customer references available on Powertools website, contrast between low-level networking and serverless development, LinkedIn as primary social media platform for professional connections, Powertools for AWS Lambda (Java) Philipp Page on twitter: @PagePhilipp

AWS Morning Brief for the week of October 6th, 2025, with Corey Quinn. Links:Deploying AI models for inference with AWS Lambda using zip packagingAnnouncing Amazon ECS Managed Instances Amazon EBS increases the maximum size and provisioned performance of General Purpose (gp3) volumes Accelerating AWS Infrastructure Deployment: A Practical Guide to Console-to-Code AWS Builder ID now supports Sign in with Google Build a dynamic workflow orchestration engine with Amazon DynamoDB and AWS LambdaAWS Transfer Family adds support for additional IAM condition keys AWS Compute Optimizer now supports 99 new Amazon EC2 instance types

An airhacks.fm conversation with Alvaro Hernandez (@ahachete) about: Framework laptop experience and build process with DIY edition, modular connectors and upgradability, running Ubuntu 25.10 beta with nix package manager, automating installation with YAML and Ansible, comparison with IBM AS/400 feature activation model, docker adoption history for server maintenance and documentation, PostgreSQL extensions, upgradability and security concerns, challenges with packing 1000+ extensions into container images, security concerns with large monolithic images containing unused extensions, dynamic extension injection using sidecar pod local controller in kubernetes, problems with mutating running containers and security tool compliance, traditional Docker build approach requiring users to become image maintainers, challenging assumptions about container image immutability and Merkle tree, container images as JSON manifests pointing to tar file layers, Dynamic OCI Registry concept for composing images on-the-fly, generating manifests dynamically in milliseconds without Docker build, interface-based approach for mapping user preferences to layer digests, PostgreSQL-specific implementation with extension URL patterns, metadata storage in PostgreSQL database for layer digest resolution, potential applications for quarkus and Java microservices, serverless deployment possibilities with AWS Lambda, comparison with Cloudflare's serverless OCI registry, enterprise use cases for automated patching and security updates, integration possibilities with AWS EventBridge for CI/CD pipelines, transparency to Docker clients with only registry change required, stackgres platform using 4 million lines of Java code, ongres company services including PostgreSQL training and Oracle migrations, Alvaro's website: aht.es Alvaro Hernandez on twitter: @ahachete

An airhacks.fm conversation with Ronald Dehuysser (@rdehuyss) about: JobRunner evolution from open source to processing 1 billion jobs daily, carbon-aware job processing using European energy grid data ( ENTSO-E ) for scheduling jobs during renewable energy peaks, correlation between CO2 emissions and energy prices for cost optimization, JobRunner Pro vs Open Source features including workflows and multi-tenancy support, bytecode analysis using ASM for lambda serialization, JSON serialization for job state persistence, support for relational databases and MongoDB with potential S3 and DynamoDB integration, distributed processing with master node coordination using heartbeat mechanism, scale-to-zero architecture possibilities using AWS EventBridge Scheduler, Java performance advantages showing 35x faster than python in benchmarks, cloud migration patterns from on-premise to serverless architectures, criticism of kubernetes complexity and lift-and-shift cloud migrations, cost-driven architecture approach using AWS Lambda and S3, quarkus as fastest Java runtime for cloud deployments, infrastructure as code using AWS CDK with Java, potential WebAssembly compilation for Edge Computing, automatic retry mechanisms with exponential backoff, dashboard and monitoring capabilities, medical industry use case with critical cancer result processing, professional liability insurance for software errors, comparison with executor service for non-critical tasks, scheduled and recurring job support, carbon footprint reduction through intelligent scheduling, spot instance integration for cost optimization, simplified developer experience with single JAR deployment, automatic table creation and data source detection in Quarkus, backwards compatibility requirements for distributed nodes, future serverless edition possibilities Ronald Dehuysser on twitter: @rdehuyss

An airhacks.fm conversation with Ingo Kegel (@IngoKegel) about: jprofiler Visual Studio Code integration using Kotlin Multiplatform, migrating Java code to Kotlin common code for cross-platform compatibility, transpiling to JavaScript for Node.js runtime, JClassLib bytecode viewer and manipulation library, Visual Studio Code's Language Server Protocol (LSP), profiling unit tests and performance regression testing, Java Flight Recorder (JFR) for production monitoring with custom business events, cost-driven development in cloud environments, serverless architecture with AWS Lambda and S3, performance optimization with parallelism in single-CPU environments, integrating profiling data with LLMs for automated optimization, MCP servers for AI agent integration, Gradle and Maven build system integration, cooperative window switching between JProfiler and VS Code, memory profiling and thread analysis, comparing streams vs for-loops performance, brokk AI's Swing-based LLM development tool, context-aware performance analysis, automated code optimization with AI agents, business event correlation with low-level JVM metrics, cost estimation based on cloud API calls, quarkus for fast startup times in serverless, performance assertions in System Tests, multi-monitor development workflow support Ingo Kegel on twitter: @IngoKegel

AWS Morning Brief for the week of September 2nd, 2025, with Corey Quinn. Links:How Ancestry optimizes a 100-billion-row Iceberg tableMastering Amazon Q Developer with Rules Bob's Used Books: Build a .NET Serverless Application on AWS – Part 2: ArchitectureHow Amazon Finance built an AI assistant using Amazon Bedrock and Amazon Kendra to support analysts for data discovery and business insights Building Your Open Source Commercial Strategy with AWSHow to optimize Amazon RDS and Amazon Aurora database costs/performance with AWS Compute OptimizerGracefully handle failed AWS Lambda events from Amazon DynamoDB StreamsAnnouncing the AWS Billing and Cost Management MCP serverAWS joins the DocumentDB project to build interoperable, open source document database technologyCount Tokens API supported for Anthropic's Claude models now in Amazon Bedrock

