Bringing The Power Of The DataHub Real-Time Metadata Graph To Everyone At Acryl Data
Data Engineering Podcast
Data Folds, Pro Active Approach to Data Quality
De la hab has imerged as the meditated platform of choice a lot of data mesh implementations. Data folds helps data teams gain visibility and confidence in the quality of their analytical data through data profiling, column level lineage and intelligent anomaly detection. All these things have really made a huge difference in how detah is being used and interms of deployment.
Summary
The binding element of all data work is the metadata graph that is generated by all of the workflows that produce the assets used by teams across the organization. The DataHub project was created as a way to bring order to the scale of LinkedIn’s data needs. It was also designed to be able to work for small scale systems that are just starting to develop in complexity. In order to support the project and make it even easier to use for organizations of every size Shirshanka Das and Swaroop Jagadish founded Acryl Data. In this episode they discuss the recent work that has been done by the community, how their work is building on top of that foundation, and how you can get started with DataHub for your own work to manage data discovery today. They also share their ambitions for the near future of adding data observability and data quality management features.
Announcements
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
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- 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. Datafold helps Data teams gain visibility and confidence in the quality of their analytical data through data profiling, column-level lineage and intelligent anomaly detection. Datafold also helps automate regression testing of ETL code with its Data Diff feature that instantly shows how a change in ETL or BI code affects the produced data, both on a statistical level and down to individual rows and values. Datafold integrates with all major data warehouses as well as frameworks such as Airflow & dbt and seamlessly plugs into CI workflows. Go to dataengineeringpodcast.com/datafold today to start a 30-day trial of Datafold. Once you sign up and create an alert in Datafold for your company data, they will send you a cool water flask.
- Your host is Tobias Macey and today I’m interviewing Shirshanka Das and Swaroop Jagadish about Acryl Data, the company driving the open source metadata project DataHub for powering data discovery, data observability and federated data governance.
Interview
- Introduction
- How did you get involved in the area of data management?
- Can you describe what Acryl Data is and the story behind it?
- How has your experience of building and running DataHub at LinkedIn informed your product direction for Acryl?
- What are some lessons that your co-founder Swaroop has contributed from his experience at AirBnB?
- The data catalog/discovery/quality market has become very active over the past year. What is your perspective on the market, and what are the gaps that are not yet being addressed?
- How does the focus of Acryl compare to what the team at Metaphor are building?
- How has the DataHub project changed in the past year with more companies outside of LinkedIn using and contributing to it?
- What are your plans for Data Observability?
- Can you describe the system architecture that you have built at Acryl?
- What are the convenience features that you are building to augment the capabilities and integration process for DataHub?
- What are some typical workflows that data teams build out when working with Acryl?
- What are some examples of automated actions that can be triggered from metadata changes?
- What are the available events that can be used to trigger actions?
- What are some of the challenges that teams are facing when integrating metadata management and analysis into their data workflows?
- What are your thoughts on the potential for the Open Lineage and Open metadata projects?
- How is the governance of DataHub being managed?
- What are the most interesting, innovative, or unexpected ways that you have seen Acryl/DataHub used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on Acryl/DataHub?
- When is Acryl the wrong choice?
- What do you have planned for the future of Acryl?
Contact Info
- Shirshanka
- @shirshanka on Twitter
- shirshanka on GitHub
- Swaroop
- @arudis on Twitter
- swaroopjagadish on GitHub
Parting Question
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
Links
- Acryl Data
- DataHub
- Hudi
- Iceberg
- Delta Lake
- Apache Gobblin
- Airflow
- Superset
- Collibra
- Alation
- Strata Conference Presentation
- Acryl/DataHub Ingestion Framework
- Joe Hellerstein
- Trifacta
- DataHub Roadmap
- Data Mesh
- OpenLineage
- OpenMetadata
- Egeria Open Metadata
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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