Abstract: It is analytical module to retrieve data from multiple sources and prioritize clinical charts along with derivedinformation like scores, specialty, etc. which are needed for downstream.
Technologies:
PySpark, Java, Oozie, Hive, Sqoop, Azure HDInsight and Azure Storage

Migrated the complex analytical engine from SAS to Big Data in short span of time.
Achieved the best performance such that whole process will be done in 8 hours for 125 million claims.
Accurately prioritized the charts based on the criticality of chart.

Abstract
: It is analytical module to retrieve data from multiple sources and prioritize clinical charts along with derivedinformation like scores, specialty, etc. which are needed for downstream.
Technologies:
PySpark, Java, Oozie, Hive, Sqoop, Azure HDInsight and Azure Storage
Achievements:

Migrated the complex analytical engine from SAS to Big Data in short span of time.
Achieved the best performance such that whole process will be done in 8 hours for 125 million claims.
Accurately prioritized the charts based on the criticality of chart.

Abstract :
Configurable Framework to evaluate the quality of the input and output data. It generates the Report with the list ofchecks Failed/Passed. It supports around 30 quality checks on files of multiple formats.
Technologies:
Spark, Java, Shell and Oozie.
Achievements:

Saved the cost of Alteryx license which was used for Data Quality before.
Expanded the tool usage to multiple teams across organization.

Abstract :
It is an analytical module to recommend a member to a appropriate assessment program among differentassessment programs available like In-Office Assessment (IOA), In-Home Assessment (IHA) etc. This is based on predefinedrules across multiple domains.
Technologies:
PySpark, Java, Oozie, Hive, Shell, Azure Storage, HDInsight and Kafka
Achievements:Able to
integrate this process to the existing analytical structure and achieved very good performance (20-30mins for8million members).
Migrating the module to Azure.