Suresh Thalamati
Principal Software Engineer at Salesforce
San Jose, California, United States
Summary
👤
Senior🎓
Top SchoolSuresh Thalamati is a Principal Software Engineer in San Jose with over 20 years of systems and data-platform experience and a decade focused on Big Data and Apache Spark/Hadoop ecosystems. He blends deep database internals expertise—demonstrated as an Apache Derby committer and contributor to Spark’s JDBC and data source layers—with pragmatic engineering that drove optimized data loads and function pushdown implementations in commercial Spark distributions. At IBM he productized research prototypes and led data-movement tooling; at Salesforce he now applies that background to large-scale, production-grade analytics services. His work on Spark’s JDBC dialects, quoted-column handling, and credential-masking shows a knack for tackling subtle interoperability and security issues that surface only at scale.
10 years of coding experience
24 years of employment as a software developer
Computer Science and Engineering, Computer Science, Computer Science and Engineering, Computer Science at National Institute of Technology Calicut
Github Skills (10)
javas10
big-data10
db210
sql10
datatypes10
jdbc10
database-design10
java10
scala10
postgresql9
Programming languages (2)
Github contributions (5)
apache/spark
Aug 2015 - Mar 2018
Apache Spark - A unified analytics engine for large-scale data processing
Role in this project:
Back-end Developer & Database Engineer
Back-end Developer & Database EngineerContributions:28 PRs, 138 comments in 2 years 7 months
Contributions summary:Suresh primarily contributed to the JDBC data source within the Apache Spark project. Their work focused on enhancing the JDBC dialect for DB2, adding support for specific data types, and addressing issues related to null values in array-type columns when reading from Postgres. The user implemented fixes for handling quoted column names and masking sensitive credentials in the SQL plan output. Furthermore, they added the functionality for users to define database column types during the table creation process via an option.
analyticspythondata-processingsqlapache
sureshthalamati/spark
Aug 2015 - Mar 2018
Mirror of Apache Spark
Contributions:119 pushes, 26 branches in 2 years 7 months
spark-mlapachebig-datasparkscala
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