Podcasts about data warehouses

This is episode 293 recorded on May 6th, 2025 where John & Jason talk the Microsoft Fabric March 2025 Feature Summary including Data Science, Data Warehouse, Real-time Intelligence, and Data Factory.

Thu, 27 Mar 2025 23:00:00 +0000 https://mydata.podigee.io/263-new-episode 62e289047630ff510f640b895cd2984d Wie revolutioniert man Private Equity mit Daten? Wie trifft man bessere Investmententscheidungen? Und warum ist Datenkultur gerade in dieser Branche so wichtig? Darum geht es in der neuen Folge von MY DATA IS BETTER THAN YOURS, in der Host Jonas Rashedi mit Daniel Lebe spricht. Dieser verantwortet als Business Intelligence Developer bei FSN Capital die Themen Datenanalyse, Prozessoptimierung und Business Intelligence. Im Gespräch der beiden Data-Enthusiasten geht es zunächst um den digitalen Wandel bei FSN Capital. Das Unternehmen hat in den letzten drei Jahren eine komplette Datentransformation durchlaufen. Das Ziel: Bessere Deals durch bessere Daten! Daniel ist Teil eines sechsköpfigen Teams, welches sich um die digitale Transformation der FSN Deal Prozesse und um die akquirierten Portfolio Unternehmen kümmert. Diese Teams setzen sich aus ausgebildeten Data Scientists, einem Data Engineer und weiteren Spezialisten zusammen. Im Private-Equity Mid Market Bereich ein schlagkräftiges Team. Der Aufbau einer modernen Dateninfrastruktur war dabei die größte Herausforderung. Für Daniel liegt der Fokus darauf, die verschiedenen Stakeholder mit ihren unterschiedlichen Bedürfnissen zusammenzubringen und datenbasierte Entscheidungsgrundlagen zu schaffen. Ein besonderer Schwerpunkt liegt auf der Analyse erfolgreicher Deals. Daniel erzählt von seiner aktuellen Aufgabe, vergangene Investments zu analysieren, um daraus für zukünftige Entscheidungen zu lernen. Dabei ist ihm wichtig, möglichst einfach anzufangen und schrittweise die richtigen Fragen zu stellen. Bei dem Aufbau der Dateninfrastruktur setzt FSN Capital auf moderne Tools. Zum Beispiel wird Power BI für Visualisierungen genutzt und BigQuery als Data Warehouse. Zum Schluss spricht Daniel noch über seine persönliche Datenreise: Wie er selbst Ziele messbar macht und warum der Film "Edge of Tomorrow" sein Data Game am besten beschreibt - manchmal braucht es mehrere Iterationen und auch Rückschläge, um am Ende zum Erfolg zu kommen. MY DATA IS BETTER THAN YOURS ist ein Projekt von BETTER THAN YOURS, der Marke für richtig gute Podcasts. Zum LinkedIn-Profil von Daniel: https://de.linkedin.com/in/daniel-lebe-a75011155 Zur Webseite von FSN Capital: https://www.fsncapital.com/en/ Zu allen wichtigen Links rund um Jonas und den Podcast: https://linktr.ee/jonas.rashedi Zeitstempel: 00:00:00 Intro und Begrüßung 00:02:05 FSN Capital und Private Equity 00:05:10 Das Digital Team 00:06:57 Datenbasierte Investments 00:11:53 Herausforderungen der Datenanalyse 00:14:06 Moderne Dateninfrastruktur 00:22:47 Spannende Use Cases 00:26:53 Effektivität und Effizienz 00:28:55 Diversität im Team 00:33:51 Persönliche Datenziele 00:35:52 Daniels Data Game full no Datapodcast,Digitale Transformation,Datenanalyse,Investment,Power BI,Use Cases,Stakeholder Management,Analyse Podcast,Datenstrategie,Datenvisualisierung

This is episode 287 recorded on March 13th, 2025 where John & Jason talk the Microsoft Fabric January 2025 Feature Summary including Python Notebooks in preview, Lineage enhancements to spark notebooks, lots of DBA enhancements to Data Warehouse, Tenant Level Private Link for Databases, and CI/CD preview for most of Fabric.

For episode 497, Head of Marketing Catherine Daly joins Brandon Zemp to talk about Space and Time, an AI driver web3 data warehouse. SxT replaces blockchain indexing, databases, and API servers with a decentralized solution.Catherine is a senior marketing strategist with a passion for building community around emerging technology. Prior to Space and Time, Catherine managed full-funnel marketing for both startups and established global organizations in the semiconductor industry. She is accomplished in developing data-driven integrated communications strategies to accelerate growth for businesses across Web3 the technology ecosystem.

Companies have crucial data stored across multiple systems in their organization. And the bigger the company, the more systems there are. Sales, marketing, finance, ERP, inventory, contract management, billing, and service delivery are just types of data and systems that show the story of the business. Many times your CRM just by itself can't show all of that!Because of this, many companies have started setting up a centralized data warehouse like Snowflake or Redshift to pull in data from their CRM and other systems to be able to run more advanced and centralized reporting across it all. But if you want to set this up, where do you start? How do you manage it? How do you protect it? How do you keep it maintained? How do you actually derive value from all the hard work of implementing it?I contacted Ryan Severns to talk through all these questions with him. We talk through all these questions and more, starting with questions like:Why set up a CRM data warehouse infrastructure in the first place?How do you build the integration?What important considerations do you need to plan for?We dig deeper into the details from there, talking through topics like ETL tooling, DBT, data governance, cross-platform analytics, building effective business intelligence systems, and more.If you're ready to level up your customer data skills and advance to the next level of your RevOps hero journey, this episode is for you! Give it a watch and a like. And hit that subscribe button so that you'll always get notified of future episodes of The RevOps Hero Podcast as well.

Doc and Matt welcome Jon Anderson of MLB Data Warehouse to the show to talk the value of dynasty leagues and what Jon looks for in his model to help dominate redraft.Follow Jon @JonPgh on X