In this episode of the AWS Podcast, we explore the evolving world of contact centers and Amazon Connect. The discussion covers why contact centers remain critical to both business and public sector operations, and how they're transforming from traditional cost centers into valuable sources of business intelligence. Key highlights include Amazon Connect's integration capabilities with AWS services, particularly through AWS Lambda functions, and the recent implementation of generative AI features including contact summarisation, agent evaluations, and Amazon Q in Connect. The conversation emphasizes how modern technology is helping organizations better understand customer needs, improve agent performance, and maintain human empathy in customer service while leveraging automation. The episode also touches on practical aspects of system integration and data management, demonstrating how Amazon Connect helps organizations overcome traditional barriers in contact center operations. https://aws.amazon.com/connect/ https://aws.amazon.com/blogs/contact-center/introducing-the-next-generation-of-amazon-connect/

An airhacks.fm conversation with Adam Dudczak (@maneo) about: early programming experiences with Commodore 64 and Pascal, demo scene participation through postal mail swapping of floppy disks, writing assembly code for 64K intros with music and graphics, developing digital library systems using Java Servlets and Hibernate, involvement in reactivating Poznan Java User Group in 2007, NetBeans Dream Team and NetBeans World Tour, appearing on Polish breakfast TV to discuss Java programming, working at Supercomputing Center on cultural heritage digitization projects, transitioning to EJB 3.0 and Glassfish based on conference inspirations, joining allegro in 2014 to rewrite search functionality from PHP to Java microservices, handling 14K requests per second with Solr-based search infrastructure, migrating big data stack from on-premise Hadoop to Google Cloud Platform, developing private banking application for children using Spring and Hibernate then migrating to Google Sheets with 70 lines of JavaScript, discussing public cloud cost optimization strategies, comparing AWS Lambda versus EC2 versus container services based on traffic patterns, emphasizing removal of code when moving to public cloud to leverage managed services, standardization benefits of Java EE for long-term maintenance and migration, quarkus as modern framework supporting old Jakarta EE code with fast startup times, importance of choosing appropriate persistence layer (S3 vs relational databases) based on cloud costs, serverless architectures for enterprise applications with predictable low traffic, differences between AWS Azure and GCP service offerings and pricing models, Turbo assembler project klatwa Adam Dudczak on twitter: @maneo

Episode Summary: AWS Morning Brief for the week of August 4th, 2025, with Corey Quinn. Amazon Aurora MySQL database clusters now support up to 256 TiB of storage volume Introducing v2 of Powertools for AWS Lambda (Java)Introducing Extended Support for Amazon ElastiCache version 4 and version 5 for Redis OSSAmazon DocumentDB Serverless is now available AWS Lambda response streaming now supports 200 MB response payloadsHow Zapier runs isolated tasks on AWS Lambda and upgrades functions at scaleAmazon Application Recovery Controller now supports Region switchAnnouncing general availability of Amazon EC2 G6f instances with fractional GPUsAmazon Promotes Malphas to Senior Vice President of Bad Decisions, Unveils 17th Leadership PrincipleAmazon CloudFront introduces new origin response timeout controlsOptimize traffic costs of Amazon MSK consumers on Amazon EKS with rack awarenessAmazon Bedrock now available in the US West (N. California) RegionNew AWS whitepaper: AWS User Guide to Financial Services Regulations and Guidelines in Australia Amazon EC2 Auto Scaling adds AWS Lambda functions as notification targets for lifecycle hooks

For memberships: join this channel as a member here:https://www.youtube.com/channel/UC_mGuY4g0mggeUGM6V1osdA/joinSummary:In this conversation, Kaivalya Apte and Rajesh Pandey talk about the engineering behind AWS Lambda, exploring its architecture, use cases, and best practices. They discuss the challenges of event handling, concurrency, and load balancing, as well as the importance of observability and testing in serverless environments. The conversation highlights the innovative solutions AWS Lambda provides for developers, emphasizing the balance between simplicity and complexity in cloud computing.Chapters:00:00 Introduction to AWS Lambda04:36 Use Cases and Best Practices for AWS Lambda09:34 Event Handling and Queue Management19:41 Idempotency and Event Duplication Challenges29:39 Cold Starts and Performance Optimization34:37 Statelessness and Resource Management in Lambda42:18 Understanding Micro-VMs and Cold Starts45:14 Resource Management and Recommendations for Developers47:04 Scaling and Back Pressure in Serverless Systems51:33 Cellular Architecture and Fairness in Resource Allocation55:23 Handling Problematic Events and Poison Pills01:01:03 Testing and Operational Readiness in Lambda01:14:11 Preparing for High Traffic EventsReferences:Handling Billions of invocations: https://aws.amazon.com/blogs/compute/handling-billions-of-invocations-best-practices-from-aws-lambda/Firecracker: https://firecracker-microvm.github.io/AWS Lambda: https://aws.amazon.com/lambda/Connect with Rajesh: https://x.com/RPandeyViewshttps://www.linkedin.com/in/rajeshpandeyiiit/Don't forget to like, share, and subscribe for more insights!=============================================================================Like building stuff? Try out CodeCrafters and build amazing real world systems like Redis, Kafka, Sqlite. Use the link below to signup and get 40% off on paid subscription.https://app.codecrafters.io/join?via=geeknarrator=============================================================================Database internals series: https://youtu.be/yV_Zp0Mi3xsPopular playlists:Realtime streaming systems: https://www.youtube.com/playlist?list=PLL7QpTxsA4se-mAKKoVOs3VcaP71X_LA-Software Engineering: https://www.youtube.com/playlist?list=PLL7QpTxsA4sf6By03bot5BhKoMgxDUU17Distributed systems and databases: https://www.youtube.com/playlist?list=PLL7QpTxsA4sfLDUnjBJXJGFhhz94jDd_dModern databases: https://www.youtube.com/playlist?list=PLL7QpTxsA4scSeZAsCUXijtnfW5ARlrsNStay Curios! Keep Learning!#aws #awslambda #serverless #distributedsystems #scalability #reliability

