About

Raghavendra Desai is a Senior Member of Technical Staff at Salesforce with over 7 years of experience in the IT industry. He is a passionate big data/Hadoop/Python developer with extensive experience in Big-data/Hadoop/Spark Technologies. He has worked on manufacturing and supply chain optimization programs within Intel IT and is currently working with marketing cloud in Salesforce. Raghavendra is currently working on developing table maintenance jobs for a lake-house project which involves manipulating data of iceberg tables in AWS through running spark jobs run with EMR instance. Previously, he worked as a Big Data Developer in the Enterprise Data Analytics team in Intel, delivering analytical solutions for manufacturing and supply chain orgs/data leveraging Hadoop and related technologies along with ML. His primary responsibility was to implement continuous ingestion pipelines to load data from multiple sources such as API, Streaming (Active MQ, Kafka), Shared Drive, RDBMS into HDFS, HBase, Hive, Elastic, Azure. Raghavendra is proficient in various programming languages such as C, Core Java, C#, Python, SQL, HQL, Shell Scripting, Linux, VI, and Object-Oriented Programming. He has experience in databases such as MYSQL, SQL SERVER, Tera Data, HANA Basics, and big data technologies such as Hadoop, HDFS, Hive, Impala, HBase, Elastic Search, Logstash, Kibana, Flume, Sqoop, Azure, Spark Core, Spark SQL, PySpark, Spark Streaming, and Apache Kafka. He is also knowledgeable in ML Libraries such as NumPy, Pandas, Scikit-Learn, NLTK, and Google Maps, and APIs and Tools such as REST API, Google, TFS, Power BI, Gitlab, AutoSys, Team City, Moba X term, Putty, and Orange. Raghavendra has experience in Agile, SAFE, CI/CD methodologies, and has 3 Scopus indexed journal publications in the Internet of Things domain and 1 IEEE conference publication. He has worked on a couple of POCs in text analytics and ML. Raghavendra is currently a Software Engineer at CenturyLink, where he has worked on a search engine project to discover internal document data from multiple locations in the network and as a Hadoop developer in ingestion of Construction Data in Excel files into Hadoop. He has optimized data ingestion using spark to integrate, process millions of records from many sources, and implemented