Starburst

Summary In this episode of the Data Engineering Podcast Alex Albu, tech lead for AI initiatives at Starburst, talks about integrating AI workloads with the lakehouse architecture. From his software engineering roots to leading data engineering efforts, Alex shares insights on enhancing Starburst's platform to support AI applications, including an AI agent for data exploration and using AI for metadata enrichment and workload optimization. He discusses the challenges of integrating AI with data systems, innovations like SQL functions for AI tasks and vector databases, and the limitations of traditional architectures in handling AI workloads. Alex also shares his vision for the future of Starburst, including support for new data formats and AI-driven data exploration tools. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Data migrations are brutal. 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This episode is brought to you by Coresignal, your go-to source for high-quality public web data to power best-in-class AI products. Instead of spending time collecting, cleaning, and enriching data in-house, use ready-made multi-source B2B data that can be smoothly integrated into your systems via APIs or as datasets. With over 3 billion data records from 15+ online sources, Coresignal delivers high-quality data on companies, employees, and jobs. It is powering decision-making for more than 700 companies across AI, investment, HR tech, sales tech, and market intelligence industries. A founding member of the Ethical Web Data Collection Initiative, Coresignal stands out not only for its data quality but also for its commitment to responsible data collection practices. Recognized as the top data provider by Datarade for two consecutive years, Coresignal is the go-to partner for those who need fresh, accurate, and ethically sourced B2B data at scale. Discover how Coresignal's data can enhance your AI platforms. Visit dataengineeringpodcast.com/coresignal to start your free 14-day trial. Your host is Tobias Macey and today I'm interviewing Alex Albu about how Starburst is extending the lakehouse to support AI workloads Interview Introduction How did you get involved in the area of data management? Can you start by outlining the interaction points of AI with the types of data workflows that you are supporting with Starburst? What are some of the limitations of warehouse and lakehouse systems when it comes to supporting AI systems? What are the points of friction for engineers who are trying to employ LLMs in the work of maintaining a lakehouse environment? Methods such as tool use (exemplified by MCP) are a means of bolting on AI models to systems like Trino. What are some of the ways that is insufficient or cumbersome? Can you describe the technical implementation of the AI-oriented features that you have incorporated into the Starburst platform?What are the foundational architectural modifications that you had to make to enable those capabilities? For the vector storage and indexing, what modifications did you have to make to iceberg?What was your reasoning for not using a format like Lance? For teams who are using Starburst and your new AI features, what are some examples of the workflows that they can expect? What new capabilities are enabled by virtue of embedding AI features into the interface to the lakehouse? What are the most interesting, innovative, or unexpected ways that you have seen Starburst AI features used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on AI features for Starburst? When is Starburst/lakehouse the wrong choice for a given AI use case? What do you have planned for the future of AI on Starburst? Contact Info LinkedIn 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__ covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems. Visit the site 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 with your story. Links StarburstPodcast Episode AWS Athena MCP == Model Context Protocol LLM Tool Use Vector Embeddings RAG == Retrieval Augmented GenerationAI Engineering Podcast Episode Starburst Data Products Lance LanceDB Parquet ORC pgvector Starburst Icehouse The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Alex Albu

