Podcasts about mozart data

In this captivating episode, we dive into the remarkable story of Peter "Fish" Fishman, a resourceful entrepreneur who has harnessed the power of data to revolutionize industries and build successful ventures. From his groundbreaking work as a statistician with the Philadelphia Eagles to leadership roles at Microsoft and Yammer, Fish's relentless curiosity and passion for data have shaped his career path. We'll explore his journey through Y Combinator, founding the Direct-to-Consumer company Bacon Hot Sauce, and his current venture, Mozart Data, which is transforming the way startups approach data infrastructure. Join us as we uncover the insights, experiences, and lessons that have fueled Fish's unique entrepreneurial journey and propelled him to success in the world of data-driven business. This show is supported by Match Relevant. A company that helps venture-backed Startups find the best people available in the market, who have the skills, experience, and desire to grow. With over a decade of experience in recruitment across multiple domains, they give people career options to choose from in their career journey. Learn more about Match Relevant at matchrelevant.com

Summary Data processing technologies have dramatically improved in their sophistication and raw throughput. Unfortunately, the volumes of data that are being generated continue to double, requiring further advancements in the platform capabilities to keep up. As the sophistication increases, so does the complexity, leading to challenges for user experience. Jignesh Patel has been researching these areas for several years in his work as a professor at Carnegie Mellon University. In this episode he illuminates the landscape of problems that we are faced with and how his research is aimed at helping to solve these problems. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics. Trusted by teams of all sizes, including Comcast and Doordash, Starburst is a data lake analytics platform that delivers the adaptability and flexibility a lakehouse ecosystem promises. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst (https://www.dataengineeringpodcast.com/starburst) and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino. Your host is Tobias Macey and today I'm interviewing Jignesh Patel about the research that he is conducting on technical scalability and user experience improvements around data management Interview Introduction How did you get involved in the area of data management? Can you start by summarizing your current areas of research and the motivations behind them? What are the open questions today in technical scalability of data engines? What are the experimental methods that you are using to gain understanding in the opportunities and practical limits of those systems? As you strive to push the limits of technical capacity in data systems, how does that impact the usability of the resulting systems? When performing research and building prototypes of the projects, what is your process for incorporating user experience into the implementation of the product? What are the main sources of tension between technical scalability and user experience/ease of comprehension? What are some of the positive synergies that you have been able to realize between your teaching, research, and corporate activities? In what ways do they produce conflict, whether personally or technically? What are the most interesting, innovative, or unexpected ways that you have seen your research used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on research of the scalability limits of data systems? What is your heuristic for when a given research project needs to be terminated or productionized? What do you have planned for the future of your academic research? Contact Info Website (https://jigneshpatel.org/) LinkedIn (https://www.linkedin.com/in/jigneshmpatel/) Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ (https://www.pythonpodcast.com) covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast (https://www.themachinelearningpodcast.com) helps you go from idea to production with machine learning. Visit the site (https://www.dataengineeringpodcast.com) to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com (mailto:hosts@dataengineeringpodcast.com)) with your story. To help other people find the show please leave a review on Apple Podcasts (https://podcasts.apple.com/us/podcast/data-engineering-podcast/id1193040557) and tell your friends and co-workers Links Carnegie Mellon Universe (https://www.cmu.edu/) Parallel Databases (https://en.wikipedia.org/wiki/Parallel_database) Genomics (https://en.wikipedia.org/wiki/Genomics) Proteomics (https://en.wikipedia.org/wiki/Proteomics) Moore's Law (https://en.wikipedia.org/wiki/Moore%27s_law) Dennard Scaling (https://en.wikipedia.org/wiki/Dennard_scaling) Generative AI (https://en.wikipedia.org/wiki/Generative_artificial_intelligence) Quantum Computing (https://en.wikipedia.org/wiki/Quantum_computing) Voltron Data (https://voltrondata.com/) Podcast Episode (https://www.dataengineeringpodcast.com/voltron-data-apache-arrow-episode-346/) Von Neumann Architecture (https://en.wikipedia.org/wiki/Von_Neumann_architecture) Two's Complement (https://en.wikipedia.org/wiki/Two%27s_complement) Ottertune (https://ottertune.com/) Podcast Episode (https://www.dataengineeringpodcast.com/ottertune-database-performance-optimization-episode-197/) dbt (https://www.getdbt.com/) Informatica (https://www.informatica.com/) Mozart Data (https://mozartdata.com/) Podcast Episode (https://www.dataengineeringpodcast.com/mozart-data-modern-data-stack-episode-242/) DataChat (https://datachat.ai/) Von Neumann Bottleneck (https://www.techopedia.com/definition/14630/von-neumann-bottleneck) The intro and outro music is from The Hug (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Love_death_and_a_drunken_monkey/04_-_The_Hug) by The Freak Fandango Orchestra (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/) / CC BY-SA (http://creativecommons.org/licenses/by-sa/3.0/)