Cost is always top of mind when building in the cloud, and recently AWS has introduced some changes worth paying attention to. In this episode of AWS Bites, we explore a shift that caught many by surprise: the “free” INIT phase for Lambda's managed runtimes is going away. That cold start time that used to fly under the billing radar? It's now part of the cost. We dig into what this means for your workloads, who might feel the impact, and whether this gives languages like Rust and Go an extra edge. But it's not all bad news. AWS has also rolled out new pricing tiers for CloudWatch Logs, making it cheaper for high-volume accounts. On top of that, there are new options to send logs directly to S3 or Firehose, helping simplify pipelines and reduce costs. We close with a few tips to help you keep your Lambda and logging spend under control. If you're building on AWS and care about efficiency, this is one you won't want to miss.Big shoutout to fourTheorem for powering yet another episode of AWS Bites. At fourTheorem, we believe the cloud should be simple, scalable, and cost-effective, and we help teams do just that. Whether you're diving into containers, stepping into event-driven architecture, or scaling a global SaaS platform on AWS, or trying to keep cloud spend under control our team has your back. Visit https://fourTheorem.com to see how we can help you build faster, better, and with more confidence using AWS cloud!In this episode, we mentioned the following resources:AWS Blog – Tiered Pricing for AWS Lambda: https://aws.amazon.com/blogs/compute/introducing-tiered-pricing-for-aws-lambda/Luc van Donkersgoed – When is the Lambda INIT phase free and when is it billed?: https://lucvandonkersgoed.com/2022/04/09/when-is-the-lambda-init-phase-free-and-when-is-it-billed/AWS Bites – Explaining Lambda Runtimes (Episode 104): https://awsbites.com/104-explaining-lambda-runtimes/AWS Blog – Standardized Billing for Lambda INIT Phase: https://aws.amazon.com/blogs/compute/aws-lambda-standardizes-billing-for-init-phase/Lambda Cold Start Benchmarks by Maxim David: https://maxday.github.io/lambda-perf/Duckbill Group Blog – Lambda Logs Just Got Cheaper: https://www.duckbillgroup.com/blog/lambda-logs-just-got-cheaper/AWS Bites – Becoming a Logs Ninja with CloudWatch (Episode 35): https://awsbites.com/35-how-can-you-become-a-logs-ninja-with-cloudwatchDo you have any AWS questions you would like us to address?Leave a comment here or connect with us on X/Twitter, BlueSky or LinkedIn:- ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://twitter.com/eoins⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠https://bsky.app/profile/eoin.sh⁠⁠⁠⁠ | ⁠⁠⁠⁠https://www.linkedin.com/in/eoins/⁠⁠⁠⁠- ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://twitter.com/loige⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠https://bsky.app/profile/loige.co⁠⁠⁠⁠ | ⁠⁠⁠⁠https://www.linkedin.com/in/lucianomammino/

Ashley Peacock, the author of Serverless Apps on Cloudflare, speaks with host Jeremy Jung about content delivery networks (CDNs). Along the way, they examine dependency injection with bindings, local development, serverless, cold starts, the V8 runtime, AWS Lambda vs Cloudflare workers, WebAssembly limitations, and core services such as R2, D1, KV, and Pages. Ashley suggests why most users use an external database and discusses eventually consistent data stores, S3-to-R2 migration strategies, queues and workflows, inter-service communication, durable objects, and describes some example projects. Brought to you by IEEE Computer Society and IEEE Software magazine.

Today's guest is AJ Stuyvenberg, a Staff Engineer at Datadog working on their Serverless observability project. He had a great article recently about how they rewrote their AWS Lambda extension in Rust. It's a really interesting look at a big, hard project, from thinking about when it's a good idea to do a rewrite to talking about their focus on performance and reliability above all else and what he thinks about the Rust ecosystem. Beyond that, AJ is just a learning machine, so I got his thoughts on all kinds of software development topics, from underrated AWS services and our favorite databases to the AWS Free Tier and the annoyances of a new AWS account. Finally, AJ dishes out some career advice for curious, ambitious developers.

In this conversation, Krish Palaniappan discusses the intricacies of deploying an API gateway on AWS, focusing on the management of API usage, reporting, and the challenges faced with certificate management. He elaborates on the deployment strategies across different environments, the debugging process for certificate issues, and the importance of understanding endpoint types and SSL certificates. The conversation also highlights the resolution of certificate chain issues and the necessary code adjustments to ensure smooth operation. In this conversation, Krish Palaniappan discusses the intricacies of optimizing AWS Lambda layers, the transition from AWS SDK version 2 to version 3, and the importance of efficient deployment strategies. He emphasizes the need for local development and testing using Express to enhance productivity and streamline the onboarding process for customers, including API key management and usage plans. Snowpal Products Backends as Services on ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠AWS Marketplace⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Mobile Apps on ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠App Store⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ and ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Play Store⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Web App⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Education Platform⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ for Learners and Course Creators