Tech Lead for AI Initiatives

Starburst

Summary Artificial intelligence technologies promise to revolutionize business and produce new sources of value. In order to make those promises a reality there is a substantial amount of strategy and investment required. Colleen Tartow has worked across all stages of the data lifecycle, and in this episode she shares her hard-earned wisdom about how to conduct an AI program for your organization. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Dagster offers a new approach to building and running data platforms and data pipelines. It is an open-source, cloud-native orchestrator for the whole development lifecycle, with integrated lineage and observability, a declarative programming model, and best-in-class testability. Your team can get up and running in minutes thanks to Dagster Cloud, an enterprise-class hosted solution that offers serverless and hybrid deployments, enhanced security, and on-demand ephemeral test deployments. Go to dataengineeringpodcast.com/dagster today to get started. Your first 30 days are free! 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 and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino. Join us at the top event for the global data community, Data Council Austin. From March 26-28th 2024, we'll play host to hundreds of attendees, 100 top speakers and dozens of startups that are advancing data science, engineering and AI. Data Council attendees are amazing founders, data scientists, lead engineers, CTOs, heads of data, investors and community organizers who are all working together to build the future of data and sharing their insights and learnings through deeply technical talks. As a listener to the Data Engineering Podcast you can get a special discount off regular priced and late bird tickets by using the promo code dataengpod20. Don't miss out on our only event this year! Visit dataengineeringpodcast.com/data-council and use code dataengpod20 to register today! Your host is Tobias Macey and today I'm interviewing Colleen Tartow about the questions to answer before and during the development of an AI program Interview Introduction How did you get involved in the area of data management? When you say "AI Program", what are the organizational, technical, and strategic elements that it encompasses? How does the idea of an "AI Program" differ from an "AI Product"? What are some of the signals to watch for that indicate an objective for which AI is not a reasonable solution? Who needs to be involved in the process of defining and developing that program? What are the skills and systems that need to be in place to effectively execute on an AI program? "AI" has grown to be an even more overloaded term than it already was. What are some of the useful clarifying/scoping questions to address when deciding the path to deployment for different definitions of "AI"? Organizations can easily fall into the trap of green-lighting an AI project before they have done the work of ensuring they have the necessary data and the ability to process it. What are the steps to take to build confidence in the availability of the data? Even if you are sure that you can get the data, what are the implementation pitfalls that teams should be wary of while building out the data flows for powering the AI system? What are the key considerations for powering AI applications that are substantially different from analytical applications? The ecosystem for ML/AI is a rapidly moving target. What are the foundational/fundamental principles that you need to design around to allow for future flexibility? What are the most interesting, innovative, or unexpected ways that you have seen AI programs implemented? What are the most interesting, unexpected, or challenging lessons that you have learned while working on powering AI systems? When is AI the wrong choice? What do you have planned for the future of your work at VAST Data? Contact Info LinkedIn 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__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning. Visit the site 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) with your story. Links VAST Data Colleen's Previous Appearance Linear Regression CoreWeave Lambda Labs MAD Landscape Podcast Episode ML Episode The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Sponsored By: Dagster:  Data teams are tasked with helping organizations deliver on the premise of data, and with ML and AI maturing rapidly, expectations have never been this high. However data engineers are challenged by both technical complexity and organizational complexity, with heterogeneous technologies to adopt, multiple data disciplines converging, legacy systems to support, and costs to manage. Dagster is an open-source orchestration solution that helps data teams reign in this complexity and build data platforms that provide unparalleled observability, and testability, all while fostering collaboration across the enterprise. With enterprise-grade hosting on Dagster Cloud, you gain even more capabilities, adding cost management, security, and CI support to further boost your teams' productivity. Go to [dagster.io](https://dagster.io/lp/dagster-cloud-trial?source=data-eng-podcast) today to get your first 30 days free! Data Council:  Join us at the top event for the global data community, Data Council Austin. From March 26-28th 2024, we'll play host to hundreds of attendees, 100 top speakers and dozens of startups that are advancing data science, engineering and AI. Data Council attendees are amazing founders, data scientists, lead engineers, CTOs, heads of data, investors and community organizers who are all working together to build the future of data and sharing their insights and learnings through deeply technical talks. As a listener to the Data Engineering Podcast you can get a special discount off regular priced and late bird tickets by using the promo code dataengpod20. Don't miss out on our only event this year! Visit [dataengineeringpodcast.com/data-council](https://www.dataengineeringpodcast.com/data-council) and use code **dataengpod20** to register today! Promo Code: dataengpod20 Starburst:  This episode is brought to you by Starburst - a data lake analytics platform for data engineers who are battling to build and scale high quality data pipelines on the data lake. Powered by Trino, Starburst runs petabyte-scale SQL analytics fast at a fraction of the cost of traditional methods, helping you meet all your data needs ranging from AI/ML workloads to data applications to complete analytics. Trusted by the teams at Comcast and Doordash, Starburst delivers the adaptability and flexibility a lakehouse ecosystem promises, while providing a single point of access for your data and all your data governance allowing you to discover, transform, govern, and secure all in one place. 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? Try Starburst Galaxy today, the easiest and fastest way to get started using Trino, and get $500 of credits free. [dataengineeringpodcast.com/starburst](https://www.dataengineeringpodcast.com/starburst) Support Data Engineering Podcast

