What is the difference between ETL and ELT pipelines?
ETL transforms data before loading it into a warehouse; ELT loads raw data first and transforms it inside the warehouse using tools like dbt on BigQuery or Snowflake. We recommend ELT for most cloud-native stacks because it preserves raw history, scales with warehouse compute, and keeps transformations version-controlled in SQL.
How long does it take to build a production data pipeline?
A single well-scoped pipeline typically ships in 2–4 weeks including orchestration, tests, and monitoring. Full platform builds with multiple sources, CDC, and quality gates usually run 8–14 weeks. We deliver in weekly sprints with demos, so value lands before the final milestone.
Which orchestration tool do you recommend — Airflow, Dagster, or Prefect?
Apache Airflow remains the default for mature teams and managed options (Cloud Composer, MWAA). Dagster suits teams that want asset-based lineage and strong local testing; Prefect favours dynamic, event-driven flows. We are production-experienced in all three and recommend based on your team's skills and cloud.
Can you migrate our legacy SSIS or Informatica jobs?
Yes. We refactored a major financial institution's SSIS estate to PySpark, migrating 10TB+ with 100% data integrity and cutting nightly processing from 10 hours to under 120 minutes. We use parallel-run validation so the legacy and new pipelines are reconciled before cutover.
How do you guarantee data quality in pipelines?
Every pipeline ships with Great Expectations (or dbt tests) as contract checks, quality gates in CI/CD, lineage tracking, and SLA alerting. Bad data is quarantined to dead-letter storage instead of silently propagating downstream.