Managing security for a device that can autonomously interact with third-party services presents unique orchestration challenges that go beyond traditional IoT security models. In this episode of Detection at Scale, Matthew Domko, Head of Security at Rabbit, gives Jack an in-depth look at building security programs for AI-powered hardware at scale.   He details how his team achieved 100% infrastructure-as-code coverage while maintaining the agility needed for rapid product iteration. Matt also challenges conventional approaches to scaling security operations, advocating for a serverless-first architecture that has fundamentally changed how they handle detection engineering. His insights on using private LLMs via Amazon Bedrock to analyze security events showcase a pragmatic approach to AI adoption, focusing on augmentation of existing workflows rather than wholesale replacement of human analysis.  Topics discussed: How transitioning from reactive SIEM operations to a data-first security approach using AWS Lambda and SQS enabled Rabbit's team to handle complex orchestration monitoring without maintaining persistent infrastructure.  The practical implementation of LLM-assisted detection engineering, using Amazon Bedrock to analyze 15-minute blocks of security telemetry across their stack.  A deep dive into security data lake architecture decisions, including how their team addressed the challenge of cost attribution when security telemetry becomes valuable to other engineering teams.  The evolution from traditional detection engineering to a "detection-as-code" pipeline that leverages infrastructure-as-code for security rules, enabling version control, peer review, and automated testing of detection logic while maintaining rapid deployment capabilities. Concrete examples of integrating security into the engineering workflow, including how they use LLMs to transform security tickets to match engineering team nomenclature and communication patterns. Technical details of their data ingestion architecture using AWS SQS and Lambda, showing how two well-documented core patterns enabled the team to rapidly onboard new data sources and detection capabilities without direct security team involvement. A pragmatic framework for evaluating where generative AI adds value in security operations, focusing on specific use cases like log analysis and detection engineering where the technology demonstrably improves existing workflows rather than attempting wholesale process automation.  Listen to more episodes:  Apple  Spotify  YouTube Website

In this episode, we explore DuckDB, an open-source analytical database known for its speed and simplicity. Discover how DuckDB stands out in various applications and compare it to other tools like SQLite, Athena, Pandas, and Polars. We also demonstrate integrating DuckDB with AWS Lambda and Step Functions for serverless analytics.AWS Bites is brought to you by fourTheorem. If you are looking for a partner to architect, develop and modernise on AWS, give fourTheorem a call. Check out ⁠fourtheorem.com⁠In this episode, we mentioned the following resources: Our `duck-query-lambda`, A Lambda runtime for DuckDB queries: https://github.com/fourTheorem/duck-query-lambda DuckDB's official website: https://duckdb.org/ LibSQL: https://github.com/tursodatabase/libsql Do you have any AWS questions you would like us to address?Leave a comment here or connect with us on X/Twitter, BlueSky or LinkedIn:- ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://twitter.com/eoins⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | https://bsky.app/profile/eoin.sh | https://www.linkedin.com/in/eoins/- ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://twitter.com/loige⁠⁠⁠⁠ | https://bsky.app/profile/loige.co | https://www.linkedin.com/in/lucianomammino/

In this episode, we discuss using AWS Lambda Powertools for Python to build serverless REST APIs with AWS Lambda. We cover the benefits of using Powertools for routing, validation, OpenAPI support, and more. Powertools provides an excellent framework for building APIs while maintaining Lambda best practices.In this episode, we mentioned the following resources: AWS Bites 41. How can Middy make writing Lambda functions easier? - ⁠https://awsbites.com/41-how-can-middy-make-writing-lambda-functions-easier⁠ AWS Bites 120. Lambda Best Practices - ⁠https://awsbites.com/120-lambda-best-practices/⁠ REST API - Powertools for AWS Lambda (Python) - ⁠https://docs.powertools.aws.dev/lambda/python/latest/core/event_handler/api_gateway/⁠ Hono - ⁠https://hono.dev/⁠ Fastify - ⁠https://fastify.dev/⁠ Axum - ⁠https://github.com/tokio-rs/axum⁠ FastAPI - ⁠https://fastapi.tiangolo.com/⁠Do you have any AWS questions you would like us to address?Leave a comment here or connect with us on BlueSky or LinkedIn: https://bsky.app/profile/eoin.sh | https://www.linkedin.com/in/eoins/ https://bsky.app/profile/loige.co | https://www.linkedin.com/in/lucianomammino/

AWS Community Builder and Software/Platform Engineer Ervin Szilágyi joins us today to talk about his project: creating a BlueSky Bot (for good, not evil

Join us on a serverless computing journey! Host Keith Townsend is with Amazon Web Services' Usman Khalid, Director, AWS Lambda on this episode of Six Five On The Road at AWS re:Invent. They look at a decade of evolution and what's in store for the future of serverless computing with AWS Lambda. Tune in for details

Sébastien Stormacq joins us to talk about AWS Lambda and Swift - what does "Serverless" mean, how deployment works, and how to get started.GuestSébastien ☁ Stormacq

