Srividya yalamanchi

• Worked closely with the client to understand and gather the detailed requirements for each job to be migrated
from Hadoop to GCP.
• Prepared detailed design documents outlining the migration process, including job analysis, code translation, and
workflow scheduling.
• Retrieved source code from UC4, analyzed it, and securely transferred it to Git repositories for version control.
• Conducted an in-depth analysis of each jobs complexity, frequency, and dependencies, determining if it required
migration and maintaining all findings in an Excel tracker for future reference.
• Converted HiveQL, SparkSQL, PySpark, and Scala code into BigQuery SQL (BQSQL) using an internal tool
(Datastreak AI) and ensured the translated code was correctly implemented and stored in Git.
• Developed and configured workflows using DALM, adding components, variables, and commands, and scheduled
them in UC4 based on job frequency (hourly/daily).
• Created unit test cases, conducted thorough testing of migrated jobs, and supported User Acceptance Testing
(UAT) phases.
• Monitored job execution via UC4, analyzed logs to troubleshoot and resolve issues, and ensured smooth running
of the scheduled jobs.
• Used Datastreak AI and Airflow DAGs to validate the migration, ensuring that the data in BigQuery matched the
original Hive data in terms of accuracy and completeness.
• Built Looker dashboards to visually present the validation results and generate business reports from BigQuery
data as per client requirements.
• Prepared implementation checklists raised Change Requests (CRQs) in ServiceNow and ensured smooth
deployment of jobs in the production environment. Hive, Spark, Pyspark, Scala, SparkSQL, Google Cloud Platform, BigQuery, SQL (HiveQL,
BQSQL), GitHub, Jenkins, UC4 and DALM for scheduling, Airflow, Datastreak AI and Looker Dashboard.

Responsibilities:
• Designed and developed data loading strategies, transformation for the business to analyze the datasets.
• Processed various input file formats and stored them as in various partition model in HDFS.
• Responsible for Building, develop, testing shared components that will be used across modules.
• Responsible for performing for cleansing the raw data, transforming it and aggregate the data as per business.
• Implemented Spark using Python and utilizing Data frames and spark SQL API for faster processing of the data.