Colleen Tartow

StarburstStarburst Data

Summary A data lakehouse is intended to combine the benefits of data lakes (cost effective, scalable storage and compute) and data warehouses (user friendly SQL interface). Multiple open source projects and vendors have been working together to make this vision a reality. In this episode Dain Sundstrom, CTO of Starburst, explains how the combination of the Trino query engine and the Iceberg table format offer the ease of use and execution speed of data warehouses with the infinite storage and scalability of data lakes. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Dagster offers a new approach to building and running data platforms and data pipelines. It is an open-source, cloud-native orchestrator for the whole development lifecycle, with integrated lineage and observability, a declarative programming model, and best-in-class testability. Your team can get up and running in minutes thanks to Dagster Cloud, an enterprise-class hosted solution that offers serverless and hybrid deployments, enhanced security, and on-demand ephemeral test deployments. Go to dataengineeringpodcast.com/dagster today to get started. Your first 30 days are free! 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 and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino. Join in with the event for the global data community, Data Council Austin. From March 26th-28th 2024, they'll play host to hundreds of attendees, 100 top speakers, and dozens of startups that are advancing data science, engineering and AI. Data Council attendees are amazing founders, data scientists, lead engineers, CTOs, heads of data, investors and community organizers who are all working togethr to build the future of data. As a listener to the Data Engineering Podcast you can get a special discount of 20% off your ticket by using the promo code dataengpod20. Don't miss out on their only event this year! Visit: dataengineeringpodcast.com/data-council today. Your host is Tobias Macey and today I'm interviewing Dain Sundstrom about building a data lakehouse with Trino and Iceberg Interview Introduction How did you get involved in the area of data management? To start, can you share your definition of what constitutes a "Data Lakehouse"? What are the technical/architectural/UX challenges that have hindered the progression of lakehouses? What are the notable advancements in recent months/years that make them a more viable platform choice? There are multiple tools and vendors that have adopted the "data lakehouse" terminology. What are the benefits offered by the combination of Trino and Iceberg? What are the key points of comparison for that combination in relation to other possible selections? What are the pain points that are still prevalent in lakehouse architectures as compared to warehouse or vertically integrated systems? What progress is being made (within or across the ecosystem) to address those sharp edges? For someone who is interested in building a data lakehouse with Trino and Iceberg, how does that influence their selection of other platform elements? What are the differences in terms of pipeline design/access and usage patterns when using a Trino/Iceberg lakehouse as compared to other popular warehouse/lakehouse structures? What are the most interesting, innovative, or unexpected ways that you have seen Trino lakehouses used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on the data lakehouse ecosystem? When is a lakehouse the wrong choice? What do you have planned for the future of Trino/Starburst? Contact Info LinkedIn dain on GitHub 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__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning. Visit the site 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) with your story. Links Trino Starburst Presto JBoss Java EE HDFS S3 GCS == Google Cloud Storage Hive Hive ACID Apache Ranger OPA == Open Policy Agent Oso AWS Lakeformation Tabular Iceberg Podcast Episode Delta Lake Podcast Episode Debezium Podcast Episode Materialized View Clickhouse Druid Hudi Podcast Episode The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Sponsored By: Data Council:  Join us at the top event for the global data community, Data Council Austin. From March 26-28th 2024, we'll play host to hundreds of attendees, 100 top speakers and dozens of startups that are advancing data science, engineering and AI. Data Council attendees are amazing founders, data scientists, lead engineers, CTOs, heads of data, investors and community organizers who are all working together to build the future of data and sharing their insights and learnings through deeply technical talks. As a listener to the Data Engineering Podcast you can get a special discount off regular priced and late bird tickets by using the promo code dataengpod20. Don't miss out on our only event this year! Visit [dataengineeringpodcast.com/data-council](https://www.dataengineeringpodcast.com/data-council) and use code **dataengpod20** to register today! Promo Code: dataengpod20 Starburst:  This episode is brought to you by Starburst - a data lake analytics platform for data engineers who are battling to build and scale high quality data pipelines on the data lake. Powered by Trino, Starburst runs petabyte-scale SQL analytics fast at a fraction of the cost of traditional methods, helping you meet all your data needs ranging from AI/ML workloads to data applications to complete analytics. Trusted by the teams at Comcast and Doordash, Starburst delivers the adaptability and flexibility a lakehouse ecosystem promises, while providing a single point of access for your data and all your data governance allowing you to discover, transform, govern, and secure all in one place. 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? Try Starburst Galaxy today, the easiest and fastest way to get started using Trino, and get $500 of credits free. [dataengineeringpodcast.com/starburst](https://www.dataengineeringpodcast.com/starburst) Dagster:  Data teams are tasked with helping organizations deliver on the premise of data, and with ML and AI maturing rapidly, expectations have never been this high. However data engineers are challenged by both technical complexity and organizational complexity, with heterogeneous technologies to adopt, multiple data disciplines converging, legacy systems to support, and costs to manage. Dagster is an open-source orchestration solution that helps data teams reign in this complexity and build data platforms that provide unparalleled observability, and testability, all while fostering collaboration across the enterprise. With enterprise-grade hosting on Dagster Cloud, you gain even more capabilities, adding cost management, security, and CI support to further boost your teams' productivity. Go to [dagster.io](https://dagster.io/lp/dagster-cloud-trial?source=data-eng-podcast) today to get your first 30 days free! Support Data Engineering Podcast

