Suraj Acharya

Director Engineering, Databricks

Suraj leads the Data team at Databricks, which is responsible for telemetry and analytics, and the internal platform for users of business and product data. Previously he’s lead Data and Infrastructure teams at Foursquare, Yahoo and BEA.

Past sessions

Summit 2021 Databricks on Databricks: AMA with Data Engineering SMEs

May 26, 2021 11:30 AM PT

Data engineers and data leaders are the linchpin of every data-driven organization. Today's data engineers face a number of critical use cases: ensuring the organization has access to clean, reliable data, maintaining governance and security as the organization scales, and providing access to data teams for analysis. Join this session for live Q&A with our SMEs to learn how we at Databricks face these challenges head-on by leveraging the Databricks lakehouse platform.

In this session watch:
Jason Pohl, Principal Solution Architect, Databricks
Mike Hamilton, VP of IT , Databricks
Stacy Kerkela, Director of Engineering, Databricks
Suraj Acharya, Director Engineering, Databricks
Sam Shah, VP of Engineering, Databricks

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Summit 2019 Databricks: What We Have Learned by Eating Our Dog Food

April 24, 2019 05:00 PM PT

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.