Enterprise-grade ELT/ETL with CDC, streaming, and dbt — designed for SLAs
From sources to lakehouse/warehouse with tests, lineage, and observability so data is trustworthy and on time.
Outcomes
- Data freshness from hours → minutes with defined SLAs and alerting
- Versioned, testable transforms (dbt) and documented lineage
- Automated quality gates (DQ checks) and incident runbooks
- Cost/performance benchmarks with capacity forecasts
What we deliver
- Design docs (HLD/LLD), data contracts, and naming conventions
- Batch & streaming pipelines (Airflow/Dagster, Kafka) with retries & backfills
- ELT with dbt to Snowflake/BigQuery/Databricks; CI for tests
- CDC from OLTP (Debezium/Fivetran/Meltano) with ordering & idempotency
- Data quality & lineage (Great Expectations/OpenLineage)
- Dashboards & on-call runbooks for pipeline SLOs
Process
- Discover sources, SLAs, privacy requirements
- Design target models & orchestration strategy
- Build pipelines, dbt models, and tests
- UAT, load tests, and phased cutover
- Operate with SLOs, observability, and optimizations
FAQs
ETL vs ELT?
We prefer ELT for speed and traceability: land raw, transform with dbt, and keep lineage and tests close to the code.
How do you protect PII?
Field-level classification, masking/tokenization at ingest, role-based access, and audit trails with data contracts.
Can you migrate without downtime?
We use dual-write/dual-read phases, backfills, and feature flags to minimize downtime and risk.