Dain Sundstrom

CTO

Starburst

Guest: Javier Molina, CRO of Starburst Starburst CRO Javier Molina’s peers, former colleagues, and even his wife often tell him the same thing: He’s difficult to read.  That doesn’t mean he’s not listening, though. In fact, he’s focusing on many different things such as speech patterns, the words being used, and the priority of those words while simultaneously keeping a pulse on social cues as well. This uncontrolled habit he describes as both a superpower and his achilles heel. “It allows me to interview really well and assess talent,” says Javier, who describes himself as a social introvert. “It allows me to read situations … understand room dynamics… It helps me understand my customers [but] I think a lot of people like extroverts because of how they’re so expressive and flashy ... and that’s not me.” In this episode, Javier and Joubin discuss Austin culture, making eye contact, social introverts, living in the future, self-awareness, betting on yourself, workhorse culture, reverse job interviews, short-term wins, in-car WiFi, great partners, and world-class interviewing. In this episode, we cover: San Francisco vs. Austin and the flood of techies moving to Texas (01:08) The “movie that you can’t turn off” and assessing people quickly (05:49) Patience, focus, and being present (14:10) “What is a common misconception of you?” (19:53) Self-awareness as a proxy for potential, and feeling different from the crowd (25:04) Buying houses, and betting on yourself (32:00) Being hired as an executive, and the culture of teams at bootstrapped companies (39:00) What Starburst does and turning the tables on CEO Justin Borgman (45:44) Being intentional, celebrating wins, and “enjoying the climb” (50:31) Getting away from work, and the strength of entrepreneurs’ relationships (57:22) The little things in interviews, and why “a problem well stated is half solved” (01:02:50) How to screen for grit (01:07:41) Links: Connect with JavierTwitter LinkedIn Connect with JoubinTwitter LinkedIn Email: grit@kleinerperkins.com  Learn more about Kleiner Perkins This episode was edited by Eric Johnson from LightningPod.fm

