The retailer generated massive volumes of sales and customer data from multiple channels, including physical stores, e-commerce platforms, mobile applications and external partners. The data arrived in different formats and at varying frequencies, making integration and management increasingly complex.

Key challenges included:

• Integrating structured and semi-structured data from multiple sources.
• Processing terabytes of weekly transaction data without performance bottlenecks.
• Eliminating duplicate records and improving overall data quality.
• Protecting sensitive customer information while meeting compliance requirements.
• Building a scalable, cost-effective data platform capable of supporting enterprise analytics and future AI initiatives.

UPSTA designed and implemented an end-to-end retail data pipeline that automated data ingestion, processing, governance and storage within a Hadoop Hive data lake.

The solution included:

• Batch, streaming and API-based ingestion using Apache Sqoop, Apache Kafka and Spark.
• Automated data cleansing, deduplication, schema validation and format standardization.
• Secure storage in HDFS using optimized Parquet/ORC formats with partitioning and bucketing for faster query performance.
• Enterprise data governance through Apache Ranger for role-based security and Apache Atlas for metadata management and lineage.
• Integration with BI and machine learning platforms, enabling reporting, predictive analytics and data science workloads.
• Cloud-based Hadoop deployment options for improved scalability and optimized infrastructure costs.