Nisha Paliwal, who leads enterprise data tech at Capital One, joins Tristan to discuss building a strong data culture for in the world of AI. She is the co-author of the book Secrets of AI Value Creation.  For full show notes and to read 6+ years of back issues of the podcast's companion newsletter, head to https://roundup.getdbt.com. The Analytics Engineering Podcast is sponsored by dbt Labs.

Dr. Eirini Kalliamvakou is a senior researcher at GitHub Next. Eirini has built a career on studying software engineers, how to measure their productivity, how developer experience impacts productivity, and more. Recently, Eirini has been working on quantifying the impacts of GitHub Copilot. Does it actually help software engineers be more productive? Tristan and Eirini explore how to quantify developer productivity in the first place, and finally, arriving at whether or not Copilot‌ makes a difference. In the search for real business value, this research is a real bellwether of things to come. For full show notes and to read 6+ years of back issues of the podcast's companion newsletter, head to https://roundup.getdbt.com. The Analytics Engineering Podcast is sponsored by dbt Labs. Join data practitioners and data leaders this October in Las Vegas at Coalesce, the analytics engineering conference hosted by dbt Labs. Register now at coalesece.getdbt.com. Listeners of this show can use the code podcast20 for a 20% discount.

The Data Product Management In Action podcast, brought to you by Soda and executive producer Scott Hirleman, is a platform for data product management practitioners to share insights and experiences. We've released a special edition series of minisodes of our podcast. Recorded live at Data Connect 2024, our host Michael Toland engages in short, sweet, informative, and delightful conversations with five prevelant practitioners who are forging their way forward in data and technology. In this minisode, Michael reconnects with his former colleague Lindsay Murphy as she delves into a crucial yet often overlooked aspect of data management—cost containment. Lindsay's session at Data Connect 2024 emphasizes the importance of considering costs as a critical piece of your data team's ROI. While data teams often focus on value creation and return on investment, they can easily lose sight of the expenses associated with the complex stacks they build. Lindsay offers practical insights on how to strike a balance between innovation and cost-efficiency. Plus, a special shout-out to Lindsay's new podcast, Women Lead Data—hurrah! This podcast is set to inspire and empower women in the data industry, providing a platform for sharing experiences, insights, and strategies for success. About our host Michael Toland: Michael is a Product Management Coach and Consultant with Pathfinder Product, a Test Double Operation. Since 2016, Michael has worked on large-scale system modernizations and migration initiatives at Verizon. Outside his professional career, Michael serves as the Treasurer for the New Leaders Council, mentors with Venture for America, sings with the Columbus Symphony, and writes satire for his blog Dignified Product. He is excited to discuss data product management with the podcast audience. Connect with Michael on LinkedIn About our guest Lindsay Murphy: Lindsay is a data leader with 13 years of experience in building and scaling data teams. She has successfully launched and led data initiatives at startups such as BenchSci, Maple, and Secoda. Her expertise includes developing internal data products, implementing modern data stack infrastructures, building and mentoring data engineering teams, and crafting data strategies that align with organizational goals. An active member of the data community, Lindsay organizes the Toronto Modern Data Stack Meetup group, which boasts over 2,500 members. She has also taught Advanced dbt to more than 100 students through Uplimit and hosts a weekly podcast, Women Lead Data, where she shares insights and amplifies the voices of women in the data industry. Connect with Lindsay on LinkedIn.  All views and opinions expressed are those of the individuals and do not necessarily reflect their employers or anyone else. Join the conversation on LinkedIn. Apply to be a guest or nominate a practitioner.  Do you love what you're listening to? Please rate and review the podcast, and share it with fellow practitioners you know. Your support helps us reach more listeners and continue providing valuable insights!

Summary Data contracts are both an enforcement mechanism for data quality, and a promise to downstream consumers. In this episode Tom Baeyens returns to discuss the purpose and scope of data contracts, emphasizing their importance in achieving reliable analytical data and preventing issues before they arise. He explains how data contracts can be used to enforce guarantees and requirements, and how they fit into the broader context of data observability and quality monitoring. The discussion also covers the challenges and benefits of implementing data contracts, the organizational impact, and the potential for standardization in the field.