Javier Molina

VP of Corporate Sales, Americas

MongoDBStarburst

Summary With all of the messaging about treating data as a product it is becoming difficult to know what that even means. Vishal Singh is the head of products at Starburst which means that he has to spend all of his time thinking and talking about the details of product thinking and its application to data. In this episode he shares his thoughts on the strategic and tactical elements of moving your work as a data professional from being task-oriented to being product-oriented and the long term improvements in your productivity that it provides. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management When you're ready to build your next pipeline, or want to test out the projects you hear about on the show, you'll need somewhere to deploy it, so check out our friends at Linode. With their new managed database service you can launch a production ready MySQL, Postgres, or MongoDB cluster in minutes, with automated backups, 40 Gbps connections from your application hosts, and high throughput SSDs. Go to dataengineeringpodcast.com/linode today and get a $100 credit to launch a database, create a Kubernetes cluster, or take advantage of all of their other services. And don't forget to thank them for their continued support of this show! Modern data teams are dealing with a lot of complexity in their data pipelines and analytical code. Monitoring data quality, tracing incidents, and testing changes can be daunting and often takes hours to days or even weeks. By the time errors have made their way into production, it’s often too late and damage is done. Datafold built automated regression testing to help data and analytics engineers deal with data quality in their pull requests. Datafold shows how a change in SQL code affects your data, both on a statistical level and down to individual rows and values before it gets merged to production. No more shipping and praying, you can now know exactly what will change in your database! Datafold integrates with all major data warehouses as well as frameworks such as Airflow & dbt and seamlessly plugs into CI workflows. Visit dataengineeringpodcast.com/datafold today to book a demo with Datafold. RudderStack helps you build a customer data platform on your warehouse or data lake. Instead of trapping data in a black box, they enable you to easily collect customer data from the entire stack and build an identity graph on your warehouse, giving you full visibility and control. Their SDKs make event streaming from any app or website easy, and their extensive library of integrations enable you to automatically send data to hundreds of downstream tools. Sign up free at dataengineeringpodcast.com/rudder Build Data Pipelines. Not DAGs. That’s the spirit behind Upsolver SQLake, a new self-service data pipeline platform that lets you build batch and streaming pipelines without falling into the black hole of DAG-based orchestration. All you do is write a query in SQL to declare your transformation, and SQLake will turn it into a continuous pipeline that scales to petabytes and delivers up to the minute fresh data. SQLake supports a broad set of transformations, including high-cardinality joins, aggregations, upserts and window operations. Output data can be streamed into a data lake for query engines like Presto, Trino or Spark SQL, a data warehouse like Snowflake or Redshift., or any other destination you choose. Pricing for SQLake is simple. You pay $99 per terabyte ingested into your data lake using SQLake, and run unlimited transformation pipelines for free. That way data engineers and data users can process to their heart’s content without worrying about their cloud bill. For data engineering podcast listeners, we’re offering a 30 day trial with unlimited data, so go to dataengineeringpodcast.com/upsolver today and see for yourself how to avoid DAG hell. Your host is Tobias Macey and today I'm interviewing Vishal Singh about his experience building data products at Starburst Interview Introduction How did you get involved in the area of data management? Can you describe what your definition of a "data product" is? What are some of the different contexts in which the idea of a data product is applicable? How do the parameters of a data product change across those different contexts/consumers? What are some of the ways that you see the conversation around the purpose and practice of building data products getting overloaded by conflicting objectives? What do you see as common challenges in data teams around how to approach product thinking in their day-to-day work? What are some of the tactical ways that product-oriented work on data problems differs from what has become common practice in data teams? What are some of the features