Retail Campaign Analytics & Data Re-Architecture Platform
Modernizing retail campaign analytics into a scalable, secure, cloud-native data engineering ecosystem using PySpark, Hive, BigQuery, GCP, Airflow, Jenkins, Kubernetes, and enterprise-grade encryption workflows.
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
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
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
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
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