Arnaud et Emmanuel discutent des nouvelles de ce mois. On y parle intégrité de JVM, fetch size de JDBC, MCP, de prompt engineering, de DeepSeek bien sûr mais aussi de Maven 4 et des proxy de répository Maven. Et d'autres choses encore, bonne lecture. Enregistré le 7 février 2025 Téléchargement de l'épisode LesCastCodeurs-Episode-322.mp3 ou en vidéo sur YouTube. News Langages Les evolutions de la JVM pour augmenter l'intégrité https://inside.java/2025/01/03/evolving-default-integrity/ un article sur les raisons pour lesquelles les editeurs de frameworks et les utilisateurs s'arrachent les cheveux et vont continuer garantir l'integrite du code et des données en enlevant des APIs existantes historiquemnt agents dynamiques, setAccessible, Unsafe, JNI Article expliques les risques percus par les mainteneurs de la JVM Franchement c'est un peu leg sur les causes l'article, auto propagande JavaScript Temporal, enfin une API propre et moderne pour gérer les dates en JS https://developer.mozilla.org/en-US/blog/javascript-temporal-is-coming/ JavaScript Temporal est un nouvel objet conçu pour remplacer l'objet Date, qui présente des défauts. Il résout des problèmes tels que le manque de prise en charge des fuseaux horaires et la mutabilité. Temporal introduit des concepts tels que les instants, les heures civiles et les durées. Il fournit des classes pour gérer diverses représentations de date/heure, y compris celles qui tiennent compte du fuseau horaire et celles qui n'en tiennent pas compte. Temporal simplifie l'utilisation de différents calendriers (par exemple, chinois, hébreu). Il comprend des méthodes pour les comparaisons, les conversions et le formatage des dates et des heures. La prise en charge par les navigateurs est expérimentale, Firefox Nightly ayant l'implémentation la plus aboutie. Un polyfill est disponible pour essayer Temporal dans n'importe quel navigateur. Librairies Un article sur les fetch size du JDBC et les impacts sur vos applications https://in.relation.to/2025/01/24/jdbc-fetch-size/ qui connait la valeur fetch size par default de son driver? en fonction de vos use cases, ca peut etre devastateur exemple d'une appli qui retourne 12 lignes et un fetch size de oracle a 10, 2 a/r pour rien et si c'est 50 lignres retournées la base de donnée est le facteur limitant, pas Java donc monter sont fetch size est avantageux, on utilise la memoire de Java pour eviter la latence Quarkus annouce les MCP servers project pour collecter les servier MCP en Java https://quarkus.io/blog/introducing-mcp-servers/ MCP d'Anthropic introspecteur de bases JDBC lecteur de filke system Dessine en Java FX demarrables facilement avec jbang et testes avec claude desktop, goose et mcp-cli permet d'utliser le pouvoir des librarires Java de votre IA d'ailleurs Spring a la version 0.6 de leur support MCP https://spring.io/blog/2025/01/23/spring-ai-mcp-0 Infrastructure Apache Flink sur Kibernetes https://www.decodable.co/blog/get-running-with-apache-flink-on-kubernetes-2 un article tres complet ejn deux parties sur l'installation de Flink sur Kubernetes installation, setup mais aussi le checkpointing, la HA, l'observablité Data et Intelligence Artificielle 10 techniques de prompt engineering https://medium.com/google-cloud/10-prompt-engineering-techniques-every-beginner-should-know-bf6c195916c7 Si vous voulez aller plus loin, l'article référence un très bon livre blanc sur le prompt engineering https://www.kaggle.com/whitepaper-prompt-engineering Les techniques évoquées : Zero-Shot Prompting: On demande directement à l'IA de répondre à une question sans lui fournir d'exemple préalable. C'est comme si on posait une question à une personne sans lui donner de contexte. Few-Shot Prompting: On donne à l'IA un ou plusieurs exemples de la tâche qu'on souhaite qu'elle accomplisse. C'est comme montrer à quelqu'un comment faire quelque chose avant de lui demander de le faire. System Prompting: On définit le contexte général et le but de la tâche pour l'IA. C'est comme donner à l'IA des instructions générales sur ce qu'elle doit faire. Role Prompting: On attribue un rôle spécifique à l'IA (enseignant, journaliste, etc.). C'est comme demander à quelqu'un de jouer un rôle spécifique. Contextual Prompting: On fournit des informations supplémentaires ou un contexte pour la tâche. C'est comme donner à quelqu'un toutes les informations nécessaires pour répondre à une question. Step-Back Prompting: On pose d'abord une question générale, puis on utilise la réponse pour poser une question plus spécifique. C'est comme poser une question ouverte avant de poser une question plus fermée. Chain-of-Thought Prompting: On demande à l'IA de montrer étape par étape comment elle arrive à sa conclusion. C'est comme demander à quelqu'un d'expliquer son raisonnement. Self-Consistency Prompting: On pose plusieurs fois la même question à l'IA et on compare les réponses pour trouver la plus cohérente. C'est comme vérifier une réponse en la posant sous différentes formes. Tree-of-Thoughts Prompting: On permet à l'IA d'explorer plusieurs chemins de raisonnement en même temps. C'est comme considérer toutes les options possibles avant de prendre une décision. ReAct Prompting: On permet à l'IA d'interagir avec des outils externes pour résoudre des problèmes complexes. C'est comme donner à quelqu'un les outils nécessaires pour résoudre un problème. Les patterns GenAI the thoughtworks https://martinfowler.com/articles/gen-ai-patterns/ tres introductif et pre RAG le direct prompt qui est un appel direct au LLM: limitations de connaissance et de controle de l'experience eval: evaluer la sortie d'un LLM avec plusieurs techniques mais fondamentalement une fonction qui prend la demande, la reponse et donc un score numerique evaluation via un LLM (le meme ou un autre), ou evaluation humaine tourner les evaluations a partir de la chaine de build amis aussi en live vu que les LLMs puvent evoluer. Decrit les embedding notament d'image amis aussi de texte avec la notion de contexte DeepSeek et la fin de la domination de NVidia https://youtubetranscriptoptimizer.com/blog/05_the_short_case_for_nvda un article sur les raisons pour lesquelles NVIDIA va se faire cahllengert sur ses marges 90% de marge quand meme parce que les plus gros GPU et CUDA qui est proprio mais des approches ardware alternatives existent qui sont plus efficientes (TPU et gros waffle) Google, MS et d'autres construisent leurs GPU alternatifs CUDA devient de moins en moins le linga franca avec l'investissement sur des langages intermediares alternatifs par Apple, Google OpenAI etc L'article parle de DeepSkeek qui est venu mettre une baffe dans le monde des LLMs Ils ont construit un competiteur a gpt4o et o1 avec 5M de dollars et des capacites de raisonnements impressionnant la cles c'etait beaucoup de trick d'optimisation mais le plus gros est d'avoir des poids de neurores sur 8 bits vs 32 pour les autres. et donc de quatizer au fil de l'eau et au moment de l'entrainement beaucoup de reinforcemnt learning innovatifs aussi et des Mixture of Expert donc ~50x moins chers que OpenAI Donc plus besoin de GPU qui on des tonnes de vRAM ah et DeepSeek est open source un article de semianalytics change un peu le narratif le papier de DeepSkeek en dit long via ses omissions par ensemple les 6M c'est juste l'inference en GPU, pas les couts de recherches et divers trials et erreurs en comparaison Claude Sonnet a coute 10M en infererence DeepSeek a beaucoup de CPU pre ban et ceratins post bans evalués a 5 Milliards en investissement. leurs avancées et leur ouverture reste extremement interessante Une intro à Apache Iceberg http://blog.ippon.fr/2025/01/17/la-revolution-des-donnees-lavenement-des-lakehouses-avec-apache-iceberg/ issue des limites du data lake. non structuré et des Data Warehouses aux limites en diversite de données et de volume entrent les lakehouse Et particulierement Apache Iceberg issue de Netflix gestion de schema mais flexible notion de copy en write vs merge on read en fonction de besoins garantie atomicite, coherence, isoliation et durabilite notion de time travel et rollback partitions cachées (qui abstraient la partition et ses transfos) et evolution de partitions compatbile avec les moteurs de calcul comme spark, trino, flink etc explique la structure des metadonnées et des données Guillaume s'amuse à générer des histoires courtes de Science-Fiction en programmant des Agents IA avec LangChain4j et aussi avec des workflows https://glaforge.dev/posts/2025/01/27/an-ai-agent-to-generate-short-scifi-stories/ https://glaforge.dev/posts/2025/01/31/a-genai-agent-with-a-real-workflow/ Création d'un générateur automatisé de nouvelles de science-fiction à l'aide de Gemini et Imagen en Java, LangChain4j, sur Google Cloud. Le système génère chaque nuit des histoires, complétées par des illustrations créées par le modèle Imagen 3, et les publie sur un site Web. Une étape d'auto-réflexion utilise Gemini pour sélectionner la meilleure image pour chaque chapitre. L'agent utilise un workflow explicite, drivé par le code Java, où les étapes sont prédéfinies dans le code, plutôt que de s'appuyer sur une planification basée sur LLM. Le code est disponible sur GitHub et l'application est déployée sur Google Cloud. L'article oppose les agents de workflow explicites aux agents autonomes, en soulignant les compromis de chaque approche. Car parfois, les Agent IA autonomes qui gèrent leur propre planning hallucinent un peu trop et n'établissent pas un plan correctement, ou ne le suive pas comme il faut, voire hallucine des “function call”. Le projet utilise Cloud Build, le Cloud Run jobs, Cloud Scheduler, Firestore comme base de données, et Firebase pour le déploiement et l'automatisation du frontend. Dans le deuxième article, L'approche est différente, Guillaume utilise un outil de Workflow, plutôt que de diriger le planning avec du code Java. L'approche impérative utilise du code Java explicite pour orchestrer le workflow, offrant ainsi un contrôle et une parallélisation précis. L'approche déclarative utilise un fichier YAML pour définir le workflow, en spécifiant les étapes, les entrées, les sorties et l'ordre d'exécution. Le workflow comprend les étapes permettant de générer une histoire avec Gemini 2, de créer une invite d'image, de générer des images avec Imagen 3 et d'enregistrer le résultat dans Cloud Firestore (base de donnée NoSQL). Les principaux avantages de l'approche impérative sont un contrôle précis, une parallélisation explicite et des outils de programmation familiers. Les principaux avantages de l'approche déclarative sont des définitions de workflow peut-être plus faciles à comprendre (même si c'est un YAML, berk !) la visualisation, l'évolutivité et une maintenance simplifiée (on peut juste changer le YAML dans la console, comme au bon vieux temps du PHP en prod). Les inconvénients de l'approche impérative incluent le besoin de connaissances en programmation, les défis potentiels en matière de maintenance et la gestion des conteneurs. Les inconvénients de l'approche déclarative incluent une création YAML pénible, un contrôle