This week, we discuss the relationship between DevOps and Platform Engineering, Gartner's take on Distributed Hybrid Infrastructure, and Nvidia's search for new use cases. Plus, a listener chimes in to clear up some Podman misconceptions. Watch the YouTube Live Recording of Episode (https://www.youtube.com/watch?v=DyjB-jmL0QQ) 495 (https://www.youtube.com/watch?v=DyjB-jmL0QQ) Runner-up Titles Prove me wrong AWS, prove me wrong Please turn off the lights Who's googling for “shift left”? I realized what they were talking about, it's computers They're talking but you're not listening Piling on the dead horse We gave this guy $5 billion dollars, check him out Podman is Pepsi Nobody's paying for that Niche Player Rundown Platform Engineering Is The New DevOps (https://www.forbes.com/sites/justinwarren/2024/11/21/platform-engineering-is-the-new-devops/) SRE Books (https://sre.google/books/) Magic Quadrant for Distributed Hybrid Infrastructure (https://www.gartner.com/doc/reprints?id=1-2J0PN9ZJ&ct=241007&st=sb&trk=0da8abef-e59d-40d4-b66b-ba96c755768b&sc_channel=el) AWS named as a leader again in the Gartner Magic Quadrant for Distributed Hybrid Infrastructure (https://aws.amazon.com/blogs/aws/aws-named-as-a-leader-again-in-the-gartner-magic-quadrant-for-distributed-hybrid-infrastructure/) Nvidia AI Easts the World — Benedict Evans (https://www.ben-evans.com/presentations) Nvidia revenue almost doubles on the year even as growth slows from previous quarter (https://on.ft.com/3Vpw2Z1) Nvidia's Huang Spreads the Gospel of AI in Search of More Customers (https://www.bloomberg.com/news/newsletters/2024-11-21/nvidia-s-huang-spreads-the-gospel-of-ai-in-search-of-more-customers?srnd=undefined) Amazon Updates Homegrown Chips, Even as It Grows Nvidia Ties (https://links.message.bloomberg.com/a/click?_t=f574328d4d0c4c359b90d8e49b10e21d&_m=e253c47d1776426cada2b989eb51ef3d&_e=BISdgjckKJ39RYZ5axUkOu4DkhEzj_0CzmZEdaLS3niAwih7Lch-yccqByy-SKSB_PawXlFTeOpypVo4aikKnrEHKgvZ1v2TyAeErFN65ZsdRhzpsl63CY7Ia4-4Y_AmaM8n0A6iEaAPInfkiRKNT3xf8OE6NLeC4L7EavGfLanwRXXmv773517sL7d2HT-Rcewoj4Ilv2S4WBW0l3E797KSeKHwZmNv3h9g8B7rUMFKXg8gnlDDRuYjGkBMn8m9-4yP3laYhYAwEeaW3arWkc1bzZFYO_N0fzB31aRoEEvMjvCyXvrv-fg1yhLbDHFZFK5xDr2cgqT8uxPoHajG8qPT7nzRt_56WNcg30HnKZ2OwDxnLJkIDzw47BuHXtk-BMsx5WG7Gn51NdUiPqUTAV5YHattNV9B5gmGwXtVZubp-eOJfFuCVKrLgVwrMLLqGMLEFhgI00D0RHwpXFbHDg%3D%3D) Nvidia Earnings, Strawberry and Video, The Networking Question (https://stratechery.com/2024/nvidia-earnings-strawberry-and-video-the-networking-question/) Podman: Podman in Action | Red Hat Developer (https://developers.redhat.com/e-books/podman-action) Kubernetes Podcast from Google: Episode 164 - Podman, with Daniel Walsh and Brent Baude (https://kubernetespodcast.com/episode/164-podman/) DevOps and Docker Talk: Cloud Native Interviews and Tooling | Podman In Action: Desktop, Machine, and more (https://podcast.bretfisher.com/episodes/podman-in-action-desktop-machine-and-more) Relevant to your Interests Microsoft Ignite 2024: Everything Revealed in 15 Minutes (https://www.youtube.com/watch?v=_4qsQ6OWZsM) Microsoft Ignite 2024: all the news from Microsoft's IT pro event (https://www.theverge.com/2024/11/19/24300001/microsoft-ignite-2024-news-ai-announcements-copilot-windows-azure-office) AWS Lambda turns ten – looking back and looking ahead | Amazon Web Services (https://aws.amazon.com/blogs/aws/aws-lambda-turns-ten-the-first-decade-of-serverless-innovation/) Kyndryl insiders claim new business is scarce (https://www.theregister.com/2024/11/20/kyndryl_little_new_business/) Snowflake snaps up data management company Datavolo (https://techcrunch.com/2024/11/20/snowflake-snaps-up-data-management-company-datavolo/) Northflank raises $22M to make Kubernetes work for your developers (https://northflank.com/blog/northflank-raises-22m-to-make-kubernetes-work-for-your-developers-ship-workloads-not-infrastructure) Overcast adds new listening stats and 48-hour undo features (https://9to5mac.com/2024/11/20/overcast-listening-history-undo-features/) Reddit was down — latest updates on major outage (https://www.tomsguide.com/news/live/reddit-down-live-updates-on-outage) Wiz acquires Dazz for $450M to expand its cybersecurity platform (https://techcrunch.com/2024/11/21/wiz-acquires-dazz-for-450m-to-expand-its-cybersecurity-platform/) Comcast is spinning off its cable TV business (https://www.theverge.com/2024/11/20/24301310/comcast-spinning-off-nbcuniversal-cable-tv-business) Snowflake's shares surge higher on blowout earnings, (https://t.co/alQ7p57V3y) Clouded Judgement 11.22.24 - Is Software Back? (https://cloudedjudgement.substack.com/p/clouded-judgement-112224-is-software?utm_source=post-email-title&publication_id=56878&post_id=151992794&utm_campaign=email-post-title&isFreemail=true&r=2l9&triedRedirect=true&utm_medium=email) WordPress.com owner Automattic snaps up grammar checker Harper (https://techcrunch.com/2024/11/21/wordpress-com-owner-automattic-snaps-up-grammar-checker-harper/) DHH Wants To Make Web Dev Easy Again, With Ruby on Rails (https://thenewstack.io/dhh-wants-to-make-web-dev-easy-again-with-ruby-on-rails/) Dear friend, you have built a Kubernetes (https://www.macchaffee.com/blog/2024/you-have-built-a-kubernetes/) Kamal 2.0 Released (https://dev.37signals.com/kamal-2/) 'I have no money': Thousands of Americans see their savings vanish in Synapse fintech crisis (https://www.cnbc.com/2024/11/22/synapse-bankruptcy-thousands-of-americans-see-their-savings-vanish.html) Pentagon audit highlights woeful ERP systems (https://www.thestack.technology/pentagon-audit-it-systems-erp/) Delivering 4K Video with Cloudflare R2 for $2.18 (https://screencasting.com/cheap-video-hosting) First Google Axion Processor Now Available: Claims Best Performance in Cloud Market (https://www.infoq.com/news/2024/11/google-axion-c4a/) Glassdoor Worklife Trends 2025 - Glassdoor US (https://www.glassdoor.com/blog/worklife-trends-2025/) Nonsense It looks like Backstage is working out. 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DevOpsDayLA (https://www.socallinuxexpo.org/scale/22x/events/devopsday-la) at SCALE22x (https://www.socallinuxexpo.org/scale/22x), March 6-9, 2025, discount code DEVOP SDT News & Community Join our Slack community (https://softwaredefinedtalk.slack.com/join/shared_invite/zt-1hn55iv5d-UTfN7mVX1D9D5ExRt3ZJYQ#/shared-invite/email) Email the show: questions@softwaredefinedtalk.com (mailto:questions@softwaredefinedtalk.com) Free stickers: Email your address to stickers@softwaredefinedtalk.com (mailto:stickers@softwaredefinedtalk.com) Follow us on social media: Twitter (https://twitter.com/softwaredeftalk), Threads (https://www.threads.net/@softwaredefinedtalk), Mastodon (https://hachyderm.io/@softwaredefinedtalk), LinkedIn (https://www.linkedin.com/company/software-defined-talk/), BlueSky (https://bsky.app/profile/softwaredefinedtalk.com) Watch us on: Twitch (https://www.twitch.tv/sdtpodcast), YouTube (https://www.youtube.com/channel/UCi3OJPV6h9tp-hbsGBLGsDQ/featured), Instagram (https://www.instagram.com/softwaredefinedtalk/), TikTok (https://www.tiktok.com/@softwaredefinedtalk) Book offer: Use code SDT for $20 off "Digital WTF" by Coté (https://leanpub.com/digitalwtf/c/sdt) Sponsor the show (https://www.softwaredefinedtalk.com/ads): ads@softwaredefinedtalk.com (mailto:ads@softwaredefinedtalk.com) Recommendations Brandon: Cursor (https://www.cursor.com/) Matt: iPhone Mirroring in macOS (https://www.google.com/search?client=safari&rls=en&q=iPhone+Mirroring+in+macOS+15&ie=UTF-8&oe=UTF-8) (https://www.google.com/search?client=safari&rls=en&q=iPhone+Mirroring+in+macOS+15&ie=UTF-8&oe=UTF-8)15 (https://www.google.com/search?client=safari&rls=en&q=iPhone+Mirroring+in+macOS+15&ie=UTF-8&oe=UTF-8) Coté: Insta360 Flow Pro gimbal (https://amzn.to/4g7t9UA) Photo Credits Header (https://unsplash.com/photos/clear-light-bulb-lot-PIrOQrqewLE) Artwork (https://unsplash.com/s/photos/grade-evaluation) Web 2.0 2FA Life Hacks (https://www.troyhunt.com/beyond-passwords-2fa-u2f-and-google-advanced-protection/)

