Chandresh Bhatt is a Senior Software Engineer based in Pune with over a decade of experience building data-intensive distributed systems using Java, Scala, Hadoop and Apache Spark. He has hands-on expertise in cluster administration, HDFS, Spark (RDDs, Spark SQL, Streaming, GraphX, ML) and has deployed analytics databases like SnappyData/ComputeDB on Docker, Kubernetes and cloud platforms. At companies from Infosys to Mastercard he’s driven full lifecycle projects—designing ETL/data lake pipelines, implementing OTP and notification systems, and automating promotional and upgrade workflows for large-scale appliances. He brings deep practical knowledge of complex data types and SQL validation from his SnappyData contributions, plus proficiency in multithreading, concurrency and common design patterns. Known for bridging engineering and operational concerns, Chandresh consistently delivers performant, production-ready solutions and reduces time-to-insight for analytics teams. A pragmatic problem-solver, he often pairs rigorous testing and schema management with lightweight automation to keep systems resilient and maintainable.
8 years of coding experience
16 years of employment as a software developer
Engineer's Degree Instrumentation Technology/Technician, Engineer's Degree Instrumentation Technology/Technician at D.N. Patel Colleage of Enigneering
Project SnappyData - memory optimized analytics database, based on Apache Spark™ and Apache Geode™. Stream, Transact, Analyze, Predict in one cluster
Role in this project:
Database Engineer / Database Administrator
Contributions:9 commits, 19 PRs, 294 pushes in 1 year 1 month
Contributions summary:Chandresh focused on testing and validating complex data types within the SnappyData memory-optimized analytics database. They created SQL scripts to test array, map, and struct types, as well as combinations of these, demonstrating a deep understanding of data structures and SQL query optimization. The user's work involved writing extensive SQL queries to validate data integrity and functionality across different data types. This involved creating, inserting, and querying data within tables, and then dropping/cleaning up the tables and schemas, indicating a proficiency in database schema management.
Contributions:106 pushes, 2 branches in 2 years 6 months
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