It's very easy to draw the wrong conclusions from data for any number of reasons.Maybe the dataset is wrong to start with. Maybe you've interpreted it wrong. But when you get it right, data is THE single biggest value tool you have as a company.This week's episode of Hire Learning features Peter Fishman, co-founder of Mozart Data.After working at a whole host of companies from international powerhouses like Shell, all the way down to independents with less than ten employees, Fish saw the opportunity to build a business that would make data way easier to take advantage of.Let's be honest, hiring without data is pointless, so I'm sure you can see why I was so excited to have this conversation with Fish.I cannot wait to hear your thoughts on this episode!Connect with our host, Oz Rashid, on LinkedIn: https://www.linkedin.com/in/ozrashid.Learn more about MSH: https://www.talentmsh.com.Don't forget to rate, download and subscribe to the podcast so you won't miss out on creative, innovative strategies for hiring the best talent.#Talent #Hiring #Learning #Teams #Jobs

Join Andrew, our resident Business Ninja, and Peter Fishman, CEO of Mozart Data, as they revolutionise the way you handle data. No more coding or constant maintenance—just plug in your data and let Mozart Data do the heavy lifting. Whether you're a data analyst, business owner, or curious mind, discover insights and make informed decisions effortlessly. Don't waste another minute struggling with data infrastructure. Visit our website at https://www.mozartdata.com and start your data journey now. -----Do you want to be interviewed for your business?  Schedule time with us, and we'll create a podcast like this for your business:  https://www.WriteForMe.io/-----https://www.facebook.com/writeforme.iohttps://www.instagram.com/writeforme.io/https://twitter.com/writeformeiohttps://www.linkedin.com/company/writeforme/https://www.pinterest.com/andysteuer/Want to be interviewed on our Business Ninjas podcast? Schedule time with us now, and we'll make it happen right away! Check out WriteForMe, more than just a Content Agency! See the Faces Behind The Voices on our YouTube Channel!

