Rohith Diddi Email & Phone Number

United States

Fort Lauderdale, Florida, United States

Selected and created data into CSV files, which were then organized and saved in AWS Redshift after being stored in AWS S3 via AWS EC2. In charge of using the SQOOP tool to import data from Postgres to HDFS and HIVE. knowledgeable about moving HiveQL data to Impala in order to reduce query response times. Avro and Parquet data formats were implemented for Apache Hive calculations to manage unique business requirements. created a Python script to extract data from on-premises systems and send it to Amazon S3 via REST APIs. used Spring Boot to implement a cloud architecture based on micro services. experience deploying using Terraform and AWS Cloud Formation templates, and converting a current AWS infrastructure to a server-less architecture (AWS Lambda, Kinesis). created intricate Talend ETL processes to move data from flat files into databases.

Cincinnati, Ohio, United States

Developed and implemented custom Hadoop applications in an Azure environment. used PySpark's Spark SQL API to import and extract data and run SQL queries. mostly engaged in the Azure data migration process through Jenkins and GitHub repository integration. used Spark Data Frames Operations to analyze the Hive data and carry out the necessary validations in the data. Using a combination of Azure Data Factory, T-SQL, Spark SQL, and U-SQL Azure Data Lake Analytics, extract, transform, and load data from source systems to Azure Data Storage services. Data processing in Azure Data Bricks once data is ingested into one or more Azure Services (Azure Data Lake, Azure Storage, Azure SQL, and Azure DW).

Stamford, Connecticut, United States

Worked on building data intake pipelines with big data technologies, such as Hive, Impala, Spark, Kafka, and Talend, utilizing an ETL tool and bash scripting. involved in leveraging Spark data frames, Scala, and Python to translate Hive/SQL queries into Spark transformations. transferred data between relational databases and Hadoop using Sqoop. worked on Hadoop's HDFS to store and retrieve massive datasets.good practical GitHub experience. functional understanding of SSL/TLS, Sentry, Kerberos, and other cluster security components. Using Pyspark and MLlib, create machine learning models to demonstrate the capabilities of big data. Talend and Kafka were used to implement the ability to stream data from several sources. Data Lake Management with AWS S3. In charge of managing requests for data coming in and going out via the big data platform.

Chesterfield, Missouri, United States

Kerberos authentication protocol was used to implement the permission and authentication service. Installed operating system updates, Hadoop updates, patches, and Cloudera version upgrades as needed while collaborating with various teams.Participated in Hadoop HBase cluster setup and benchmarking for internal use. created Pig Latin scripts to load data into HDFS by extracting it from web server output files. Shell scripts were used to export the result set from HIVE to MySQL.actively participating in bug fixes and code reviews to enhance performance.analyzed Hadoop clusters and several big data analysis tools, such as Hive, Spark, Pig and Sqoop, and Zookeeper.configured MySQL to store the Hive table metadata in the Hive meta store.expertise using Oozie to schedule jobs.

Hyderabad, Telangana, India

Worked as a transformation developer, warehouse designer, mapping designer, and source analyst for Informatica PowerCenter Designer. Data warehouse reconstruction was done using a variety of Informatica transformations. In charge of handling the module's urgent production problems at the post-implementation stage. had the duty of drafting the design and implementation documentation, estimating work, organizing the coding and implementation process, and authoring and fine-tuning the mappings to enhance performance in the production setting. created and constructed business rules called Aggregate, Join, and Lookup transformation rules to provide consolidated (fact/summary) data that is categorized by dimensions. For the Slowly Changing Dimension tables, Lookup, Sequence generator, Router, and Update Strategy transformations were used to insert, remove, and update the records. Served as the team leader for a three-person Informatica, Unix, and Oracle front-end and back-end Epiphany team.