de parallélisation limité, l'absence d'émulateur local et un débogage moins intuitif. Le choix entre les approches dépend des exigences du projet, la déclarative étant adaptée aux workflows plus simples. L'article conclut que la planification déclarative peut aider les agents IA à rester concentrés et prévisibles. Outillage Vulnérabilité des proxy Maven https://github.blog/security/vulnerability-research/attacks-on-maven-proxy-repositories/ Quelque soit le langage, la techno, il est hautement conseillé de mettre en place des gestionnaires de repositories en tant que proxy pour mieux contrôler les dépendances qui contribuent à la création de vos produits Michael Stepankin de l'équipe GitHub Security Lab a cherché a savoir si ces derniers ne sont pas aussi sources de vulnérabilité en étudiant quelques CVEs sur des produits comme JFrog Artifactory, Sonatype Nexus, et Reposilite Certaines failles viennent de la UI des produits qui permettent d'afficher les artifacts (ex: mettez un JS dans un fichier POM) et même de naviguer dedans (ex: voir le contenu d'un jar / zip et on exploite l'API pour lire, voir modifier des fichiers du serveur en dehors des archives) Les artifacts peuvent aussi être compromis en jouant sur les paramètres propriétaires des URLs ou en jouant sur le nomage avec les encodings. Bref, rien n'est simple ni niveau. Tout système rajoute de la compléxité et il est important de les tenir à mettre à jour. Il faut surveiller activement sa chaine de distribution via différents moyens et ne pas tout miser sur le repository manager. L'auteur a fait une présentation sur le sujet : https://www.youtube.com/watch?v=0Z_QXtk0Z54 Apache Maven 4… Bientôt, c'est promis …. qu'est ce qu'il y aura dedans ? https://gnodet.github.io/maven4-presentation/ Et aussi https://github.com/Bukama/MavenStuff/blob/main/Maven4/whatsnewinmaven4.md Apache Maven 4 Doucement mais surement …. c'est le principe d'un projet Maven 4.0.0-rc-2 est dispo (Dec 2024). Maven a plus de 20 ans et est largement utilisé dans l'écosystème Java. La compatibilité ascendante a toujours été une priorité, mais elle a limité la flexibilité. Maven 4 introduit des changements significatifs, notamment un nouveau schéma de construction et des améliorations du code. Changements du POM Séparation du Build-POM et du Consumer-POM : Build-POM : Contient des informations propres à la construction (ex. plugins, configurations). Consumer-POM : Contient uniquement les informations nécessaires aux consommateurs d'artefacts (ex. dépendances). Nouveau Modèle Version 4.1.0 : Utilisé uniquement pour le Build-POM, alors que le Consumer-POM reste en 4.0.0 pour la compatibilité. Introduit de nouveaux éléments et en marque certains comme obsolètes. Modules renommés en sous-projets : “Modules” devient “Sous-projets” pour éviter la confusion avec les Modules Java. L'élément remplace (qui reste pris en charge). Nouveau type de packaging : “bom” (Bill of Materials) : Différencie les POMs parents et les BOMs de gestion des dépendances. Prend en charge les exclusions et les imports basés sur les classifiers. Déclaration explicite du répertoire racine : permet de définir explicitement le répertoire racine du projet. Élimine toute ambiguïté sur la localisation des racines de projet. Nouvelles variables de répertoire : ${project.rootDirectory}, ${session.topDirectory} et ${session.rootDirectory} pour une meilleure gestion des chemins. Remplace les anciennes solutions non officielles et variables internes obsolètes. Prise en charge de syntaxes alternatives pour le POM Introduction de ModelParser SPI permettant des syntaxes alternatives pour le POM. Apache Maven Hocon Extension est un exemple précoce de cette fonctionnalité. Améliorations pour les sous-projets Versioning automatique des parents Il n'est plus nécessaire de définir la version des parents dans chaque sous-projet. Fonctionne avec le modèle de version 4.1.0 et s'étend aux dépendances internes au projet. Support complet des variables compatibles CI Le Flatten Maven Plugin n'est plus requis. Prend en charge les variables comme ${revision} pour le versioning. Peut être défini via maven.config ou la ligne de commande (mvn verify -Drevision=4.0.1). Améliorations et corrections du Reactor Correction de bug : Gestion améliorée de --also-make lors de la reprise des builds. Nouvelle option --resume (-r) pour redémarrer à partir du dernier sous-projet en échec. Les sous-projets déjà construits avec succès sont ignorés lors de la reprise. Constructions sensibles aux sous-dossiers : Possibilité d'exécuter des outils sur des sous-projets sélectionnés uniquement. Recommandation : Utiliser mvn verify plutôt que mvn clean install. Autres Améliorations Timestamps cohérents pour tous les sous-projets dans les archives packagées. Déploiement amélioré : Le déploiement ne se produit que si tous les sous-projets sont construits avec succès. Changements de workflow, cycle de vie et exécution Java 17 requis pour exécuter Maven Java 17 est le JDK minimum requis pour exécuter Maven 4. Les anciennes versions de Java peuvent toujours être ciblées pour la compilation via Maven Toolchains. Java 17 a été préféré à Java 21 en raison d'un support à long terme plus étendu. Mise à jour des plugins et maintenance des applications Suppression des fonctionnalités obsolètes (ex. Plexus Containers, expressions ${pom.}). Mise à jour du Super POM, modifiant les versions par défaut des plugins. Les builds peuvent se comporter différemment ; définissez des versions fixes des plugins pour éviter les changements inattendus. Maven 4 affiche un avertissement si des versions par défaut sont utilisées. Nouveau paramètre “Fail on Severity” Le build peut échouer si des messages de log atteignent un niveau de gravité spécifique (ex. WARN). Utilisable via --fail-on-severity WARN ou -fos WARN. Maven Shell (mvnsh) Chaque exécution de mvn nécessitait auparavant un redémarrage complet de Java/Maven. Maven 4 introduit Maven Shell (mvnsh), qui maintient un processus Maven résident unique ouvert pour plusieurs commandes. Améliore la performance et réduit les temps de build. Alternative : Utilisez Maven Daemon (mvnd), qui gère un pool de processus Maven résidents. Architecture Un article sur les feature flags avec Unleash https://feeds.feedblitz.com//911939960/0/baeldungImplement-Feature-Flags-in-Java-With-Unleash Pour A/B testing et des cycles de développements plus rapides pour « tester en prod » Montre comment tourner sous docker unleash Et ajouter la librairie a du code java pour tester un feature flag Sécurité Keycloak 26.1 https://www.keycloak.org/2025/01/keycloak-2610-released.html detection des noeuds via la proble base de donnée aulieu echange reseau virtual threads pour infinispan et jgroups opentelemetry tracing supporté et plein de fonctionalités de sécurité Loi, société et organisation Les grands morceaux du coût et revenus d'une conférence. Ici http://bdx.io|bdx.io https://bsky.app/profile/ameliebenoit33.bsky.social/post/3lgzslhedzk2a 44% le billet 52% les sponsors 38% loc du lieu 29% traiteur et café 12% standiste 5% frais speaker (donc pas tous) Ask Me Anything Julien de Provin: J'aime beaucoup le mode “continuous testing” de Quarkus, et je me demandais s'il existait une alternative en dehors de Quarkus, ou à défaut, des ressources sur son fonctionnement ? J'aimerais beaucoup avoir un outil agnostique utilisable sur les projets non-Quarkus sur lesquels j'intervient, quitte à y metttre un peu d'huile de coude (ou de phalange pour le coup). https://github.com/infinitest/infinitest/ Conférences La liste des conférences provenant de Developers Conferences Agenda/List par Aurélie Vache et contributeurs : 6-7 février 2025 : Touraine Tech - Tours (France) 21 février 2025 : LyonJS 100 - Lyon (France) 28 février 2025 : Paris TS La Conf - Paris (France) 6 mars 2025 : DevCon #24 : 100% IA - Paris (France) 13 mars 2025 : Oracle CloudWorld Tour Paris - Paris (France) 14 mars 2025 : Rust In Paris 2025 - Paris (France) 19-21 mars 2025 : React Paris - Paris (France) 20 mars 2025 : PGDay Paris - Paris (France) 20-21 mars 2025 : Agile Niort - Niort (France) 25 mars 2025 : ParisTestConf - Paris (France) 26-29 mars 2025 : JChateau Unconference 2025 - Cour-Cheverny (France) 27-28 mars 2025 : SymfonyLive Paris 2025 - Paris (France) 28 mars 2025 : DataDays - Lille (France) 28-29 mars 2025 : Agile Games France 2025 - Lille (France) 3 avril 2025 : DotJS - Paris (France) 3 avril 2025 : SoCraTes Rennes 2025 - Rennes (France) 4 avril 2025 : Flutter Connection 2025 - Paris (France) 4 avril 2025 : aMP Orléans 04-04-2025 - Orléans (France) 10-11 avril 2025 : Android Makers - Montrouge (France) 10-12 avril 2025 : Devoxx Greece - Athens (Greece) 16-18 avril 2025 : Devoxx France - Paris (France) 23-25 avril 2025 : MODERN ENDPOINT MANAGEMENT EMEA SUMMIT 2025 - Paris (France) 24 avril 2025 : IA Data Day 2025 - Strasbourg (France) 29-30 avril 2025 : MixIT - Lyon (France) 7-9 mai 2025 : Devoxx UK - London (UK) 15 mai 2025 : Cloud Toulouse - Toulouse (France) 16 mai 2025 : AFUP Day 2025 Lille - Lille (France) 16 mai 2025 : AFUP Day 2025 Lyon - Lyon (France) 16 mai 2025 : AFUP Day 2025 Poitiers - Poitiers (France) 24 mai 2025 : Polycloud - Montpellier (France) 24 mai 2025 : NG Baguette Conf 2025 - Nantes (France) 5-6 juin 2025 : AlpesCraft - Grenoble (France) 5-6 juin 2025 : Devquest 2025 - Niort (France) 10-11 juin 2025 : Modern Workplace Conference Paris 2025 - Paris (France) 11-13 juin 2025 : Devoxx Poland - Krakow (Poland) 12-13 juin 2025 : Agile Tour Toulouse - Toulouse (France) 12-13 juin 2025 : DevLille - Lille (France) 13 juin 2025 : Tech F'Est 2025 - Nancy (France) 17 juin 2025 : Mobilis In Mobile - Nantes (France) 24 juin 2025 : WAX 2025 - Aix-en-Provence (France) 25-26 juin 2025 : Agi'Lille 2025 - Lille (France) 25-27 juin 2025 : BreizhCamp 2025 - Rennes (France) 26-27 juin 2025 : Sunny Tech - Montpellier (France) 1-4 juillet 2025 : Open edX Conference - 2025 - Palaiseau (France) 7-9 juillet 2025 : Riviera DEV 2025 - Sophia Antipolis (France) 18-19 septembre 2025 : API Platform Conference - Lille (France) & Online 2-3 octobre 2025 : Volcamp - Clermont-Ferrand (France) 6-10 octobre 2025 : Devoxx Belgium - Antwerp (Belgium) 9-10 octobre 2025 : Forum PHP 2025 - Marne-la-Vallée (France) 16-17 octobre 2025 : DevFest Nantes - Nantes (France) 4-7 novembre 2025 : NewCrafts 2025 - Paris (France) 6 novembre 2025 : dotAI 2025 - Paris (France) 7 novembre 2025 : BDX I/O - Bordeaux (France) 12-14 novembre 2025 : Devoxx Morocco - Marrakech (Morocco) 28-31 janvier 2026 : SnowCamp 2026 - Grenoble (France) 23-25 avril 2026 : Devoxx Greece - Athens (Greece) 17 juin 2026 : Devoxx Poland - Krakow (Poland) Nous contacter Pour réagir à cet épisode, venez discuter sur le groupe Google https://groups.google.com/group/lescastcodeurs Contactez-nous via X/twitter https://twitter.com/lescastcodeurs ou Bluesky https://bsky.app/profile/lescastcodeurs.com Faire un crowdcast ou une crowdquestion Soutenez Les Cast Codeurs sur Patreon https://www.patreon.com/LesCastCodeurs Tous les épisodes et toutes les infos sur https://lescastcodeurs.com/