Summary The data ecosystem has been building momentum for several years now. As a venture capital investor Matt Turck has been trying to keep track of the main trends and has compiled his findings into the MAD (ML, AI, and Data) landscape reports each year. In this episode he shares his experiences building those reports and the perspective he has gained from the exercise. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Businesses that adapt well to change grow 3 times faster than the industry average. As your business adapts, so should your data. RudderStack Transformations lets you customize your event data in real-time with your own JavaScript or Python code. Join The RudderStack Transformation Challenge today for a chance to win a $1,000 cash prize just by submitting a Transformation to the open-source RudderStack Transformation library. Visit dataengineeringpodcast.com/rudderstack (https://www.dataengineeringpodcast.com/rudderstack) today to learn more Your host is Tobias Macey and today I'm interviewing Matt Turck about his annual report on the Machine Learning, AI, & Data landscape and the insights around data infrastructure that he has gained in the process Interview Introduction How did you get involved in the area of data management? Can you describe what the MAD landscape report is and the story behind it? At a high level, what is your goal in the compilation and maintenance of your landscape document? What are your guidelines for what to include in the landscape? As the data landscape matures, how have you seen that influence the types of projects/companies that are founded? What are the product categories that were only viable when capital was plentiful and easy to obtain? What are the product categories that you think will be swallowed by adjacent concerns, and which are likely to consolidate to remain competitive? The rapid growth and proliferation of data tools helped establish the "Modern Data Stack" as a de-facto architectural paradigm. As we move into this phase of contraction, what are your predictions for how the "Modern Data Stack" will evolve? Is there a different architectural paradigm that you see as growing to take its place? How has your presentation and the types of information that you collate in the MAD landscape evolved since you first started it?~~ What are the most interesting, innovative, or unexpected product and positioning approaches that you have seen while tracking data infrastructure as a VC and maintainer of the MAD landscape? What are the most interesting, unexpected, or challenging lessons that you have learned while working on the MAD landscape over the years? What do you have planned for future iterations of the MAD landscape? Contact Info Website (https://mattturck.com/) @mattturck (https://twitter.com/mattturck) on Twitter MAD Landscape Comments Email (mailto:mad2023@firstmarkcap.com) Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ (https://www.pythonpodcast.com) covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast (https://www.themachinelearningpodcast.com) helps you go from idea to production with machine learning. Visit the site (https://www.dataengineeringpodcast.com) to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com (mailto:hosts@dataengineeringpodcast.com)) with your story. To help other people find the show please leave a review on Apple Podcasts (https://podcasts.apple.com/us/podcast/data-engineering-podcast/id1193040557) and tell your friends and co-workers Links MAD Landscape (https://mad.firstmarkcap.com) First Mark Capital (https://firstmark.com/) Bayesian Learning (https://en.wikipedia.org/wiki/Bayesian_inference) AI Winter (https://en.wikipedia.org/wiki/AI_winter) Databricks (https://www.databricks.com/) Cloud Native Landscape (https://landscape.cncf.io/) LUMA Scape (https://lumapartners.com/lumascapes/) Hadoop Ecosystem (https://www.analyticsvidhya.com/blog/2020/10/introduction-hadoop-ecosystem/) Modern Data Stack (https://www.fivetran.com/blog/what-is-the-modern-data-stack) Reverse ETL (https://medium.com/memory-leak/reverse-etl-a-primer-4e6694dcc7fb) Generative AI (https://generativeai.net/) dbt (https://www.getdbt.com/) Transform (https://transform.co/) Podcast Episode (https://www.dataengineeringpodcast.com/transform-co-metrics-layer-episode-206/) Snowflake IPO (https://www.cnn.com/2020/09/16/investing/snowflake-ipo/index.html) Dataiku (https://www.dataiku.com/) Iceberg (https://iceberg.apache.org/) Podcast Episode (https://www.dataengineeringpodcast.com/tabular-iceberg-lakehouse-tables-episode-363) Hudi (https://hudi.apache.org/) Podcast Episode (https://www.dataengineeringpodcast.com/hudi-streaming-data-lake-episode-209/) DuckDB (https://duckdb.org/) Podcast Episode (https://www.dataengineeringpodcast.com/duckdb-in-process-olap-database-episode-270/) Trino (https://trino.io/) Y42 (https://www.y42.com/) Podcast Episode (https://www.dataengineeringpodcast.com/y42-full-stack-data-platform-episode-295) Mozart Data (https://www.mozartdata.com/) Podcast Episode (https://www.dataengineeringpodcast.com/mozart-data-modern-data-stack-episode-242/) Keboola (https://www.keboola.com/) MPP Database (https://www.techtarget.com/searchdatamanagement/definition/MPP-database-massively-parallel-processing-database) The intro and outro music is from The Hug (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Love_death_and_a_drunken_monkey/04_-_The_Hug) by The Freak Fandango Orchestra (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/) / CC BY-SA (http://creativecommons.org/licenses/by-sa/3.0/)

Mozart Data is the fastest way to set up scalable, reliable data infrastructure that doesn't need to be maintained by you. Mozart Data's all-in-one modern data platform empowers anyone to easily centralize, organize, and analyze their data without engineering resources. Connect with Peter

Today's guest is Peter Fishman, Co-Founder at Mozart Data in San Francisco. Founded in 2020, Mozart Data is the all-in-one modern data platform that gives anyone the tools to consolidate, organize, and prepare data for analysis. Mozart Data is on a mission to empower anyone to get the most out of their data without engineering or technical knowledge. Businesses of all sizes, from start-ups to Fortune 100 companies, use Mozart Data to power their data infrastructure and free up their teams to focus on the data engineering and analysis that's unique to them. Mozart Data provides a Snowflake data warehouse, ETL, transformation, and other no-code tools to consolidate, organize, and clean company data before the analysis stage. In the episode, Peter will chat about: The motivation for founding Mozart Data, Milestones and challenges they faced bringing the idea to life, What's unique about their data engineering team, Use cases of the benefits they bring to customers, Upcoming career opportunities, and Why Mozart Data is a great place to work