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data managementData lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst is an end-to-end data lakehouse platform built on Trino, the query engine Apache Iceberg was designed for, with complete support for all table formats including Apache Iceberg, Hive, and Delta Lake. Trusted by teams of all sizes, including Comcast and Doordash. 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.At Outshift, the incubation engine from Cisco, they are driving innovation in AI, cloud, and quantum technologies with the powerful combination of enterprise strength and startup agility. Their latest innovation for the AI ecosystem is Motific, addressing a critical gap in going from prototype to production with generative AI. Motific is your vendor and model-agnostic platform for building safe, trustworthy, and cost-effective generative AI solutions in days instead of months. Motific provides easy integration with your organizational data, combined with advanced, customizable policy controls and observability to help ensure compliance throughout the entire process. Move beyond the constraints of traditional AI implementation and ensure your projects are launched quickly and with a firm foundation of trust and efficiency. Go to motific.ai today to learn more!Your host is Tobias Macey and today I'm interviewing Tom Baeyens about using data contracts to build a clearer API for your dataInterview IntroductionHow did you get involved in the area of data management?Can you describe the scope and purpose of data contracts in the context of this conversation?In what way(s) do they differ from data quality/data observability?Data contracts are also known as the API for data, can you elaborate on this?What are the types of guarantees and requirements that you can enforce with these data contracts?What are some examples of constraints or guarantees that cannot be represented in these contracts?Are data contracts related to the shift-left?Data contracts are also known as the API for data, can you elaborate on this?The obvious application of data contracts are in the context of pipeline execution flows to prevent failing checks from propagating further in the data flow. What are some of the other ways that these contracts can be integrated into an organization's data ecosystem?How did you approach the design of the syntax and implementation for Soda's data contracts?Guarantees and constraints around data in different contexts have been implemented in numerous tools and systems. What are the areas of overlap in e.g. dbt, great expectations?Are there any emerging standards or design patterns around data contracts/guarantees that will help encourage portability and integration across tooling/platform contexts?What are the most interesting, innovative, or unexpected ways that you have seen data contracts used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on data contracts at Soda?When are data contracts the wrong choice?What do you have planned for the future of data contracts?Contact Info LinkedInParting 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 [email protected] with your story.Links SodaPodcast EpisodeJBossData ContractAirflowUnit TestingIntegration TestingOpenAPIGraphQLCircuit Breaker PatternSodaCLSoda Data ContractsData MeshGreat Expectationsdbt Unit TestsOpen Data ContractsODCS == Open Data Contract StandardODPS == Open Data Product SpecificationThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

OpenLineage is an open standard for lineage data collection, integrated into the Airflow codebase, facilitating lineage collection across providers like Google, Amazon, and more. Atlan Data Catalog is a 3rd generation active metadata platform that is a single source of trust unifying cataloging, data discovery, lineage, and governance experience. We will demonstrate what OpenLineage is and how, with minimal and intuitive setup across Airlfow and Atlan, it presents unified workflows view, efficient cross-platform lineage collection, including column level, in various technologies (Python, Spark, dbt, SQL etc.) and clouds (AWS, Azure, GCP, etc.) - all orchestrated by Airflow. This integration enables further use case unlocks on automated metadata management by making the operational pipelines dataset-aware for self-service exploration. It also will demonstrate real world challenges and resolutions for lineage consumers in improving audit and compliance accuracy through column-level lineage traceability across the data estate. The talk will also briefly overview the most recent OpenLineage developments and planned future enhancements.