Le BigDataHebdo, reçoit Mehdi, Developer Advocate chez MotherDuck, pour explorer l'univers de DuckDB et MotherDuck. Au programme, les origines académiques de DuckDB, son évolution en tant que moteur SQL analytique performant, et son extension MotherDuck qui permet de l'utiliser comme un Data Warehouse en ligne.Show notes sur http://bigdatahebdo.com/podcast/episode-211-motherduck/

What if querying terabytes of data in milliseconds was your new normal in modern cloud data warehousing? Join The Ravit Show with Eldad Farkash, Firebolt CEO, as we explore the architecture and innovations behind Firebolt—the cloud data warehouse built for data-intensive applications. Here's a look at what's ahead:

Microsoft Fabric offers two enterprise-scale, open-standard format workloads for data storage: Warehouse and Lakehouse. Which service should you choose? In this episode, we dive into the technical components of OneLake, along with some of the decisions you'll be asked to make as you start to build out your data infrastructure. These are two good articles we mention in the podcast that could help inform your decision on the services to implement in your OneLake. Microsoft Fabric Decision Guide: Choose between Warehouse and Lakehouse - Microsoft Fabric | Microsoft Learn Lakehouse vs Data Warehouse vs Real-Time Analytics/KQL Database: Deep Dive into Use Cases, Differences, and Architecture Designs | Microsoft Fabric Blog | Microsoft Fabric We hope you enjoyed this conversation on the nuances of data storage within Microsoft OneLake! If you have questions or comments, please send them our way. We would love to answer your questions on a future episode. Leave us a comment and some love ❤️ on LinkedIn, X, Facebook, or Instagram. The show notes for today's episode can be found at Episode 283: Data Lakehouse vs Data Warehouse vs My House. Have fun on the SQL Trail!

Thu, 05 Dec 2024 23:00:00 +0000 https://mydata.podigee.io/248-new-episode 10d3a398ad306a031ce82451897595b0 Wie digitalisiert man die Gastronomiebranche von Grund auf? Welche digitalen Tools brauchen Restaurants heute? Und wie nutzt man Daten, um bessere Geschäftsentscheidungen zu treffen? Darum geht es in der neuen Folge von MY DATA IS BETTER THAN YOURS, in der Host Jonas Rashedi mit Dr. Volker Gläser spricht. Dieser verantwortet als Chief Operating Officer die Themen Strategy & Business Intelligence, Finanzen, Qualitätsmanagement sowie People & Culture. Im Gespräch geht es zunächst um den Aufbau von DISH. Das Unternehmen hat einen kompletten digitalen Service entwickelt, um Gastronomen bei der Digitalisierung zu unterstützen. Von der Website über Reservierungssysteme bis hin zu Payment-Lösungen bietet DISH alle wichtigen Tools aus einer Hand. Volker ist dabei in einem zentralen Team, das aus mehreren wichtigen Einheiten besteht: einem Analytics-Team, Infrastruktur-Experten, einem Consumer Engagement Team, einem Website-Team und einem Team für Schnittstellen. Der Aufbau war eine besondere Herausforderung, weil man bei null anfing. Für Volker war das Ziel, die unterschiedlichen Stakeholder mit ihren verschiedenen Herausforderungen und Bedürfnissen zusammenzubringen. Besonders spannend sind die konkreten Use Cases: DISH nutzt Daten, um Gastronomen bei der Menüoptimierung zu unterstützen und ihre Profitabilität zu steigern. Dabei ist es Volker wichtig, möglichst einfach anzufangen und zunächst zu schauen, welche Daten überhaupt verfügbar sind. Bei der technischen Implementierung setzt DISH auf moderne Tools wie Power BI für Visualisierungen und Snowflake als Data Warehouse. Die Entscheidungen für diese Tools wurden in enger Abstimmung mit den Stakeholdern getroffen. Zum Schluss geht es um die Zukunft der Branche und wie digitale Lösungen die Gastronomie verändern werden. Volker teilt seine Vision einer vollständig vernetzten Restaurant-Experience. MY DATA IS BETTER THAN YOURS ist ein Projekt von BETTER THAN YOURS, der Marke für hochwertige Podcasts. Zum LinkedIn-Profil von Volker: https://www.linkedin.com/in/volkerglaeser/ Zur Webseite von DISH: https://www.dish.co/DE/de/ Zu allen wichtigen Links rund um Jonas und den Podcast: https://linktr.ee/jonas.rashedi 00:00:00 Intro und Begrüßung 00:02:52 Geschichte und Entwicklung von DISH 00:08:32 Digitalisierung in der Gastronomie 00:12:11 Strategie von DISH und Verortung in der Metro AG 00:14:29 Datennutzung und Kundenvorteile 00:26:06 Personalisierung und Innovation 00:30:50 Menüoptimierung und Analytics 00:37:15 Zukunftstrends der Branche 00:43:32 Wettbewerb in der Branche 00:48:24 Persönliche Datennutzung von Volker full no

Zusammenfassend unsere Must-Haves: Datenbank / DWH  Lösung zur Datenvisualisierung Möglichkeit, unkompliziert zu entwickeln (lokal oder im Web) Versionskontrolle / CI/CD Deployment-Lösung Trennung von Entwicklungs- und Produktivumgebung Monitoring für Modell & Ressourcen   Verwandte Podcast-Episoden Folge #2: Erfolgsfaktoren für Predictive Analytics Projekte Folge #5: Data Warehouse vs. Data Lake vs. Data Mesh Folge #20: Ist Continuous Integration (CI) ein Muss für Data Scientists? Folge #21: Machine Learning Operations (MLOps) Folge #29: Die Qual der Wahl: Data Science Plattform vs. Customized Stack Folge #35: Erfolgsfaktoren für Machine Learning Projekte mit Philipp Jackmuth von dida Folge #43: Damit es im Live-Betrieb nicht kracht: Vermeidung von Overfitting & Data Leakage Folge #54: Modell-Deployment: Wie bringe ich mein Modell in die Produktion?   Technologien & Tools Datenvisualisierung: Azure Databricks, AWS Quicksight, Redash Entwicklungsumgebung: VSCode, INWT Python IDE V2, Remote Explorer, Pycharm Versionskontrolle: GitHub, GitLab, Azure DevOps CI/CD: GitHub Actions, GitLab CI, Jenkins Deployment: Kubernetes, Docker, Helm, ArgoCD Experiment-Tracking: MLFlow, DVC, Tensorboard Monitoring: Prometheus, Grafana, AWS Cloudwatch