Mona Akmal, CEO of sales intelligence platform Falkon, is the outspoken co-founder behind an emerging leader in a hot space. Mona migrated to the United States at age 20 with a CS degree and little else. She had an impressive 12-year run as a product leader at Microsoft where she helped scale OneDrive and Office. She subsequently led product and technology organizations at places like Code.org and Amperity. Two decades later, Mona's the CEO of Falkon AI, an intelligence platform for go to market teams. Falkon recently raised $16M from a group of A-list investors that includes Greylock and Madera among others.Listen and learn...Why Mona's philosophy revolves around two words: "efficiency" and "excellence"What makes a standout sales rep great.How do find signal in noisy sales and marketing dataHow many touches are required from stage one to closing a B2B dealHow to fix the CRM data hygiene problemWhy econometrics approaches perform better than machine learning to solve the "small data problem"Why "everyone needs to be coached and nobody needs to be managed"Mona's (legendary) mental health advice to entrepreneursReferences in this episode...Barr Moses from Monte Carlo on AI and the Future of WorkDerek Steer from Mode on AI and the Future of WorkPeter Fishman from Mozart Data on AI and the Future of WorkStephen Messer from  Collective[i] on AI and the Future of Work Kamal Ahluwalia on AI and the Future of WorkLeading scientists fear AI could lead to nuclear war by the end of the century

Ahmed Elsamadisi built the data infrastructure at WeWork before realizing every company could benefit from his team's innovation. Traditional star schemas aren't the best way to manage data. Ahmed instead pioneered a new approach using a single-table column model better suited for real questions people ask. He launched Narrator in 2017 to make it easier to turn data questions into answers and has since raised $6.2M from Initialized Capital, Flybridge Capital Partners, and Y Combinator. Ahmed received his BS in Robotics from Cornell. Hear from a pioneer (and tech provocateur) how new data wrangling techniques are making it easier for mere mortals to get more value out of their data.Listen and learn…How a roboticist who got his start building self-driving cars and designing missile defense systems ended up redefining how data is storedWhy traditional approaches that require SQL to access data are brokenHow a single-column schema eliminates the complexity of joining systems and tablesWhy it's easier to tell better stories with data using temporal relationships extracted from customer journeysWhy Snowflake, Redshift, and BigQuery are really all the same… and data modeling is the place to innovate What it means to replace traditional tables with activities… and why they'll eliminate the need for specialized data analysts How to reduce data storage costs by 90% and time to generate data insights from weeks to minutes Why data management vendors are responsible for bad decisions made using your data What is data cleaning and how you should do it What is a racist algorithm Why querying data with natural language will never work Is the WeCrashed version of Adam Neumann's neuroticism accurate? Hear from someone who lived it... References in this episode:Google's LaMDA isn't sentientChandra Khatri from Got It AI on AI and the Future of Work Derek Steer from Mode on AI and the Future of Work Barr Moses from Monte Carlo on AI and the Future of Work Peter Fishman from Mozart Data on AI and the Future of Work Ahmed on Twitter 

This podcast interview focuses on product innovation that has the power to make becoming data-driven easier than ever before. My guest is Peter Fishman, Co-founder, and CEO of Mozart Data Dr. Peter Fishman has over a decade of experience running data and data-adjacent teams at companies like Microsoft, Yammer, Opendoor, Playdom, and Eaze. He realized that he was building the same types of modern data stacks at each company. Taking a broader perspective, he saw many other companies building a data stack over and over again. This inspired him, and his co-founder, Dan, to found Mozart Data in 2020.  Mozart Data is on a mission to make it easy for anyone to set up a modern data stack, without a data engineer, in under an hour. Why does this matter? Because that enables 10x more employees to get access to data, it decreases the time to insight by 76% and delivers 30% cost savings compared to assembling your own data stack.  And that inspired me, and hence I invited Peter to my podcast. We explore what's broken around the way we can embrace the full potential of data. Peter explains his vision of what can be when we can leverage the power of data as a first principle versus an afterthought. He shares his lessons learned around what a SaaS application has to excel at to overcome the trust issues customers have and create a sustainable business from the start. Here are some of his quotes:  The idea of, let's build everything and let's be good at everything. And I think like this is like, almost the kiss of death. Customers don't want good. Customers want the best. You might say, well, the customer won't know the difference between good and the best. They will know the difference.  What I think of as the way to win business is you have a small contract, and you expand with a combination of the startup and the impact that you're having. So, as you're helpful, that sort of growth within the company, ends up being sort of a no brainer.  During this interview, you will learn four things: That you can build a thriving business by working closely with your competitors  That customers want the best product in the market - whether we like it or not. The opportunity is: they define 'best' - no one else. What principles to follow to grow solid traction around adoption When you know your vision is clear and powerful enough For more information about the guest from this week: Peter Fishman Website Mozart Data Subscribe to the Daily Value Inspiration Stressed by the thought of ‘not enough' traction? Eager to know how to remove the roadblocks that slow down your entire SaaS business? Then Subscribe here It's a short daily reflection on how to shape a B2B SaaS business your customers would miss if it were gone.   Learn more about your ad choices. Visit megaphone.fm/adchoices