Airflow is often used for running data pipelines, which themselves connect with other services through the provider system. However, it is also increasingly used as an engine under-the-hood for other projects building on top of the DAG primitive. For example, Cosmos is a framework for automatically transforming dbt DAGs into Airflow DAGs, so that users can supplement the developer experience of dbt with the power of Airflow. This session dives into how a select group of these frameworks (Cosmos, Meltano, Chronon) use Airflow as an engine for orchestrating complex workflows their systems depend on. In particular, we will discuss ways that we’ve increased Airflow performance to meet application-specific demands (high-task-count Cosmos DAGs, streaming jobs in Chronon), new Airflow features that will evolve how these frameworks use Airflow under the hood (DAG versioning, dataset integrations), and paths we see these projects taking over the next few years as Airflow grows. Airflow is not just a DAG platform, it’s an application platform!

Looking for a way to streamline your data workflows and master the art of orchestration? As we navigate the complexities of modern data engineering, Airflow’s dynamic workflow and complex data pipeline dependencies are starting to become more and more common nowadays. In order to empower data engineers to exploit Airflow as the main orchestrator, Airflow Datasets can be easily integrated in your data journey. This session will showcase the Dynamic Workflow orchestration in Airflow and how to manage multi-DAGs dependencies with Multi-Dataset listening. We’ll take you through a real-time data pipeline with Pub/Sub messaging integration and dbt in Google Cloud environment, to ensure data transformations are triggered only upon new data ingestion, moving away from rigid time-based scheduling or the use of sensors and other legacy ways to trigger a DAG.

Balyasny Asset Management (BAM) is a diversified global investment firm founded in 2001 with over $20 billion in assets under management. As dbt took hold at BAM, we had multiple teams building dbt projects against Snowflake, Redshift, and SQL Server. The common question was: How can we quickly and easily productionise our projects? Airflow is the orchestrator of choice at BAM, but our dbt users ranged from Airflow power users to people who’d never heard of Airflow before. We built a single solution on top of Cosmos that allowed us to: Decouple the dbt project from the Airflow repository Have each dbt node run as a separate Airflow task Allow users to run dbt with little to no Airflow knowledge Enable users to have fine-grained control over how dbt is run and to combine it with other Airflow tasks Provide observability, monitoring, and alerting.

dbt became the de facto for data teams building reliable and trustworthy SQL code leveraging a modern data stack architecture. The dbt logic needs to be orchestrated, and jobs scheduled to meet business expectations. That’s where Airflow comes into play. In this quick introduction session, you’ll gonna learn: How to leverage dbt-Core & Airflow to orchestrate pipelines Write DAGs in a Pythonic way Apply best practices on your jobs

There are many Airflow tutorials. However, many don’t show the full process of sourcing, transforming, testing, alerting, documenting, and finally supplying data. This talk with go over how to piece together an end-to-end Airflow project that transforms raw data to be consumable by the business. It will include how various technologies can all be orchestrated by Airflow to satisfy the needs of analysts, engineers, and business stakeholders. The talk will be divided into the following sections: Introduction: Introducing the business problem and how we came up with the solution design Data sourcing: Fetching and storing API data using basic operators and hooks Transformation and Testing: How to use dbt to build and test models based on the raw data Alerting: Alerting the necessary parties when any part of this DAG fails using Slack Consumption: How to make dynamic data accessible to business stakeholders

The integration between dbt and Airflow is a popular topic in the community, both in previous editions of Airflow Summit, in Coalesce and the #airflow-dbt Slack channel. Astronomer Cosmos ( https://github.com/astronomer/astronomer-cosmos/ ) stands out as one of the libraries that strives to enhance this integration, having over 300k downloads per month. During its development, we’ve encountered various performance challenges in terms of scheduling and task execution. While we’ve managed to address some, others remain to be resolved. This talk describes how Cosmos works, the improvements made over the last 1.5 years, and the roadmap. It also aims to collect feedback from the community on how we can further improve the experience of running dbt in Airflow.