If you have a data warehouse, what do you think your ratio of reads to writes is on any given day? Do you think 1:1, as in one read for each write? Is it 10:1, with 10 reads for each write? 100:1? Do you track this in any way? One would think that most of the databases we work on in the transactional world have many more reads than writes. I'd have assumed the ratios might be higher for data warehouses, where we load data that is queried (read) as the primary use case. After all, I expect that there are lots of people querying data that is loaded into this warehouse, with relatively few changes. Read the rest of The Load of Real Time Data Warehouses

What makes data lakehouses a game changer in modern data management? In this episode, Bill sits down with Alex Merced, Senior Tech Evangelist at Dremio, to explore the evolution of data lakehouses and their role in bridging the gap between data lakes and data warehouses. Alex breaks down the components of data lakehouses and dives into the rise of Apache Iceberg.---------Key Quotes:“I love just get really deep into technology, really see what it does. And then scream at the rooftops how cool it is. And basically that was my charter. And [Apache] Iceberg, the more I learned about it, the more I realized this is really interesting.”“Interoperability and data. Basically, a lot of the things that kept data in silos is now breaking apart.”"So here we're talking about something that's going to be a standard. And that's when I think of the highest levels of openness matter because if it's something that a whole industry is going to build on, it should be something that the whole industry has to say in its evolution…And that's the beauty of openness that it does create these nice sort of places where we can collaborate and compete together.”--------Timestamps: (01:32) How Alex got started in his career(03:54) Breaking down data lakehouses(07:08) The idea behind an open data lakehouse(10:10) Alex's involvement with Apache Iceberg(15:13) Key components of a data lakehouse(23:41) The growth of Apache Iceberg(32:07) Dremio's Apache Iceberg crash course(38:43) Explaining self-service analytics--------Sponsor:Over the Edge is brought to you by Dell Technologies to unlock the potential of your infrastructure with edge solutions. From hardware and software to data and operations, across your entire multi-cloud environment, we're here to help you simplify your edge so you can generate more value. Learn more by visiting dell.com/edge for more information or click on the link in the show notes.--------Credits:Over the Edge is hosted by Bill Pfeifer, and was created by Matt Trifiro and Ian Faison. Executive producers are Matt Trifiro, Ian Faison, Jon Libbey and Kyle Rusca. The show producer is Erin Stenhouse. The audio engineer is Brian Thomas. Additional production support from Elisabeth Plutko.--------Links:Follow Bill on LinkedInFollow Alex on LinkedIn

As organizations grapple with data spread across various storage locations, solutions like Coginiti Hybrid Query offer a much-needed alternative to fragmented tools. Published at: https://www.eckerson.com/articles/a-novel-approach-for-reducing-cloud-data-warehouse-expenses-from-coginiti

Jonathan Mendez has been a founder and leader in Adtech and Martech for two decades, with a focus on building first-party data products to optimize media performance.  He is the founder and CEO at Neuralift AI, having prior to that been Chief Digital Officer at a major cruise line, and having also spent five years building composable CDPs (Customer Data Platform) for global retail brands and telcos. He was also the Founder and CEO of Yieldbot, which in 2016 was the fourth largest Digital Advertising Network. He was also the CSO at Offermatica, eventually acquired by Omniture, now part of Adobe.  Jonathan's blog has been active for 17 years and is a recognized source of insights into AdTech, MarTech or Media. References: Jonathan Mendez (blog): Optimize & Prophesize Neuralift AI Jonathan Mendez on X Jonathan Mendez on LinkedIn Tejas Manohar (Hightouch): data activation and composable CDPs in a privacy-first world (Masters of Privacy) Nicola Newitt (Infosum): the legal case for Data Clean Rooms (Masters of Privacy) Matthias Eigenmann (Decentriq): Confidential Computing, contractual relationships and legal bases for Data Clean Rooms (Masters of Privacy)  

In this captivating episode of the Tech Optimist podcast, Managing Partner Ray Wu sits down with Nate Holiday, the visionary co-founder and CEO of Space and Time. Dive into the intricate world of decentralized data warehouses as they discuss how Space and Time ensures verifiability and transparency in database operations. Learn about their innovative technology, Proof of SQL, which secures data interactions within blockchain environments, offering a new level of security and integrity for enterprise applications. Tune in to uncover how Space and Time is transforming data management and security, forging a path toward a more trustworthy digital infrastructure.Nate's Ask: Nate invites the community to join and engage with Space and Time's community of developers, contributing to their open-source projects on GitHub. He also encourages the adoption of their verifiable database solutions available through platforms like Microsoft's Azure Marketplace and Google's marketplace, to enhance data security and transparency in enterprise applications.To Learn More:Alumni Ventures (AV)AV LinkedInAV AI FundTech OptimistSpace and TimeSpeakers:Ray Wu - Guest Nate Holiday - Guest Chapters:(00:00) - Intro (01:51) - Interview (17:02) - Nate's Ask (24:31) - Closing Legal Disclosure:https://av-funds.com/tech-optimist-disclosures

This is episode 281 recorded on August 9th, 2024 where John & Jason talk to Charles Webb, Principal PM Manager for Data Warehouse in Microsoft Fabric, about Fabric, Data Warehouse, his history with Power BI & Dataflows, AI, college sports & business, his work in philanthropy and much more.

In this episode, host Sean Falconer sits down with Eric Flaningam, a researcher at Felicis Ventures, to explore the fascinating world of data warehouses. They dive into the history, evolution, and future trends of data warehousing, shedding light on its importance. Key topics discussed include an overview of the article "A Primer on Data Warehouses," and the definition and key characteristics of data warehouses. They also cover the historical evolution and major milestones in data warehousing, the shift from batch processing to real-time data, and the convergence of data warehouses and SQL. Eric and Sean discuss the impact of unstructured and complex data, advancements in technology and their effect on data warehouses, and the technical architecture and components of a typical data warehouse. They share real-world benefits and use cases of data warehouses, common challenges in implementing and maintaining data warehouses, and future trends and the influence of AI and machine learning on data warehouses. For further reading, check out Eric Flaningam's article, A Primer on Data Warehouses: https://www.generativevalue.com/p/a-primer-on-data-warehouses

Companies are more successful and efficient when they have current, accurate data. Employing metrics, dashboards, and data warehouses—a central repository of data from different systems—can help businesses achieve their goals. Saad Shah of RentViewer joins the podcast to discuss the importance of utilizing data and analytics in the real estate management industry. Doing so gives companies better visibility and business intelligence; it eliminates manual work; and streamlines expense and work order management. By evaluating the processes and tasks that are costing you the most money, you can use data to improve your bottom line.Learn more about Rent Manager's industry-leading accounting, reporting, maintenance, and communication features at RentManager.com, or connect with us on LinkedIn, Facebook, Instagram, YouTube, and Twitter.You can learn more about Saad Shah on LinkedIn, and RentViewer on the company's website.Visit RentManager.com/Podcast to submit an idea for an upcoming episode of Beyond Rent and discover more about the program.

Highlights from this week's conversation include:Ryan's Background and Roles in Data (0:05)Data Activation and Dashboard Staleness (1:27)Profiles and Data Activation (2:54)Customer-Facing Experience and Product Management (3:40)Profiles Product Overview (5:10)Use Cases for Profiles (6:44)Challenges with Data Projects (9:19)Entity Management and Account Views (15:33)Handling Entities and Duplicates (17:55)Challenges in Entity Management (22:18)Product Management and Data Solutions (26:08)Reverse ETL and Data Movement (31:58)Accessibility of Data Warehouses (36:14)Profiles and Entity Features (37:47)Cohorts Creation and Use Cases (41:17)Customer Data and Targeting (43:09)Activations and Reverse ETL (45:57)ML and AI Use Cases (55:53)Data Activation and ML Predictions (57:02)Spicy Take and Future Product Features (59:47)ETL Evolution and Cloud Tools (1:00:50)Unbundling and Future Trends (1:02:10)The Data Stack Show is a weekly podcast powered by RudderStack, the CDP for developers. Each week we'll talk to data engineers, analysts, and data scientists about their experience around building and maintaining data infrastructure, delivering data and data products, and driving better outcomes across their businesses with data.RudderStack helps businesses make the most out of their customer data while ensuring data privacy and security. To learn more about RudderStack visit rudderstack.com.