Peter Fishman's resume has its fair share of disparate data points. “I was once considered the world's expert in video rental,” he explains. “This is essentially the world's worst skill.” He spent a decade as Chief Bacon officer of Bacon Hot Sauce, a company he co-founded with Dan Silverman. He even describes himself as a “failed” academic, despite earning a PhD in Economics from Berkeley. Fittingly, he eventually streamlined these career data points into Mozart Data, another company he co-founded, with Silverman. Mozart Data is an all-in-one modern data platform that allows anyone to—without the need for engineering resources–organize, centralize, and analyze their data. Even outside of the data space itself, knowing the statistics of consumers and investors is crucial to launching any successful enterprise. Fishman admits that the relationships and reputation that he and Silverman formed from all of their previous years in business helped them launch their platform with unusual speed, as their first customers were investing in 30 years of accumulated trust. On this episode of Billion Dollar Tech, you'll learn the value of a data pipeline, what major change in the data space has Peter excited, and the crucial difference between customers and friends. Quotes: “B2B companies were not developing like consumer companies. They would sell to a CIO and magically whatever the CIO decided, that's what the company had to adopt. (5:54-6:07 | Peter)  “You started to see that in the software space, you started to see teams that would make decisions about what software they were using to basically make themselves move quicker.” (6:27-6:37 | Peter)  “What has happened in enterprise or B2B in the last almost two decades has been this shift from exclusively a top-down sale from somebody with a fancy title making a decision on behalf of the whole company, to a more natural, bottom-up motion. What do you prefer and vote with your credit card.” (9:05-9:31 | Peter) “It's very hard to raise money without either a product, a vision, or traction. If you have all three of them, you're in the driver's seat.” (11:48-11:57 | Peter)  “The reality is 99.99% or entrepreneurs don't end up on a rocketship to Mars. They end up toiling for many years in a really challenging environment.” (15:03-15:17 | Peter)  “We were selling a product, not ourselves as a service, that said the buyers trusted our names. This is about 30 years of trust we had built with these three customers.” (17:58-18:12 | Peter)  “So you mentioned the word data pipeline. What's a data pipeline?'(25:54-26:00 | Brendan) “You have to understand some of the nuances of your data, what's generating your data. What's the mechanism?” (35:56-36:01 | Peter)    Connect with Brendan Dell: LinkedIn: https://www.linkedin.com/in/brendandell/ YouTube: https://www.youtube.com/c/BrendanDell Instagram: @thebrendandellTikTok: @brendandell39 Buy a copy of Brendan's Book, The 12 Immutable Laws of High-Impact Messaging: https://www.indiebound.org/book/9780578210926    Connect with Peter Fishman:LinkedIn: https://www.linkedin.com/in/petefishmanv Twitter: @peterfishman https://www.mozartdata.com/ Blog: https://www.mozartdata.com/blog Check out Peter Fishman's recommended resources:Moneyball by Michael Lewis: https://www.indiebound.org/book/9780393324815 https://fivethirtyeight.com/https://www.linkedin.com/company/mozartdata Please don't forget to rate, comment, and subscribe to Billion Dollar Tech on Apple, Spotify, or wherever you listen to podcasts! Use code Brendan30 for 30% off your annual membership with RiverSide.fm  Podcast production and show notes provided by HiveCast.fm

As data volumes grow and become ever-more complex, the role of the data analyst has never been more important. At the disposal of the modern data analyst, are tools that reduce time to insight, and increase collaboration. However, as the tools of a data analyst evolve, so do the skills.  Today's guest, Peter Fishman, Co-Founder at Mozart Data, speaks to this exact notion.  Join us as we discuss: Defining a data-driven organization & main challenges Breaking down the modern data stack & what it means What makes a great data analyst How data analysts can develop deep subject matter expertise in the areas they serve Find every episode of DataFramed on Apple, Spotify, and more. Find us on our website and join the conversation on LinkedIn. Listening on a desktop and can't see the links? Just search for DataFramed in your favorite podcast player.

