- VP of Operations
Executive Summary
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.
Business Challenges
- Fragmented open-source stack causing high operational burden (cluster management, schema drift, fragile jobs).
- Delayed insights due to nightly batches; limited visibility into fast-changing POS and inventory signals.
- Seasonal demand spikes leading to performance bottlenecks and ad-hoc scaling efforts.
- Inconsistent governance and testing eroding trust in KPIs.
Objectives
- Centralize and standardize data with near–real-time freshness for POS and inventory.
- Establish robust data quality, lineage, and access controls.
- Reduce operational toil and simplify schema evolution and change management.
- Enable flexible, cost-efficient scaling and faster analytics development cycles.
Architecture
Legacy Source Systems
Streaming events
- Apache Kafka (POS transactions, inventory updates, clickstream)
Operational data
- Apache Cassandra or HBase (high-write operational entities)
Historical data
- Hive tables on HDFS (24 months of sales, product, and store data)
Auxiliary sources
- CRM, ERP, and e-commerce platforms connected via Fivetran
Migration Strategy and Phases
Phase 1: Discovery and Planning
- Inventory Kafka topics, Cassandra/HBase schemas, and Hive databases/tables.
- Establish SLAs by domain :
- POS : sub-15 minutes freshness
- Inventory : 15–60 minutes
- Product master/pricing : daily
- Define governance standards : naming conventions, business keys, PII classification, and retention.
- Environments : Separate Dev/Test/Prod Snowflake accounts; dbt and Coalesce projects aligned to domains.
Phase 2: Landing and Ingestion
- Configure Fivetran connectors :
- Direct connectors where available; for Kafka, use supported patterns or export to cloud object storage with Snowpipe auto-ingest.
- Historical backfill from Hive to cloud object storage in columnar format (e.g., Parquet) for efficient bulk load.
- Raw zone in Snowflake :
- Fivetran-managed schemas per source, preserving metadata and load history.
- Enable Time Travel with appropriate retention for recovery and validation.
- Initial validation :
- Row counts and aggregate checks between source extracts and Snowflake raw tables.
- Monitor connector lag and load failures with alerting.
Phase 3: Modeling and Transformations
- Approach :
- dbt for code-driven transformations and tests; Coalesce for visual development, standardization, and lineage across complex domains.
- Logical layers : raw (source), staging (stg_), core (conformed dims/facts), marts (subject-area).
- Data quality :
- dbt tests : unique, not_null, accepted_values, referential integrity.
- Freshness checks for POS and inventory sources with SLAs.
- Incremental modeling :
- dbt incremental patterns for large facts (e.g., fact_sales, fact_inventory_positions) with late-arriving data logic.
- Use Snowflake features (streams/tasks or model predicates) to minimize compute for incremental loads.
Phase 4: Orchestration and CI/CD
- Scheduling :
- Align Fivetran sync schedules to source SLAs.
- Chain dbt Cloud jobs after ingestion; separate jobs for staging/core and marts to isolate failures.
- CI/CD :
- Pull requests trigger dbt build + tests in ephemeral environments.
- Coalesce deployments coordinated with dbt; approval workflows for production releases.
Phase 5: BI Enablement and Adoption
- Power BI :
- Executive dashboards : Sales performance, promotion effectiveness, store rankings, margin trends.
- Operational dashboards : POS pulse (last 15 minutes), inventory health and stock-out risk, price compliance.
- Configure secure connectivity to Snowflake; implement row-level security aligned to store/region roles.
- Data documentation and trust :
- dbt docs and exposures link marts to BI assets, owners, and SLAs.
- Coalesce lineage enables impact analysis and accelerates change management.
Phase 6: Hardening, Security, and Cost Governance
- Security and access :
- RBAC roles (e.g., SYSADMIN, ETL, ANALYST, BI_SERVICE).
- Dynamic data masking policies for PII; audit logs for data access.
- Private networking and secret management per platform capabilities.
- Reliability :
- Define RPO/RTO targets; leverage Time Travel for point-in-time recovery.
- Periodic restore tests and failover runbooks.
- FinOps :
- Resource Monitors with spend caps and alerts.
- Separate virtual warehouses for ingestion, transformation, and BI; auto-suspend/auto-resume.
- Optimize Fivetran MAR (monthly active rows) with connector configuration and partitioning.
- Schedule dbt jobs to reduce contention with peak BI usage.
Data Model Blueprint
| 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)
|
|---|---|
| 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)
|
| Marts |
mart_daily_sales,mart_promo_lift_analysis
mart_stockout_risk,mart_inventory_turnover
mart_basket_analysis,mart_channel_performance
|
| Governance & lineage | dbt documentation/tests and exposures for BI datasets. Coalesce visual lineage across sources, models, and marts for impact analysis. |
Validation & Cutover Plan
Historical reconciliation
- Load 24 months of sales and inventory; reconcile day/store/category totals to within ±0.5%.
Parallel run
- Operate new and legacy pipelines in tandem for 2–4 weeks.
- KPI parity checks for daily sales, top SKUs, inventory deltas, and promo impact.
Cost efficiency
- 40–55% reduction in data operations overhead through managed ingestion, ELT patterns, and workload-isolated compute.
Gradual switch
- Redirect Power BI datasets to Snowflake marts domain-by-domain.
- Keep legacy read-only for one cycle before decommission.
Results & KPIs
Freshness and performance
- POS dashboards refreshed in 5–15 minutes; inventory within 15–60 minutes; product/pricing daily.
- 30–50% faster time-to-insight and quicker iteration on analytics use cases.
Elasticity and reliability
- Independent scaling for ingestion, transformation, and BI; stable performance during promotions/holidays.
- Time Travel and documented recovery procedures increase resilience.
Cost efficiency
- 40–55% reduction in data operations overhead through managed ingestion, ELT patterns, and workload-isolated compute.
Data quality and trust
- 95% pass rate on critical dbt tests; freshness SLOs met >99% of days.
Minimal Downtime & Maximum Confidence
- Achieved near-zero downtime cutovers through parallel run, domain-by-domain switchovers, automated tests, and Time Travel–backed rollback.
Tech Stack
Snowflake
- Elastic compute isolation via virtual warehouses, separation of storage and compute, Time Travel, secure data sharing, and robust governance features.
Fivetran
- Managed connectors and scheduling reduce pipeline maintenance; predictable costs with MAR-based pricing.
dbt
- Transformation-as-code with built-in testing, documentation, and CI/CD for reliable analytics engineering.
Coalesce
- Visual modeling, standardization, and end-to-end lineage at scale; accelerates development and simplifies impact analysis.
Power BI
- Widely adopted enterprise BI with strong governance and security, ideal for executive and operational reporting.
Operational Runbook
Daily
- Review Fivetran connector health; inspect dbt test results and freshness.
- Monitor Snowflake usage and right-size virtual warehouses.
Weekly
- Validate KPI parity and address user feedback on Power BI dashboards.
- Review lineage changes in Coalesce for upcoming releases.
Monthly
- FinOps optimization; schema evolution captured via dbt/Coalesce change requests.
Quarterly
- Recovery drills using Time Travel; security and access reviews; model performance benchmarking.
Future Enhancements
- Real-time alerting for stock-out risk and promo anomalies driven by near–real-time marts
- ML integration for demand forecasting using features sourced from curated marts.
- Secure data sharing with logistics and vendor partners for collaborative planning and replenishment.
Team Composition
- Project Manager: 1
- Data Architects: 1
- Cloud Engineer: 1
- QA Analyst: 1
- Support Engineer: 1