Earnings season - better and stats - BIGGEST BUYBACK EVER - We are gauging investor sentiment --- Remember - Confidence  and Sentiment (Cheer-leading helps) PLUS we are now on Spotify and Amazon Music/Podcasts! Click HERE for Show Notes and Links DHUnplugged is now streaming live - with listener chat. Click on link on the right sidebar. Love the Show? Then how about a Donation? Follow John C. Dvorak on Twitter Follow Andrew Horowitz on Twitter DONATE - Show 700 Campaign Warm Up - Earnings season - better and stats - BIGGEST BUYBACK EVER - We are gauging investor sentiment -- --- Remember - Confidence  and Sentiment (Cheer-leading helps) - Announcing the WINNER  CTP for Apple - Fake Work? Market Update - If down - buy.... Names that were hammered due to earnings catching bids again - Follow up - Utilities - Fed Speaks - Can't stop the Dove - Employment - Excitement about the Unemployment Rate Earnings Season Update: - Overall, 80% of the companies in the S&P 500 have reported actual results for Q1 2024 to date. - Of these companies, 77% have reported actual EPS above estimates, which is equal to the 5-year average of 77% but above the 10-year average of 74%. - In aggregate, companies are reporting earnings that are 7.5% above estimates, which is also below the 5-year average of 8.5% but above the 10-year average of 6.7% - Eight of the eleven sectors are reporting year-over-year earnings growth, led by the Communication Services, Utilities, Consumer Discretionary, and Information Technology sectors. - Three sectors are reporting a year-over-year decline in earnings: Energy, Health Care, and Materials. - Revenue - up again - estimated to be 4.1% when all said and done. -  -    If 4.1% is the actual revenue growth rate for the quarter, it will mark the 14th consecutive quarter of revenue growth for the index. Fake Work - An investor at famed Silicon Valley firm Andreessen Horowitz is the latest VC to get involved in the debate around "fake work" in the tech industry. - Ulevitch went on to point the finger at Google specifically, calling it "an amazing example." - "I don't think it's crazy to believe that half the white-collar staff at Google probably does no real work," he said. "The company has spent billions and billions of dollars per year on projects that go nowhere for over a decade, and all that money could have been returned to shareholders who have retirement accounts." - Marc Andreessen has criticized a managerial "laptop class" and tweeted in 2022, "The good big companies are overstaffed by 2x. The bad big companies are overstaffed by 4x or more." Buy 'em - Companies that took a hit after earnings (NFLX, AMD) getting bid again - NFLX gapped lower from ~$608 to $551 and now $592 - AMD dropped from $160 to $140 and now $156 - SPY , IWM and QQQ- Now above the 50day Moving average again Follow Up - Utilities - Just wanted to provide this idea again - Data Warehouses and other AI Power hungry places --- Symbol list of some utilities to look at further - SO, NEE, EXC, CMS - Natural gas producers are planning for a significant spike in demand over the next decade, as artificial intelligence drives a surge in electricity consumption that renewables may struggle to meet alone. - After a decade of flat power growth in the U.S., electricity demand is forecast to grow as much as 20% by 2030, according to a Wells Fargo analysis published in April. Power companies are moving to quickly secure energy as the rise of AI coincides with the expansion of domestic semiconductor and battery manufacturing as well as the electrification of the nation's vehicle fleet. - AI data centers alone are expected to add about 323 terawatt hours of electricity demand in the U.S. by 2030 Utilities ETF Apple - Earnings - Nothing great in the earnings. --- A few pockets of sunshine.... --- Raises dividend and $110 BILLION buyback - largest buyback EVER ...

Today's episode is with Nikhil Benesch, who's the co-founder and CTO at Materialize, an Operational Data Warehouse. Materialize gets you the best of both worlds, combining the capabilities of your data warehouse with the immediacy of streaming. This fusion allows businesses to operate with data in real-time. We discussed the data infrastructure stuff of it, how they built it, how they think about billing, how they think about cloud primitives and what they wish they had.

In this episode Brad Redding and Jon Cairo are joined by Jacob Cook, Founder of Tadpull, to get into the weeds on data warehousing, ML and AI use cases within your data warehouse, how to sell data executives, how quality data can drive better personalization and journeys, plus much more in this jam packed episode.-----We release new episodes every week that go deep into the world of tracking, analytics, and conversion optimization.-----Links Referenced:TadpullSean from Tydo Puma PostPuma GCP Announcement Jupyter NotepadCustomer Base Audit Book-----And if you're new to Elevar, Elevar automates server-side conversion tracking for Shopify. Check us out!-----Previous episodes you might like:100K/spend day myths with Nigel ThomasSignal Loss -- what it is and how it impacts marketersDeep dive with Simo Ahava on intersection of technical marketersClient vs server-side cookies and server-side tracking 101How to double conversion rate in 100 days with Ben ZettlerHow to blend attribution + conversion tracking + data warehousing for insights with Austin Harrison from Northbeam

This episode features an interview with Kevin Niparko, Vice President of Product for Twilio Segment CDP. Kevin joined the team in 2015 to lead Growth & Analytics, before helping form Segment's Product Management organization. He's led a variety of Twilio Segment's products over the years, from Connections, Cloud Sources and ETL, and Profiles.In this episode, Kailey and Kevin discuss future proofing organizations to take advantage of AI breakthroughs, accelerating time to value, and solving problems through data strategy alignment.-------------------Key Takeaways:Keeping up with the evolving landscape of data management requires flexibility, extensibility, and interoperability built into your data architecture.How modern enterprises can quickly and continuously adapt to the proliferation of tools and technologies.The importance of creating a data strategy to evolve with the needs of your business and serve your cross-functional stakeholders.-------------------“The problems that we see our customers running into that really feel intractable are the ones more on the people and the process side of data. It's something that technology can help with. It's something that CDPs can play a role in. But, I think we're also realistic that no tech or software is going to be the silver bullet. It's about different parts of the organization coming together and aligning on an overall data strategy that everybody will abide by.” – Kevin Niparko-------------------Episode Timestamps:‍*(02:59) - Kevin's career journey*(05:52) - Trends impacting technology and customer engagement*(09:04) - Components of a flexible enterprise*(16:55) - How AI intersects with data management*(30:04) - How Kevin defines “good data”*(36:50) - Kevin's recommendations for upleveling inclusive marketing strategies-------------------Links:Connect with Kevin on LinkedInConnect with Kailey on LinkedInLearn more about Caspian Studios-------------------SponsorGood Data, Better Marketing is brought to you by Twilio Segment. In today's digital-first economy, being data-driven is no longer aspirational. It's necessary. Find out why over 20,000 businesses trust Segment to enable personalized, consistent, real-time customer experiences by visiting Segment.com

Mike, Seth, & Tommy discuss a great article by Sam Debruyn on what really a Fabric Lakehouse is, and where should we spend out time. Get in touch: Send in your questions or topics you want us to discuss by tweeting to @PowerBITips with the hashtag #empMailbag or submit on the PowerBI.tips Podcast Page. Visit PowerBI.tips: https://powerbi.tips/ Watch the episodes live every Tuesday and Thursday morning at 730am CST on YouTube: https://www.youtube.com/powerbitips Subscribe on Spotify: https://open.spotify.com/show/230fp78XmHHRXTiYICRLVv Subscribe on Apple: https://podcasts.apple.com/us/podcast/explicit-measures-podcast/id1568944083‎ Check Out Community Jam: https://jam.powerbi.tips Follow Mike: https://www.linkedin.com/in/michaelcarlo/ Follow Seth: https://www.linkedin.com/in/seth-bauer/ Follow Tommy: https://www.linkedin.com/in/tommypuglia/

In this episode of the Kubernetes Unpacked Podcast, Kristina and Michael catch up with Mark from Yellowbrick to talk about all things underlying architecture. Very rarely do we get a vendor to chat about what's going on underneath the hood and how a particular application stack/tool is running, so this was an awesome episode! Mark... Read more »

In this episode of the Kubernetes Unpacked Podcast, Kristina and Michael catch up with Mark from Yellowbrick to talk about all things underlying architecture. Very rarely do we get a vendor to chat about what's going on underneath the hood and how a particular application stack/tool is running, so this was an awesome episode! Mark... Read more »

In this episode of the Kubernetes Unpacked Podcast, Kristina and Michael catch up with Mark from Yellowbrick to talk about all things underlying architecture. Very rarely do we get a vendor to chat about what's going on underneath the hood and how a particular application stack/tool is running, so this was an awesome episode! Mark... Read more »