Mark Rittman is joined in this episode by Peter Fishman to talk about Mozart Data, the modern data stack and the need to automate the role of the analytics engineer.MozartData websiteData Bites: A Virtual Lunch and Learn with Powered by Fivetran, Snowflake and Mozart Data Launch HN: Mozart Data (YC S20) – One-stop shop for a modern data pipeline

Mark Rittman is joined in this episode by Peter Fishman to talk about Mozart Data, the modern data stack and the need to automate the role of the analytics engineer.MozartData websiteData Bites: A Virtual Lunch and Learn with Powered by Fivetran, Snowflake and Mozart Data Launch HN: Mozart Data (YC S20) – One-stop shop for a modern data pipeline

How to go from messy, siloed data to analysis-ready in several hours? In this episode, we sat down with Peter Fishman to talk about the benefits and challenges of the modern data stack. Peter is a co-founder and co-CEO of Mozart Data, an out-of-the-box data stack that provides quick and easy ways to consolidate, organize, and get data ready for analysis without the extensive engineering efforts or technical knowledge which is common today.   Cool links: BaconHotSauce MozartData

Peter Fishman the CFounder & CEO of Mozart Data shares how to build a successful data stack. Get more info at https://www.MozartData.com/

Follow Ryan here:  https://www.linkedin.com/in/ryan-w-gross/Tools to explore: Keboola: https://www.keboola.com/Thoughtspot: https://www.thoughtspot.com/Mozart Data: https://www.mozartdata.com/A good SQL course: https://www.udemy.com/course/the-complete-sql-bootcamp/From the Publisher:Subscribe on Apple: https://podcasts.apple.com/us/podcast/data-in-construction/id1604092908Subscribe on Spotify: https://open.spotify.com/show/2AUUpaT0yYueyah826JOOQ?si=a34ca4e3acf24835Sign up for the Data in Construction Book: http://eepurl.com/hTtFPHSign up for Data in Construction skills webinarsBuy The Construction Technology Handbook here: https://www.amazon.com/gp/product/B08PNHBB1M/ref=dbs_a_def_rwt_bibl_vppi_i0

On this episode of Hashmap on Tap, host Kelly Kohlleffel is joined by Peter Fishman. Fish is Co-Founder and CEO at Mozart Data, where they are helping clients go from siloed, messy data to analysis-ready data in a matter of hours. Prior to starting Mozart Data, Fish was Chief Strategy Officer at Eaze, founded the Bacon Hot Sauce Company, and spent time at Opendoor, Zenefits, and Yammer. Listen in and hear how Fish went from sports statistics to starting a hot sauce company to starting Mozart Data and some of the practical data lessons he learned along the way. Show Notes: Check out Mozart Data: https://www.mozartdata.com/ Check out Moneyball: The Art of Winning an Unfair Game: https://www.amazon.com/Moneyball-Art-Winning-Unfair-Game/dp/0393324818 On tap for today's episode: Vietnamese Coffee and a Double Espresso Contact Us: https://www.hashmapinc.com/reach-out

Luke Arrigoni started Arricor in 2012 to help large companies make sense of their data. Since then, he and the team have taught organizations like Goldman Sachs, AT&T, and Thomson Reuters about the principles of AI. His secret? Focus on the business problem and the right technology approach becomes obvious.Listen and learn...How UPS uses AI to automatically assign the right tax code for packagesWhat responsibility AI developers have for the decisions their algorithms makeHow to clean dirty data to make it ready for AI model training When to use neural nets vs. gradient-boosted treesWhich tasks are good candidates for classifier models vs. NLPWhich job skills are future-proof... and which are likely to be replaced by automation References in this episode:Fish from Mozart Data on AI and the Future of WorkAirflow for data pipeline automation

