A multi-location retail chain transformed its analytics capabilities by migrating from an operations-heavy Apache data stack (Kafka, Cassandra/HBase, Hive/HDFS) to a modern, cloud-agnostic ELT platform. Leveraging Snowflake as the data platform, Fivetran for managed ingestion, dbt and Coalesce for transformations and governance, and Power BI for executive and operational analytics, the retailer reduced data operations overhead by 40–55%, enabled near–real-time POS insights (5–15 minutes), improved data quality and lineage, and accelerated decision-making across merchandising, store operations, and finance.
Staging (stg_) |
stg_pos_transactions (from Kafka POS topics)
stg_inventory_events (from Kafka or Cassandra/HBase)
stg_product_master (from Hive/ERP/master data)
stg_store_dim (from master data)
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Core/Conformed |
dim_store , dim_product , dim_date , dim_customer
fact_sales (incremental, with late-arriving handling)
fact_inventory_positions (incremental; end-of-day snapshots for balances)
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Marts |
mart_daily_sales ,mart_promo_lift_analysis
mart_stockout_risk ,mart_inventory_turnover
mart_basket_analysis ,mart_channel_performance
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Governance & lineage | dbt documentation/tests and exposures for BI datasets. Coalesce visual lineage across sources, models, and marts for impact analysis. |