“We love working with credit unions to become more data-driven, so they can better support their members.” - Andrea BrownThank you for tuning in to The CUInsight Network, with your host, Lauren Culp, President & CEO of CUInsight. In The CUInsight Network, we take a deeper dive with the thought leaders who support the credit union community. We discuss issues and challenges facing credit unions and identify best practices to learn and grow together.My guest on today's show is Andrea Brown, SVP of Growth at Lodestar. Andrea is a return guest to the podcast and shares what has changed since last year and what remains a common focus for leaders. Lodestar is a data warehouse and analytics partner for credit unions. They provide a full-service analytics platform of data connectors, visuals, workflows, and strategic guidance to help credit unions move forward in their analytics journey. During our conversation, Andrea discusses which data strategies credit unions should be focusing on to benefit business goals. She explains how leveraging data analytics and choosing the right data warehouse is crucial for a successful core conversion. Listen as Andrea talks about growth plans for the future and continuing to support credit unions with the complex technology systems needed to embrace efficiency and sustainability.As we wrap up the episode, Andrea talks about spending time with her family, splurging on new experiences, and preferring the audiobook of this recent read. Enjoy my conversation with Andrea Brown!Find the full show notes on cuinsight.com.Connect with Andrea:Andrea Brown, SVP of Growth at Lodestarandrea.brown@lodestartech.calodestartech.caAndrea: LinkedInLodestar: LinkedIn

Unser heutiger Gast Benjamin Aunkofer ist in folgenden Rollen unterwegs: - Chief Data Scientist & Founder von DATANOMIQ (innovative Data & AI Services for all companies) - Founder & Co-CEO von AUDAVIS (AI-powered Automated Auditing Cloud Platform) - Trainer für Data Science und AI - Interim Head of BI / Process Mining / Data Science - Betreiber des Blogs www.data-science-blog.com ----------------------------------- Wir unterhalten uns unter anderem über diese Themen: 1. Data Scientists sind oft schlecht qualifiziert, haben oft "entweder-oder"-Skills. 2. Unternehmen brauchen immer noch mehr Data Engineers als Data Scientists. 3. Business Intelligence, Process Mining und Data Science werden oft zu sehr voneinander getrennt gesehen. Und Datenlücken sind keine Hindernisse, sondern Findings für Daten- und Prozesstransparenz. 4. Data Lakehousing und Data Mesh servieren die Daten für BI, Process Mining und Data Science zu gleich. Unternehmen verlieren Geld mit doppelter Datenbereitstellung/-haltung. 5. KI automatisiert nicht nur Medien, Finanzberichte und Wirtschaftsprüfung, sondern ersetzt auch Data Engineers / Scientists. ----------------------------------- 0:00 - Freelancer sein ist nicht leicht und Weg zu DATANOMIQ 7:00 - Ein Blick auf das Data Science Studium 17:28 - Viele Data Scientists sind keine Data Scientists 25:00 - Data Warehouse, Data Lakehouse und Machine Learning 36:10 - Was macht Benjamin und was macht DATANOMIQ eigentlich? 40:30 - Hier verlieren Unternehmen am meisten Geld in Sachen DATA 43:50 - Aufbau einer Datenabteilung 47:00 - Gute Weiterbildung ist der Schlüssel für Data Scientists 52:15 - 150.000€+ Gehalt für Top Data Scientists 54:00 - Gründung von AUDAVIS 59:00 - Hybride AI 1:11:38 - Zukunftsausblick ----------------------------------- Weiterführende Informationen: ► LinkedIn Benjamin: https://www.linkedin.com/in/benjamin-aunkofer-98710714/ ► LinkedIn Bernard: https://www.linkedin.com/in/bernardsonnenschein/ ► Nutze jetzt den Code "Friends20" auf https://www.eventbrite.de/e/dataunplugged-tickets-686542897287, um einen Rabatt von 20% auf das Ticket zu erhalten. ► Wir danken außerdem unserem Partner, der Public Cloud Group (PCG): https://hubs.li/Q02cH6qN0

Norbert Kremer, Ph.D., cloud solution architect and TDWI faculty member, joins host Andrew Miller to discuss his upcoming course on building a data warehouse in the cloud. For more information on Norbert's course please visit Building a Data Warehouse the Google Cloud Way and for the full agenda please visit TDWI Transform 24 Las Vegas.

Changes to Google Analytics have brought questions about how to retain historical data to the front of the marketing analytics world. One option for keeping your data stable in a rapidly changing landscape is a marketing data warehouse.  In this episode we're joined by Christina Davis, Vice President of Media & Analytics at Tambourine and Evan Kaeding, Lead Sales Engineer and Product Evangelist at Supermetrics to learn everything you need to get started with a marketing data warehouse. We'll cover their benefits and explain why now is an especially great time to move to one. Watch on-demand webinar to learn ‘Everything you need to know to build your marketing data warehouse': https://supermetrics.com/webinars/build-marketing-data-warehouse  Check out Supermetrics: https://supermetrics.com/start-trial 

TNS host Alex Williams is joined by Florian Valeye, a data engineer at Back Market, to shed light on the evolving landscape of data engineering, particularly focusing on Delta Lake and his contributions to open source communities. As a member of the Delta Lake community, Valeye discusses the intersection of data warehouses and data lakes, emphasizing the need for a unified platform that breaks down traditional barriers.Delta Lake, initially created by Databricks and now under the Linux Foundation, aims to enhance reliability, performance, and quality in data lakes. Valeye explains how Delta Lake addresses the challenges posed by the separation of data warehouses and data lakes, emphasizing the importance of providing asset transactions, real-time processing, and scalable metadata.Valeye's involvement in Delta Lake began as a response to the challenges faced at Back Market, a global marketplace for refurbished devices. The platform manages large datasets, and Delta Lake proved to be a pivotal solution in optimizing ETL processes and facilitating communication between data scientists and data engineers.The conversation delves into Valeye's journey with Delta Lake, his introduction to Rust programming language, and his role as a maintainer in the Rust-based library for Delta Lake. Valeye emphasizes Rust's importance in providing a high-level API with reliability and efficiency, offering a balanced approach for developers.Looking ahead, Valeye envisions Delta Lake evolving beyond traditional data engineering, becoming a platform that seamlessly connects data scientists and engineers. He anticipates improvements in data storage optimization and envisions Delta Lake serving as a standard format for machine learning and AI applications.The conversation concludes with Valeye reflecting on his future contributions, expressing a passion for Rust programming and an eagerness to explore evolving projects in the open-source community. Learn more from The New Stack about Delta Lake and The Linux Foundation:Delta Lake: A Layer to Ensure Data QualityData in 2023: Revenge of the SQL NerdsWhat Do You Know about Your Linux System?

No episódio de hoje, Luan Moreno, Mateus Oliveira e Orlando Marley entrevistam Bill Inmon, criador do conceito de Data Warehouse e escritor de diversos livros com temáticas voltadas para dados.Data Warehouse é o conceito de centralização de dados analíticos das organizações, de forma estruturar um visão 360° do business. Neste episódio, você irá aprender: Diferenças entre OLTP e OLAP;Histórico dos dados para tomada de decisão;Criar um processo resiliente para entender os fatos dos dados.Falamos também, neste bate-papo, sobre os seguintes temas: História do Bill Inmon;Pilares de sistemas analíticos;Nova geração de plataforma de dados analíticos;Aprenda mais sobre análise de dados, como utilizar tecnologias para tornar o seu ambiente analítico confiável e resiliente com as palavras do pai do Data Warehouse. Bill Inmon = Linkedin Luan Moreno = https://www.linkedin.com/in/luanmoreno/

Welcome to the Boomer Briefing Podcast, where we help you solve a critical business issue in 20 minutes or less. On this episode of the Boomer Briefing Podcast, Marc Staut, Shareholder and Chief Innovation Technology Officer at Boomer Consulting speaks to Steve Perkins, Chief Information Officer at HoganTaylor about Data Governance. Marc Staut Social Media:Twitter: @CPATechGeek LinkedIn: @mstaut Steve/HoganTaylor Social Media: LinkedIn: @steveperkinscio HoganTaylor Facebook: @hogantaylorllp HoganTaylor Twitter: @hogantaylorllp HoganTaylor LinkedIn: @hogantaylor-llp HoganTaylor Instagram: @hogantaylorllp Look out for new episodes every Tuesday, involving The Boomer Advantage 5 Pillars of a Successful Firm: leadership, process, technology, talent, and growth. For more information about Boomer Consulting, visit boomer.com

Why are semantics important for a data warehouse? Lukas Schulte joins us to chat about why semantics are important, the heterogeneity of data systems, how semantics relate to SQL compilers, his project SDF, and much more.Please be aware that this discussion will get into the nitty-gritty and technical weeds of all things data. #sql #data #datawarehouse

In this episode, we're joined by our original host, Anna Shutko. Anna is now an Analytics Consultant at Supermetrics. She's using her knowledge of marketing and data to help companies solve their analytics challenges. Anna and Edward will discuss the importance of first-party data in marketing and why CMOs should invest in a marketing data warehouse. On October 25th, we're bringing together top minds in marketing and data for a global virtual event—SuperSummit. Join us for discussions on the latest trends in marketing data, including Generative AI, Big Data, and Measurement & Privacy. Sign up for free: https://supermetrics.com/supersummit

In this episode of the AI Today podcast hosts Kathleen Walch and Ron Schmelzer define the terms Data, Dataset, Big Data, DIKUW Pyramid, explain how these terms relate to AI and why it's important to know about them. Show Notes: FREE Intro to CPMAI mini course CPMAI Training and Certification AI Glossary AI Glossary Series – DevOps, Machine Learning Operations (ML Ops) AI Glossary Series – Automated Machine Learning (AutoML) AI Glossary Series – Data Preparation, Data Cleaning, Data Splitting, Data Multiplication, Data Transformation AI Glossary Series – Data Augmentation, Data Labeling, Bounding box, Sensor fusion AI Glossary Series – Data, Dataset, Big Data, DIKUW Pyramid Continue reading AI Today Podcast: AI Glossary Series – Data Warehouse, Data Lake, Extract Transform Load (ETL) at Cognilytica.