Bret and Nirmal Mehta are joined by Ken Collins to dig into using AI for more than coding, and if we can build an AI assistant that knows us.They touch on a lot of tools and platforms. "We're bit all over the place on this one, from talking about AI features in our favorite note taking apps like Notion, to my journey of making an open AI assistant with all of my Q&A from my courses, thousands of questions and answers, to coding agents and more." Ken is a local friend in Virginia Beach and was on the show last year talking about AWS Lambda, and we've both been trying to find value in all of these AI tools for our day to day work.Be sure to check out the live recording of the complete show from October 24, 2024 on YouTube (Stream 279).★Topics★The Lifestyle Copilot Blog PostServerless AI Inference with Gemma 2 Blog Post Creators & Guests Cristi Cotovan - Editor Beth Fisher - Producer Bret Fisher - Host Ken Collins - Guest Nirmal Mehta - Host (00:00) - Intro (01:26) - AI in Recruitment at Torc (03:25) - AI for Day to Day Workflows (04:44) - Notion AI and RAG (07:20) - Creating Your Own AI Search Solution (13:59) - Choosing the Right LLM for the Job (20:55) - Personal AI and Long Context Windows (25:10) - Future of Personal Fine-Tuned Models (25:52) - AI Assistants in Meetings (27:34) - Temperature and AI Hallucinations (32:07) - Agents and Tool Integration (39:31) - Apple Intelligence and Personal AI (44:56) - AI Apps on Mobile (50:00) - LoRA You can also support my free material by subscribing to my YouTube channel and my weekly newsletter at bret.news!Grab the best coupons for my Docker and Kubernetes courses.Join my cloud native DevOps community on Discord.Grab some merch at Bret's Loot BoxHomepage bretfisher.com

An airhacks.fm conversation with Vadym Kazulkin (@VKazulkin) about: journey as a Java developer from the late 1990s to present, early experiences with Java and J2EE development, transition to cloud and serverless technologies, particularly AWS Lambda, discussion of Java performance on lambda compared to node.js, detailed explanation of AWS SnapStart technology for improving Java cold starts, pros and cons of "fat" Lambda functions versus microservices, challenges of using GraalVM with Lambda, importance of optimizing Lambda package size and dependencies, comparison of quarkus and Spring Boot on Lambda, benefits of serverless architecture for business logic focus, involvement with Java User Group Bonn and AWS Community Builder program, brief mention of asynchronous patterns in serverless architectures, importance of staying technically hands-on as a manager in the rapidly evolving cloud world Vadym Kazulkin on twitter: @VKazulkin

AWS Morning Brief for the week of November 4, with Corey Quinn. Links:Amazon CloudWatch now monitors EBS volumes exceeding provisioned performanceAmazon Q Developer announces support for inline chat to streamline the developer experienceAmazon Route 53 announces HTTPS, SSHFP, SVCB, and TLSA DNS resource record supportAmazon Virtual Private Cloud launches new security group sharing featuresAWS now accepts partial card paymentsAnnouncing AWS Amplify integration with Amazon S3 for static website hostingAWS CodeBuild now supports retrying builds automaticallyAWS Trust & Safety Center is now available on AWS re:Post2024 re:Invent Know Before You Go – Cloud Financial Management SessionsIntroducing an enhanced local IDE experience for AWS Lambda developers