that you are building at Starburst that contribute to the efforts of data teams to build full-featured product experiences for their data? What are the most interesting, innovative, or unexpected ways that you have seen Starburst used in the context of data products? What are the most interesting, unexpected, or challenging lessons that you have learned while working at Starburst? When is a data product the wrong choice? What do you have planned for the future of support for data product development at Starburst? Contact Info LinkedIn @vishal_singh on Twitter 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__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning. Visit the site 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) with your story. To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers Links Starburst Podcast Episode Geophysics Product-Led Growth Trino DataNova Starburst Galaxy Tableau PowerBI Podcast Episode Metabase Podcast Episode Great Expectations Podcast Episode The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Sponsored By: Rudderstack:  RudderStack provides all your customer data pipelines in one platform. You can collect, transform, and route data across your entire stack with its event streaming, ETL, and reverse ETL pipelines. RudderStack’s warehouse-first approach means it does not store sensitive information, and it allows you to leverage your existing data warehouse/data lake infrastructure to build a single source of truth for every team. RudderStack also supports real-time use cases. You can Implement RudderStack SDKs once, then automatically send events to your warehouse and 150+ business tools, and you’ll never have to worry about API changes again. Visit [dataengineeringpodcast.com/rudderstack](https://www.dataengineeringpodcast.com/rudderstack) to sign up for free today, and snag a free T-Shirt just for being a Data Engineering Podcast listener. Upsolver:  Build Real-Time Pipelines. Not Endless DAGs! Creating real-time ETL pipelines is extremely time-consuming and engineering intensive. Why? Because when we attempt to shoehorn a 30-year old batch process into a real-time pipeline, we create an orchestration hell that makes every pipeline a data engineering project. Every pipeline is composed of transformation logic (the what) and orchestration (the how). If you run daily batches, orchestration is simple and there’s plenty of time to recover from failures. However, real-time pipelines with per-hour or per-minute batches make orchestration intricate and data engineers find themselves burdened with building Direct Acyclic Graphs (DAGs), in tools like Apache Airflow, with 10s to 100s of steps intended to address all success and failure modes, task dependencies and maintain temporary data copies. Ori Rafael, CEO and co-founder of Upsolver, will unpack this problem that bottlenecks real-time analytics delivery, and describe a new approach that completely eliminates the need for orchestration, so you can remove Airflow from your development critical path and deliver reliable production pipelines quickly. Go to [dataengineeringpodcast.com/upsolver](dataengineeringpodcast.com/upsolver) to start your 30 day trial with unlimited data, and see for yourself how to avoid DAG hell. Datafold:  Datafold helps you deal with data quality in your pull request. It provides automated regression testing throughout your schema and pipelines so you can address quality issues before they affect production. No more shipping and praying, you can now know exactly what will change in your database ahead of time. Datafold integrates with all major data warehouses as well as frameworks such as Airflow & dbt and seamlessly plugs into CI, so in a few minutes you can get from 0 to automated testing of your analytical code. Visit our site at [dataengineeringpodcast.com/datafold](https://www.dataengineeringpodcast.com/datafold) today to book a demo with Datafold. Linode:  Your data platform needs to be scalable, fault tolerant, and performant, which means that you need the same from your cloud provider. Linode has been powering production systems for over 17 years, and now they’ve launched a fully managed Kubernetes platform. With the combined power of the Kubernetes engine for flexible and scalable deployments, and features like dedicated CPU instances, GPU instances, and object storage you’ve got everything you need to build a bulletproof data pipeline. If you go to: [dataengineeringpodcast.com/linode](https://www.dataengineeringpodcast.com/linode) today you’ll even get a $100 credit to use on building your own cluster, or object storage, or reliable backups, or… And while you’re there don’t forget to thank them for being a long-time supporter of the Data Engineering Podcast! Support Data Engineering Podcast