Jay White is the co-founder of Space and Time, a decentralized data warehouse. Space and Time can be understood as a web3 native database platform with applications in gaming and DeFi.  Why you should listen Space and Time is (SxT) is a decentralized data warehouse that comes preloaded with indexed blockchain data from major chains. You can join tamperproof on-chain data with your own off-chain datasets in a simple SQL format and publish the result to smart contracts, dapps, dashboards, BI tools, and ML models. Proof of SQL is a novel zk cryptography that proves that queries run in SxT are verifiably tamperproof. SxT revolutionizes data for Web3, powering sub-second queries and enterprise-scale analytics in a tamperproof and blockchain-anchored way. The development process is ongoing with an alpha launch recently completed. Space and time investors include Chainlink, Microsoft, Samsung Next, among others. Supporting links Coinsbee Space and Time Andy on Twitter  Brave New Coin on Twitter Brave New Coin   If you enjoyed the show please subscribe to the Crypto Conversation and give us a 5-star rating and a positive review in whatever podcast app you are using.  

Highlights from this week's conversation include:The need for reverse ETL in marketing (2:24)Closing the gap between engineering, data, and marketing teams (8:37)The analytics persona's opportunity (11:53)Interface layer (13:06)Approach to messy warehouse data (15:57)The need for a complicated infrastructure (28:43)Challenges in data integration for marketers (29:26)The evolution of the analytics stack (31:53)Orchestration of the data warehouse (38:39)The role of marketing tools (40:35)Generating custom assets (46:27)The shift towards making data processes easier (48:13)Final thoughts and takeaways (49:23)The Data Stack Show is a weekly podcast powered by RudderStack, the CDP for developers. Each week we'll talk to data engineers, analysts, and data scientists about their experience around building and maintaining data infrastructure, delivering data and data products, and driving better outcomes across their businesses with data.RudderStack helps businesses make the most out of their customer data while ensuring data privacy and security. To learn more about RudderStack visit rudderstack.com.

This week we had a very special guest on the podcast: Matthew Lynley, one of the founding hosts of Equity and a former TechCruncher. Since his Equity days, Lynley went off and started his very own AI-focused publication called Supervised.We brought him back on the show to ask him questions in a format where we can all learn together. Here's what we got into:From Transformers to GPT4: How attention became so critical inside of neural networks, and how transformers set the path for modern AI services.Recent acquisitions in the AI space, and what it means for the “LLM stack:” With Databricks buying MosaicML and Snowflake already busy with its own checkbook, a lot of folks are working to build out a full-stack LLM data extravaganza. We talked about what that means.Where startups sit in the current AI race: While it's great to think about the majors, we also need to know what the startup angle is. The answer? It's a little early to say, but what is clear is that startups are taking some big swings at the industry and are hellbent to snag a piece of the pie.Thanks to everyone for hanging out with us. Equity is back on Friday for our weekly news roundup!For episode transcripts and more, head to Equity's Simplecast website.Equity drops at 7 a.m. PT every Monday, Wednesday and Friday, so subscribe to us onApple Podcasts, Overcast, Spotify and all the casts. TechCrunch also has a great show on crypto, a show that interviews founders, one that details how our stories come together and more!

No episódio de hoje, Luan Moreno e Mateus Oliveira conversam com Matheus Willian, atualmente Head de Engenharia de Dados na One Way Solution.dbt é uma das tecnologias mais faladas e utilizadas fora do país, possibilitando aos times de todos os portes trabalhar com o conceito de Modern Data Stack, tornando o desenvolvimento de transformações dos dados de forma simples e com SQL.Com dbt, você tem os seguintes benefícios:Desenvolvimento de pipeline de dados usando SQL;Reutilização dos códigos usando estruturas de git;Simplificação da Stack de dados;Processamento em Modern Data Warehouses dentro outros adapters.Falamos também nesse bate-papo sobre os seguintes temas:Dados como pilar central;Dbt;Times de BI Moderno.Aprenda mais sobre dbt, como utilizar uma tecnologia para Modern Data Stack, junto com o time da One Way Solution, que mais impulsiona a comunidade, tanto com conteúdo, como com treinamentos e eventos para ajudar os profissionais de dados brasileiros em vagas de trabalho dentro e fora do país.Matheus Willian = https://www.linkedin.com/in/matheuswillian/https://www.getdbt.com/ Luan Moreno = https://www.linkedin.com/in/luanmoreno/

Highlights from this week's conversation include:Stipo and Rob's background in data (2:43)What is Sigma? (7:46)Takeaways from building analytics products in-house (9:16)Sigma's approach to datastore interface (11:32)Why analytics and BI are still not a solved problem (15:50)Combining SQL and spreadsheets for useful interface (23:17)The evolution of BI to today (29:40)Overcoming the challenges of collaboration in working with data (33:17)Creating operational coding that humans can understand (46:50)The future of BI (54:00)Cloud's impact on BI and analytics (1:00:04)The value of getting close to the data for analytics (1:02:21)Final thoughts and takeaways (1:08:45)The Data Stack Show is a weekly podcast powered by RudderStack, the CDP for developers. Each week we'll talk to data engineers, analysts, and data scientists about their experience around building and maintaining data infrastructure, delivering data and data products, and driving better outcomes across their businesses with data.RudderStack helps businesses make the most out of their customer data while ensuring data privacy and security. To learn more about RudderStack visit rudderstack.com.

A peek at the present state of marketing data warehouses, and where it might be headed, with Head of Channel Partnerships at Hightouch, Deven Ravel. SHOWPAGE: www.ninjacat.io/blog/unboxing-the-marketing-data-warehouse © 2023, NinjaCat

Tejas Manohar, Co-CEO at Hightouch, talks about the importance and benefits of data warehousing. Many marketing technology problems come down to the challenge of effectively activating data. However, many companies are starting to take a new approach to the issue by leveraging existing investments like their data warehouse to simplify the data activation process. Today, Tejas discusses the data warehouse and why it's your most powerful marketing tool. Show NotesConnect With: Tejas Manohar: Website // LinkedInThe MarTech Podcast: Email // LinkedIn // TwitterBenjamin Shapiro: Website // LinkedIn // TwitterSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Tejas Manohar, Co-CEO at Hightouch, talks about the importance and benefits of data warehousing. Many marketing technology problems come down to the challenge of effectively activating data. However, many companies are starting to take a new approach to the issue by leveraging existing investments like their data warehouse to simplify the data activation process. Today, Tejas discusses the data warehouse and why it's your most powerful marketing tool. Show NotesConnect With: Tejas Manohar: Website // LinkedInThe MarTech Podcast: Email // LinkedIn // TwitterBenjamin Shapiro: Website // LinkedIn // TwitterSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

No episódio de hoje, Luan Moreno & Mateus Oliveira entrevistaram Tobias Mao, atualmente como Co-Founder e CTO na Tobiko Data.SQLMesh é um framework desenvolvido em Python para automatizar tudo que se faça necessário para uma plataforma de dados escalável utilizando o conceito de DataOps.Com SQLMesh, você possui os seguintes benefícios:Foco nos dados do negócio, usando DataOps como premissa principal. Foco em escalabilidade sem se preocupar com seu Data Warehouse ou Engine de Query.Nosso bate papo iremos falar sobre:Estado dos Dados {State of Data}SQLMeshDataOpsPython e SQL para Engenharia de DadosTobiko DataEm todas as organizações independentemente do porte, vemos a necessidade de tornar o processo de uso dos dados mais escalável, sendo assim o SQLMesh é uma excelente opção para otimizar o processo de DataOps.Tobias MaoSQLMeshTobiko Data Luan Moreno = https://www.linkedin.com/in/luanmoreno/