AWS Morning Brief for the week of October 28, with Corey Quinn. Links:Amazon Aurora launches Global Database writer endpointAmazon Connect now offers screen sharingAmazon EKS endpoints now support connectivity over Internet Protocol version 6 (IPv6)AWS IAM Identity Center simplifies calls to AWS services with single identity contextEC2 Image Builder now supports building and testing macOS imagesIntroducing an enhanced in-console editing experience for AWS Lambda

In this episode of the vBrownBag, Damian does a deep dive on getting started with PowerShell on AWS Lambda. He covers setting up a development environment, packaging & publishing PowerShell on Lambda, lessons learned, and more! 00:28 A quick overview of PowerShell & AWS Lambda

We'll explore 3 use cases for monitoring data. They are:* Analyzing long-term trends* Comparing over time or experiment groups* Conducting ad hoc retrospective analysis Analyzing long-term trends You can ask yourself a couple of simple questions as a starting point:* How big is my database?* How fast is the database growing? * How quickly is my user count growing?As you get comfortable with analyzing data for the simpler questions, you can start to analyze trends for less straightforward questions like:* How is the database performance evolving? Are there signs of degradation?* Is there consistent growth in data volume that may require future infrastructure adjustments?* How is overall resource utilization trending over time across different services?* How is the cost of cloud resources evolving, and what does that mean for budget forecasting?* Are there recurring patterns in downtime or service degradation, and what can be done to mitigate them?Sebastian mentioned that it's a part of observability he enjoys doing. I can understand why. It's exciting to see how components are changing over a period and working out solutions before you end up in an incident response nightmare.Getting to effectively analyze the trends requires the right level of data retention settings. Because if you're throwing out your logs, traces, and metrics too early, you will not have enough historical data to do this kind of work.Doing this right means having the right amount of data in place to be able to analyze those trends over time, and that will of course depend on your desired period. Comparing over time or experiment groupsGoogle's definitionYou're comparing the data results for different groups that you want to compare and contrast. Using a few examples from the SRE (2016) book:* Are your queries faster in this version of this database or this version of that database? * How much better is my memcache hit rate with an extra node and is my site slower than it was last week? You're comparing it to different buckets of time and different types of products.A proper use case for comparing groupsSebastian did this particular use case recently because he had to compare two different technologies for deploying code: AWS Lambda vs AWS Fargate ECS. He took those two services and played around with different memories and different virtual CPUs. Then he ran different amounts of requests against those settings and tried to figure out which one was the better technology option most cost-effectively.His need for this went beyond engineering work but enabling product teams with the right decision-making data. He wrote out a knowledge base article to give them guidance for a more educated decision on the right AWS service.Having the data to compare the two services allowed him to answer questions like:* When should you be using either of these technologies? * What use cases would either technology be more suitable for?This data-based decision support is based mainly on monitoring or observability data. The idea of using the monitoring data to compare tools and technologies for guiding product teams is something I think reliability folk can gain a lot of value from doing. Conducting ad hoc retrospective analysis (debugging)Debugging is a bread-and-butter responsibility for anyone who is a software engineer of any level. It's something that everybody should know a little bit more about than other tasks because there are very effective and also very ineffective ways of going about debugging. Monitoring data can help make the debugging process fall into the effective side.There are organizations where you have 10 different systems. In one system, you might get one fragmented piece of information. In another, you'll get another fragment. And so on for all the different systems. And then you have to correlate these pieces of information in your head and hopefully, you get some clarity out of the fragments to form some kind of insight. Monitoring data that are brought together into one datastream can help correlate and combine all these pieces of information. With it, you can:* Pinpoint slow-running queries or functions by analyzing execution times and resource usage, helping you identify inefficiencies in your code* Correlate application logs with infrastructure metrics to determine if a performance issue is due to code errors or underlying infrastructure problems* Track memory leaks or CPU spikes by monitoring resource usage trends, which can help you identify faulty code or services* Set up detailed error tracking that automatically flags code exceptions and matches them with infrastructure events, to get to the root cause faster* Monitor system load alongside application performance to see if scaling issues are related to traffic spikes or inefficient code pathsBeing able to do all this makes the insight part easier for you. And so your debugging approach becomes very different. It becomes much more effective. It becomes much less time-consuming. It potentially makes the debugging task fun.Because you get to the root cause of the thing that is not working much faster. Your monitoring/observability data setup can make it nice and fun to a certain degree, or it can make it downright miserable. If it's done well, it's just one of those things you don't even have to think about. It's just part of your job. You do it. It's very effective and you move on. Wrapping upSo we've covered three more use cases for monitoring data, other than the usual alerts and dashboards.They are once again:* analyzing long-term trends* comparing over time or experiment groups and* conducting ad hoc retrospective analysis, aka debuggingNext time your boss asks you what all these systems do, you now have three more reasons that you need to focus on your monitoring and be able to use it more effectively. Until next time, happy monitoring. This is a public episode. 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Thank you to Hookdeck for sponsoring this episode. If you're looking to level-up your event-driven architecture, then check out their serverless event gateway at hookdeck.com/theburningmonk and help support this channel.AWS Serverless Hero Luciano Mammino shares the history of Middy, the popular middleware engine for AWS Lambda functions; why he's sold on writing Lambda functions in Rust and why you should too!Links from the episode:AWS Bites channelMiddyHow to sponsor MiddyCrafting Lambda Functions in RustEasy mode RustUsing Node.js ES modules and top-level await in AWS LambdaUsing Middy with TypescriptEp97 on LLRT (the superfast JavaScript runtime for Lambda)Opening theme song:Cheery Monday by Kevin MacLeodLink: https://incompetech.filmmusic.io/song/3495-cheery-mondayLicense: http://creativecommons.org/licenses/by/4.0