Peter Fishman ("Fish"), co-founder and CEO of Mozart Data, had a vision for making it easy for any business to unlock the value of their data via a modern data stack. He and his co-founder believe rote data engineering work shouldn't require teams of in-house data engineers. Fish turned his PhD in Economics and passion for statistics into a successful, venture-backed YC company that is defining the future of data analytics.Listen and learn...Why Fish believes "not every business gets value out of their data... but every business can."The role of data pipelines in automating the cleaning and transforming of data.Fish's prediction for where humans will be needed for data analysis in a decade.What Fish learned working with David Sacks at Yammer.How bacon hot sauce inspired the founding of Mozart Data.References in this episode:Barr Moses from Monte Carlo  on AI and the Future of WorkDerek Steer from Mode on AI and the Future of WorkFivetran for simplifying data integration

Our guest today is Peter Fishman, who is the CEO and co-founder of Mozart Data, which is the fastest and easiest way to get the data stack to consolidate, organize, and clean your data, so it's ready for analysis.

Dan Silberman and Bob Haffner discuss the traditional approach of Extract, Transform and Load (ETL) vs Extract, Load and Transform (ELT). What each one means, why has ELT become so popular and how ELT will evolve. #data #dataengineering #elt #etl #analysticsengineering Check out Mozart Data https://www.mozartdata.com/ Connect with Bob Twitter - @bobhaffner LinkedIn - linkedin.com/in/bobhaffner

Are you eager to learn more about Modern Data Stack? In this episode Peter Fishman, CEO and Founder at Mozart Data, talks about his journey, modern data stack, analytics, pitfalls of a data analytics team and power of data! #data #datascience #python #machinelearning #analytics #moderndatastack #ai #bi #artificialintelligence

The modern data stack has been gaining a lot of attention recently with a rapidly growing set of managed services for different stages of the data lifecycle. With all of the available options it is possible to run a scalable, production grade data platform with a small team, but there are still sharp edges and integration challenges to work through. Peter Fishman and Dan Silberman experienced these difficulties firsthand and created Mozart Data to provide a single, easy to use option for getting started with the modern data stack. In this episode they explain how they designed a user experience to make working with data more accessibly by organizations without a data team, while allowing for more advanced users to build out more complex workflows. They also share their thoughts on the modern data ecosystem and how it improves the availability of analytics for companies of all sizes.

Modern companies leverage dozens or even hundreds of software solutions to solve specific needs of the business.  Organizations need to collect all these disparate data sources into a data warehouse in order to add value.  The raw data typically needs transformation before it can be analyzed.  In many cases, companies develop homegrown solutions, thus reinventing The post Modern Data Stacks Optimized by Mozart Data with Peter Fishman and Dan Silberman appeared first on Software Engineering Daily.

Modern companies leverage dozens or even hundreds of software solutions to solve specific needs of the business.  Organizations need to collect all these disparate data sources into a data warehouse in order to add value.  The raw data typically needs transformation before it can be analyzed.  In many cases, companies develop homegrown solutions, thus reinventing

Modern companies leverage dozens or even hundreds of software solutions to solve specific needs of the business.  Organizations need to collect all these disparate data sources into a data warehouse in order to add value.  The raw data typically needs transformation before it can be analyzed.  In many cases, companies develop homegrown solutions, thus reinventing The post Modern Data Stacks Optimized by Mozart Data with Peter Fishman and Dan Silberman appeared first on Software Engineering Daily.

Modern companies leverage dozens or even hundreds of software solutions to solve specific needs of the business.  Organizations need to collect all these disparate data sources into a data warehouse in order to add value.  The raw data typically needs transformation before it can be analyzed.  In many cases, companies develop homegrown solutions, thus reinventing The post Modern Data Stacks Optimized by Mozart Data with Peter Fishman and Dan Silberman appeared first on Software Engineering Daily.

Mozart Data specializes in data stack, SaaS, data-visualization and engineering solutions for companies. In this episode Peter Fishman talks to Ben Jackson at VenorTech ti discuss his growth story. Make sure to drop your comments in the chat below and enjoy the episode! #SaaS #Startups #Startuplife #EnterpriseSoftware

Mozart Data founders Peter Fishman and Dan Silberman have been friends for over 20 years, working at various startups, and even launching a hot sauce company together along the way. As technologists, they saw companies building a data stack over and over. They decided to provide one for them and Mozart Data was born. The […]