Retail data processing and storage

• Automated ingestion of structured and unstructured retail data (e.g., customer data, transaction history, sales by product) from internal and external data sources.

• Cost-effective storing of all data types in a data lake.

• Batch and real-time retail data processing.

• Automated data cleansing: adding missing data to customer profiles, removing redundant product details.

• Aggregating structured data in a data warehouse that is optimized for analytics querying and reporting.