Databricks: What We Have Learned by Eating Our Dog Food

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Databricks Unified Analytics Platform (UAP) is a cloud-based service for running all analytics in one place – from highly reliable and performant data pipelines to state-of-the-art Machine Learning. From the original creators of Apache Spark and MLflow, it provides data science and engineering teams ready to use pre-packaged clusters with optimized Apache Spark and various ML frameworks coupled with powerful collaboration capabilities to improve productivity across the ML lifecycle. Yada yada yada… But in addition to being a vendor Databricks is also a user of UAP.

So, what have we learned by eating our own dogfood? Attend a “from the trenches report” from Suraj Acharya, Director Engineering responsible for Databricks’ in-house data engineering team how his team put Databricks technology to use, the lessons they have learned along the way and best practices for using Databricks for data engineering.

 

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About Xuan Wang

Xuan Wang is a data scientist/engineer at Databricks. He is working on building data products and ETL pipelines on top of Databricks’ Unified Analytic Platform and Apache Spark. Prior to joining Databricks, he was a postdoctoral researcher working on probabilistic models in random graphs and random medium. He received his Ph.D. in Statistics from The University of North Carolina at Chapel Hill in 2014.