What "Data Lineage Done Right" Looks Like And How They're Doing It At Manta
Data Engineering Podcast
00:00
Using Custom Lineage Information in a Hybrid Environment?
The first thing is to kind of follow a methodology, which is, what is it that my user wants to see? If they only need to see that it came from this application and goes to that application, that may be as deep as you need to go. Anif you do application to application n lineage, you could just draw up the lineage in an hour. But if you want that kind of lineage and you need to do it manually, that means you have to dig deeper. And finally, the last question i think needs to get asked when you get into those hybrid situations is, how many?
Transcript
Play full episode
Transcript
Episode notes
Summary
Data lineage is the roadmap for your data platform, providing visibility into all of the dependencies for any report, machine learning model, or data warehouse table that you are working with. Because of its centrality to your data systems it is valuable for debugging, governance, understanding context, and myriad other purposes. This means that it is important to have an accurate and complete lineage graph so that you don’t have to perform your own detective work when time is in short supply. In this episode Ernie Ostic shares the approach that he and his team at Manta are taking to build a complete view of data lineage across the various data systems in your organization and the useful applications of that information in the work of every data stakeholder.
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.
- The only thing worse than having bad data is not knowing that you have it. With Bigeye’s data observability platform, if there is an issue with your data or data pipelines you’ll know right away and can get it fixed before the business is impacted. Bigeye let’s data teams measure, improve, and communicate the quality of your data to company stakeholders. With complete API access, a user-friendly interface, and automated yet flexible alerting, you’ve got everything you need to establish and maintain trust in your data. Go to dataengineeringpodcast.com/bigeye today to sign up and start trusting your analyses.
- Prefect is the modern Dataflow Automation platform for the modern data stack, empowering data practitioners to build, run and monitor robust pipelines at scale. Guided by the principle that the orchestrator shouldn’t get in your way, Prefect is the only tool of its kind to offer the flexibility to write code as workflows. Prefect specializes in glueing together the disparate pieces of a pipeline, and integrating with modern distributed compute libraries to bring power where you need it, when you need it. Trusted by thousands of organizations and supported by over 20,000 community members, Prefect powers over 100MM business critical tasks a month. For more information on Prefect, visit dataengineeringpodcast.com/prefect.
- Your host is Tobias Macey and today I’m interviewing Ernie Ostic about Manta, an automated data lineage service for managing visibility and quality of your data workflows
Interview
- Introduction
- How did you get involved in the area of data management?
- Can you describe what Manta is and the story behind it?
- What are the core problems that Manta aims to solve?
- Data lineage and metadata systems are a hot topic right now. What is your summary of the state of the market?
- What are the capabilities that would lead a team or organization to choose Manta in place of the other options?
- What are some examples of "data lineage done wrong"? (what does that look like?)
- What are the risks associated with investing in an incomplete solution for data lineage?
- What are the core attributes that need to be tracked consistently to enable a comprehensive view of lineage?
- How do the practices for collecting lineage and metadata differ between structured, semi-structured, and unstructured data assets and their movement?
- Can you describe how Manta is implemented?
- How have the design and goals of the product changed or evolved?
- What is involved in integrating Manta with an organization’s data systems?
- What are the biggest sources of friction/errors in collecting and cleaning lineage information?
- One of the interesting capabilities that you advertise is versioning and time travel for lineage information. Why is that a necessary and useful feature?
- Once an organization’s lineage information is available in Manta, how does it factor into the daily workflow of different roles/stakeholders?
- There are a variety of use cases for metadata in a data platform beyond lineage. What are the benefits that you see from focusing on that as a core competency?
- Beyond validating quality, identifying errors, etc. it seems that automated discovery of lineage could produce insights into when the presence of data assets that shouldn’t exist. What are some examples of similar discoveries that you are aware of?
- What are the most interesting, innovative, or unexpected ways that you have seen Manta used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on Manta?
- When is Manta the wrong choice?
- What do you have planned for the future of Manta?
Contact Info
- @dsrealtime01 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
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
The AI-powered Podcast Player
Save insights by tapping your headphones, chat with episodes, discover the best highlights - and more!