Vishal Singh

Head of Products

Starburst

Summary The ecosystem for data tools has been going through rapid and constant evolution over the past several years. These technological shifts have brought about corresponding changes in data and platform architectures for managing data and analytical workflows. In this episode Colleen Tartow shares her insights into the motivating factors and benefits of the most prominent patterns that are in the popular narrative; data mesh and the modern data stack. She also discusses her views on the role of the data lakehouse as a building block for these architectures and the ongoing influence that it will have as the technology matures. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their new managed database service you can launch a production ready MySQL, Postgres, or MongoDB cluster in minutes, with automated backups, 40 Gbps connections from your application hosts, and high throughput SSDs. Go to dataengineeringpodcast.com/linode today and get a $100 credit to launch a database, create a Kubernetes cluster, or take advantage of all of their other services. And don’t forget to thank them for their continued support of this show! Atlan is the metadata hub for your data ecosystem. Instead of locking your metadata into a new silo, unleash its transformative potential with Atlan’s active metadata capabilities. Push information about data freshness and quality to your business intelligence, automatically scale up and down your warehouse based on usage patterns, and let the bots answer those questions in Slack so that the humans can focus on delivering real value. Go to dataengineeringpodcast.com/atlan today to learn more about how Atlan’s active metadata platform is helping pioneering data teams like Postman, Plaid, WeWork & Unilever achieve extraordinary things with metadata and escape the chaos. Modern data teams are dealing with a lot of complexity in their data pipelines and analytical code. Monitoring data quality, tracing incidents, and testing changes can be daunting and often takes hours to days or even weeks. By the time errors have made their way into production, it’s often too late and damage is done. Datafold built automated regression testing to help data and analytics engineers deal with data quality in their pull requests. Datafold shows how a change in SQL code affects your data, both on a statistical level and down to individual rows and values before it gets merged to production. No more shipping and praying, you can now know exactly what will change in your database! Datafold integrates with all major data warehouses as well as frameworks such as Airflow & dbt and seamlessly plugs into CI workflows. Visit dataengineeringpodcast.com/datafold today to book a demo with Datafold. Tired of deploying bad data? Need to automate data pipelines with less red tape? Shipyard is the premier data orchestration platform built to help your data team quickly launch, monitor, and share workflows in a matter of minutes. Build powerful workflows that connect your entire data stack end-to-end with a mix of your code and their open-source, low-code templates. Once launched, Shipyard makes data observability easy with logging, alerting, and retries that will catch errors before your business team does. So whether you’re ingesting data from an API, transforming it with dbt, updating BI tools, or sending data alerts, Shipyard centralizes these operations and handles the heavy lifting so your data team can finally focus on what they’re good at — solving problems with data. Go to dataengineeringpodcast.com/shipyard to get started automating with their free developer plan today! Your host is Tobias Macey and today I’m interviewing Colleen Tartow about her views on the forces shaping the current generation of data architectures Interview Introduction How did you get involved in the area of data management? In your opinion as an astrophysicist, how well does the metaphor of a starburst map to your current work at the company of the same name? Can you describe what you see as the dominant factors that influence a team’s approach to data architecture and design? Two of the most repeated (often mis-attributed) terms in the data ecosystem for the past couple of years are the "modern data stack" and the "data mesh". As someone who is working at a company that can be construed to provide solutions for either/both of those patterns, what are your thoughts on their lasting strength and long-term viability? What do you see as the strengths of the emerging lakehouse architecture in the context of the "modern data stack"? What are the factors that have prevented it from being a default choice compared to cloud data warehouses? (e.g. BigQuery, Redshift, Snowflake, Firebolt, etc.) What are the recent developments that are contributing to its current growth? What are the weak points/sharp edges that still need to be addressed? (both internal to the platforms and in the external ecosystem/integrations) What are some of the implementation challenges that teams often experience when trying to adopt a lakehouse strategy as the core building block of their data systems? What are some of the exercises that they should be performing to help determine their technical and organizational capacity to support that strategy over the long term? One of the core requirements for a data mesh implementation is to have a common system that allows for product teams to easily build their solutions on top of. How do lakehouse/data virtualization systems allow for that? What are some of the lessons that need to be shared with engineers to help them make effective use of these technologies when building their own data products? What are some of the supporting services that are helpful in these undertakings? What do you see as the forces that will have the most influence on the trajectory of data architectures over the next 2 – 5 years? What are the most interesting, innovative, or unexpected ways that you have seen lakehouse architectures used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on the Starburst product? When is a lakehouse the wrong choice? What do you have planned for the future of Starburst’s technology platform? Contact Info LinkedIn @ctartow on Twitter 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__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning. Visit the site 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) with your story. To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers Links Starburst Trino Teradata Cognos Data Lakehouse Data Virtualization Iceberg Podcast Episode Hudi Podcast Episode Delta Podcast Episode Snowflake Podcast Episode AWS Lake Formation Clickhouse Podcast Episode Druid Pinot Podcast Episode Starburst Galaxy Varada The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Colleen Tartow

StarburstStarburst Data

In this episode, Sahir Azam, Chief Product Officer at MongoDB, and Javier Molina, SVP at MongoDB, share their journey to increasing company revenue through their cohesive sales and product relationship. When sales and product work together, amazing things happen. Video and blog post: https://www.saastr.com/how-sales-and-product-really-should-work-together-with-javier-molina-vp-corporate-sales-americas-mongodb-and-sahir-azam-chief-product-officer-mongodb/

Javier Molina

VP of Corporate Sales, Americas

MongoDBStarburst

Sahir Azam

Chief Product Officer

MongoDB