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.
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 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!
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 dataengineeringpodcast.com/datafold today to book a demo with Datafold.
Have you ever woken up to a crisis because a number on a dashboard is broken and no one knows why? Or sent out frustrating slack messages trying to find the right data set? Or tried to understand what a column name means?
Our friends at Atlan started out as a data team themselves and faced all this collaboration chaos themselves, and started building Atlan as an internal tool for themselves. Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more.
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Shipyard is an orchestration platform that helps data teams build out solid data operations from the get-go by connecting data tools and streamlining data workflows. Shipyard offers low-code templates that are configured using a visual interface, replacing the need to write code to build workflows while enabling engineers to get their work into production faster. If a solution can’t be built with existing templates, engineers can always automate scripts in the language of their choice to bring any internal or external process into their workflows.
Observability and alerting are built into the Shipyard platform, ensuring that breakages are identified before being discovered downstream by business teams. With a high level of concurrency, scalability, and end-to-end encryption, Shipyard enables data teams to accomplish more without relying on other teams or worrying about infrastructure challenges, while also ensuring that business teams trust the data made available to them. Go to dataengineeringpodcast.com/shipyard to get started automating powerful workflows with their free developer plan today!
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
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- 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
- 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?
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
- 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.
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- Data Lakehouse
- Data Virtualization
- AWS Lake Formation
- Starburst Galaxy