Data Engineering

ETL (Extract, Transform, Load) transforms data before loading into the warehouse - traditional approach for on-premise systems. ELT (Extract, Load, Transform) loads raw data first, then transforms in the warehouse using tools like dbt - modern cloud approach leveraging warehouse computing power. ELT is generally better for cloud data warehouses like Snowflake and BigQuery as it's more flexible and cost-effective.

We implement multi-layer data quality checks: schema validation at ingestion, business rule validation during transformation, statistical anomaly detection, data reconciliation between source and destination, and automated testing. Tools like Great Expectations, dbt tests, and custom validation logic ensure data accuracy and completeness with automated alerts for quality issues.

Snowflake offers excellent performance, ease of use, and multi-cloud support - best for enterprise needs. BigQuery is ideal for Google Cloud users with serverless architecture and competitive pricing. Redshift works well for AWS-centric organizations. The choice depends on your cloud strategy, team skills, existing investments, query patterns, and budget. We analyze your requirements to recommend the optimal solution.

We use stream processing frameworks like Apache Kafka for event ingestion (handles millions of events/second), Apache Flink or Spark Streaming for processing logic, and appropriate sinks (databases, data warehouses, caches). Architecture includes horizontal scaling, partitioning strategies, checkpointing for fault tolerance, and monitoring. We optimize for both throughput and latency based on use case requirements.

We implement comprehensive data governance including data cataloging (DataHub, Amundsen), lineage tracking, access controls (RBAC), encryption at rest and in transit, audit logging, data masking/tokenization for PII, and retention policies. For compliance (GDPR, HIPAA, SOC2), we provide documentation, implement right-to-deletion, data anonymization, and regular compliance audits.