What are you doing differently today that you're stopping tomorrow's legacy? In this episode Ashish spoke to Adrian Asher, CISO and Cloud Architect at Checkout.com, to explore the journey from monolithic architecture to cloud-native solutions in a regulated fintech environment. Adrian shared his perspective on why there "aren't enough lambdas" and how embracing cloud-native technologies like AWS Lambda and Fargate can enhance security, scalability, and efficiency. Guest Socials:⁠ ⁠⁠Adrian's Linkedin ⁠ Podcast Twitter - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@CloudSecPod⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ If you want to watch videos of this LIVE STREAMED episode and past episodes - Check out our other Cloud Security Social Channels: - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Cloud Security Podcast- Youtube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Cloud Security Newsletter ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Cloud Security BootCamp Questions asked: (00:00) Introduction (01:59) A bit about Adrian (02:47) Cloud Naive vs Cloud Native (03:54) Checkout's Cloud Native Journey (05:44) What is AWS Fargate? (06:52) There are not enough Lambdas (09:52) The evolution of the Security Function (12:15) Culture change for being more cloud native (15:23) Getting security teams ready for Gen AI (18:16) Where to start with Cloud Native? (19:14) Where you can connect with Adrian? (19:39) The Fun Section

How to secure AWS cloud using AWS Lambda? We spoke to Lily Chau from Roku at BSidesSF about her experience and innovative approach to tackling security issues in AWS environments. From deploying IAM roles to creating impactful playbooks with AWS Lambda, Lily shared her take on automating remediation processes. We spoke about the challenges of managing cloud security with tools like CSPM and CNAPP, and how Lily and her team took a different approach that goes beyond traditional methods to achieve real-time remediation. Guest Socials:⁠ ⁠⁠Lily Twitter Podcast Twitter - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@CloudSecPod⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ If you want to watch videos of this LIVE STREAMED episode and past episodes - Check out our other Cloud Security Social Channels: - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Cloud Security Podcast- Youtube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Cloud Security Newsletter ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Cloud Security BootCamp Questions asked: (00:00) Introduction (01:56) A bit about Lily (02:27) What is Auto Remediation? (03:56) Example of Auto Remediation (05:19) CSPMs and Auto Remediation (06:58) Make Auto Remediation in Cloud work for you (09:49) Where to get started with Auto Remediation? (11:52) What defines a High Impact Playbook? (12:58) Auto Remediation for Lateral Movement (14:35) What is running in the background? (16:41) What skillset is required? (19:08) The Fun Section Resources for the episode: Lily's talk at BsidesSF

Scott and Wes chat with Richard Davison from AWS about LLRT, a new runtime tailored specifically for Lambda. They dive into the benefits of using LLRT, challenges with JavaScript in serverless, and why Rust was chosen for its development. Show Notes 00:00 Welcome to Syntax! 01:07 Who is Richard Davison? 05:11 What is LLRT and what's the motivation for building it? 08:25 AWS Lambda example. 11:20 What makes LLRT specifically tailored to Lambda? 14:55 Brought to you by Sentry.io. 15:22 Node.js in Lambda. 16:00 What are some challenges that people have with JavaScript in serverless? 17:20 Lambda memory configuration. 19:23 Managing cost of compute. 21:29 Simpler and faster than Node, Bun, Dino, but not a replacement. 22:31 The benchmarks. 27:00 Quick.js, the main reason for the performance gains. Fabrice Bellard QuickJS. 28:03 The Quick.js engine. 30:35 What was the reason behind creating Quick.js? 33:46 What made you pick Rust for LLRT? 36:34 Abstractions and the value of speed. 39:08 The JIT Compiler. 42:38 Compile cache. 43:27 De-optimizations. 44:59 Node.js Compat, what to use and avoid with LLRT. GitHub AWS Labs Compatibility Chart. 47:52 Will you target with WinterCG spec? 50:22 Streams API. 52:06 What about WebSockets? 53:10 Is this going to be promoted from a labs project? 54:49 Sick Picks + Shameless Plugs. Sick Picks Richard: QuickJS Engine, JSLinux. Shameless Plugs Richard: Javascript Hit us up on Socials! Syntax: X Instagram Tiktok LinkedIn Threads Wes: X Instagram Tiktok LinkedIn Threads Scott: X Instagram Tiktok LinkedIn Threads Randy: X Instagram YouTube Threads

Poor air quality is a major global issue causing around 7 million premature deaths per year, disproportionately affecting low and middle income countries. OpenAQ is an organization dedicated to identifying solutions to this large issue. OpenAQ uses Amazon Web Services (AWS) to collect and harmonize open air quality data from government sources and low-cost sensors around the world, ingesting around 10-12 million measurements daily. To learn more, the Fix This team sat down with Russ Biggs, director of technology at OpenAQ. Russ shared how AWS Lambda helps OpenAQ scale its data collection and harmonization pipeline in a serverless manner. And helps scientists, environmental justice groups, and community organizations access OpenAQ's data to study air pollution impacts, advocate for regulations, and raise awareness.

What is the state of serverless computing and Python in 2024? What are some of the new tools and best practices? We are lucky to have Tony Sherman who has a lot of practical experience with serverless programming on the show. Episode sponsors Sentry Error Monitoring, Code TALKPYTHON Mailtrap Talk Python Courses Links from the show Tony Sherman on Twitter: twitter.com Tony Sherman: linkedin.com PyCon serverless talk: youtube.com AWS re:Invent talk: youtube.com Powertools for AWS Lambda: docs.powertools.aws.dev Pantsbuild: The ergonomic build system: pantsbuild.org aws-lambda-power-tuning: github.com import-profiler: github.com AWS Fargate: aws.amazon.com Run functions on demand. Scale automatically.: digitalocean.com Vercel: vercel.com Deft: deft.com 37 Signals We stand to save $7m over five years from our cloud exit: world.hey.com The Global Content Delivery Platform That Truly Hops: bunny.net Watch this episode on YouTube: youtube.com --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy