PySpark + Hive + GCP + Airflow + Jenkins + Encryption Case Study

Revuteck contributed to a large-scale retail campaign analytics modernization initiative focused on re-architecting legacy Hive workloads into scalable PySpark pipelines, enabling cloud migration to GCP, implementing secure encrypted extract workflows, automating orchestration pipelines, and improving production support reliability using Airflow, Jenkins, Gerrit, Kubernetes, and enterprise-grade monitoring practices.

Business Required:

The retail business operated multiple campaign processing systems responsible for campaign performance tracking, SalesHub extracts, Google Ads ingestion, historical campaign analytics, and secure downstream delivery workflows.

Over time, the existing ecosystem became difficult to maintain due to fragmented ETL jobs, legacy Hive logic, isolated shell-script workflows, inconsistent orchestration patterns, and growing scalability demands.

The business required a modern data platform that could:

• Modernize legacy Hive workloads

• Improve campaign analytics scalability

• Support secure extract generation

• Enable GCP-based cloud migration

• Improve workflow orchestration reliability

• Strengthen CI/CD governance

• Improve production support visibility

• Support enterprise-grade campaign analytics

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Solution Summary

The solution modernized the retail campaign ecosystem by introducing a cloud-native architecture where:

• Campaign data was ingested into GCP buckets

• PySpark and Hive handled transformation workflows

• BigQuery supported analytical processing and reporting

• Scala supported secure detokenization workflows

• Airflow and Azkaban orchestrated scheduling operations

• Jenkins and Gerrit automated CI/CD workflows

• Kubernetes optimized scalable PySpark execution

• PGP and AES encryption secured downstream extract delivery

• SRE monitoring improved operational reliability and support visibility