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Implemented an automated solution for resource configuration, deployment, and scheduling


Client Background

Client is an American media measurement and analytics company providing marketing data and analytics to enterprises, media and advertising agencies, and publishers.


  • Needed consultation for evaluation of tools and approaches for cloud adaptation. The objective was to offload computing from existing out-moded on-premise MapR cluster to the cloud.
  • Needed a solution custom-built for their live data (largest module) for evaluation and decision-making. 
  • Needed an automated solution for resource configuration, deployment, scheduling, scalability, etc.
  • Needed the ability to process incoming incremental data (10 TB or more) in a better and more efficient manner.


  • Provided a cloud-optimized, on-demand spin up solution for the computation offloading and Snowflake-based reporting solution.
  • Weekly extraction of 5TB or more data performed from the on premise MapR cluster and placed in S3 using shell script & AWS CLI executed by Airflow jobs.
  • Based on data size, copied over AWS EMR cluster is spun up using cloud formation templates and AWS CLI for executing Spark & Pig scripts.
  • Resultant data post-processing from EMR is pushed into S3 buckets for persistence.
  • AWS EMR cluster is auto-scaling enabled and gets purged post-processing.

Tools & Technologies

Amazon S3, Apache Pig, Apache Spark, Cloud Formation, Amazon EMR, MAPR, Apache Airflow, Python, R, Powershell, Snowflake, Bash

Key benefits

  • Provided a cost-efficient – On-demand solution for computation on AWS platform
  • Added value by providing best-suited recommendations for resource type and configuration for a cost-efficient and optimal solution.
  • Offloaded jobs that would need 48 hours in on perm server to cloud and processed them within 24 hours.
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