In this talk, we’ll discuss how Instacart leverages Apache Airflow to orchestrate a vast network of data pipelines, powering both our core infrastructure and dbt deployments. As a data-driven company, Airflow plays a critical role in enabling us to execute large and intricate pipelines securely, compliantly, and at scale. We’ll delve into the following key areas: a. High-Throughput Cluster Management: We’ll explore how we manage and maintain our Airflow cluster, ensuring the efficient execution of over 2,000 DAGs across diverse use cases. b. Centralized Airflow Vision: We’ll outline our plans for establishing a company-wide, centralized Airflow cluster, consolidating all Airflow instances at Instacart. c. Custom Airflow Tooling: We’ll showcase the custom tooling we’ve developed to manage YML-based DAGs, execute DAGs on external ECS workers, leverage Terraform for cluster deployment, and implement robust cluster monitoring at scale. By sharing our extensive experience with Airflow, we aim to contribute valuable insights to the Airflow community.

Summary

Data lakehouse architectures have been gaining significant adoption. To accelerate adoption in the enterprise Microsoft has created the Fabric platform, based on their OneLake architecture. In this episode Dipti Borkar shares her experiences working on the product team at Fabric and explains the various use cases for the Fabric service.

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 is an end-to-end data lakehouse platform built on Trino, the query engine Apache Iceberg was designed for, with complete support for all table formats including Apache Iceberg, Hive, and Delta Lake. Trusted by teams of all sizes, including Comcast and Doordash. 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. Your host is Tobias Macey and today I'm interviewing Dipti Borkar about her work on Microsoft Fabric and performing analytics on data withou

Interview

Introduction How did you get involved in the area of data management? Can you describe what Microsoft Fabric is and the story behind it? Data lakes in various forms have been gaining significant popularity as a unified interface to an organization's analytics. What are the motivating factors that you see for that trend? Microsoft has been investing heavily in open source in recent years, and the Fabric platform relies on several open components. What are the benefits of layering on top of existing technologies rather than building a fully custom solution?

What are the elements of Fabric that were engineered specifically for the service? What are the most interesting/complicated integration challenges?

How has your prior experience with Ahana and Presto informed your current work at Microsoft? AI plays a substantial role in the product. What are the benefits of embedding Copilot into the data engine?

What are the challenges in terms of safety and reliability?

What are the most interesting, innovative, or unexpected ways that you have seen the Fabric platform used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on data lakes generally, and Fabric specifically? When is Fabric the wrong choice? What do you have planned for the future of data lake analytics?

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 [email protected] with your story.

Links

Microsoft Fabric Ahana episode DB2 Distributed Spark Presto Azure Data MAD Landscape

Podcast Episode ML Podcast Episode

Tableau dbt Medallion Architecture Microsoft Onelake ORC Parquet Avro Delta Lake Iceberg

Podcast Episode

Hudi

Podcast Episode

Hadoop PowerBI

Podcast Episode

Velox Gluten Apache XTable GraphQL Formula 1 McLaren

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Sponsored By: Starburst:

This episode is brought to you by Starburst - an end-to-end data lakehouse platform for data engineers who are battling to build and scale high quality data pipelines on the data lake. Powered by T

Yohei Nakajima is an investor by day and coder by night. In particular, one of his projects, an AI agent framework called BabyAGI that creates a plan-execute loop, got a ton of attention in the past year. The truth is that AI agents are an extremely experimental space, and depending on how strict you want to be with your definition, there aren't a lot of production use cases today.  Yohei discusses the current state of AI agents and where they might take us.  For full show notes and to read 6+ years of back issues of the podcast's companion newsletter, head to https://roundup.getdbt.com. The Analytics Engineering Podcast is sponsored by dbt Labs.

Misha Panko has worked in data for a long time, including on high performance data teams at Uber and Google. Today, Misha is the co-founder and CEO of Motif Analytics, a product focused on helping growth and ops teams understand their event data. In this episode, Tristan and Misha nerd out about the state of the art in computational neuroscience, where Misha got his PhD. They then go deep into event stream data and how it differs from classical fact and dimension data, and why it needs different analytical tools. Make sure to check out the back half of the episode, where they dive into AI and how Motif is applying breakthroughs in language modeling to train foundation models of event sequences—check out his team's blog post on their work. For full show notes and to read 6+ years of back issues of the podcast's companion newsletter, head to https://roundup.getdbt.com. The Analytics Engineering Podcast is sponsored by dbt Labs.