Michael Armbrust
Distinguished Software Engineer at Databricks
Berkeley, California, United States
Summary
🤩
Rockstar🎓
Top SchoolMichael Armbrust is a Distinguished Software Engineer based in Berkeley with 17 years of experience building and evolving large-scale data processing systems. At Databricks he led work on Structured Streaming and Delta Lake, driving production-ready features and performance improvements that power real-time analytics. A prolific Apache Spark contributor, he helped design the Dataset API, advanced Spark SQL and Parquet performance, and maintained key connectors like spark-avro and spark-redshift. He combines deep research credentials (PhD, UC Berkeley) with hands-on release engineering and DevOps experience, routinely owning builds, compatibility and publication for open-source projects. Known for translating academic rigor into production systems, he also contributes technical documentation and benchmarks to make complex changes accessible to users. His background blends distributed-systems research with practical engineering discipline, making him effective at shipping robust, scalable data platforms.
17 years of coding experience
15 years of employment as a software developer
PhD, Computer Science, PhD, Computer Science at University of California, BerkeleyBS, Computer Science and Mathematics, BS, Computer Science and Mathematics at Purdue University
Stackoverflow
1,565reputation
215kreached
22answers
0questions
apache-spark
top-5%
scala
top-5%
databricks
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json
top-5%
Github Skills (47)
spark-sql10
notebook10
avro10
apache-spark10
spark-structured-streaming10
sbt10
java10
performance-testing10
scala10
stream10
javas10
release-management10
sql10
build-automation10
data-processing10
Programming languages (6)
Github contributions (5)
databricks/spark-sql-perf
Jul 2015 - Mar 2016
Role in this project:
Back-end Developer
Back-end DeveloperContributions:63 commits, 45 PRs, 39 pushes in 8 months
Contributions summary:Michael refactored code to enable the project to function within notebooks. They modified the `query.scala`, `runBenchmarks.scala`, and other supporting files to enable the execution and benchmarking of queries within a notebook environment. The changes included adjustments to the query execution process, handling of results, and integration with the notebook environment. The contributions focused on improving the usability and adaptability of the performance testing framework.
databricks/spark-avro
Oct 2014 - Sep 2015
Avro Data Source for Apache Spark
Role in this project:Back-end Developer
Back-end DeveloperContributions:29 commits, 17 PRs, 17 pushes in 10 months
Contributions summary:Michael contributed primarily to the `spark-avro` project, focusing on the integration of Apache Spark with the Avro data format. Their work included fixing build and dependency issues, refactoring code to align with API changes, and implementing features such as saving DataFrames as Avro files. They were involved in porting the library to Spark 1.3 and addressing binary compatibility concerns through code cleanup and exception handling.
avroavro-datadata-sourceapachebig-data
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