The Enterprise Data Challenge
Enterprises generate data across dozens of systems - CRMs, ERPs, marketing platforms, IoT devices, and custom applications. Turning this fragmented data into business insights requires robust data pipelines.
ETL vs ELT
ETL (Extract, Transform, Load) - Transform data before loading into the warehouse. Traditional approach, suitable when transformation logic is complex and data volumes are moderate.
ELT (Extract, Load, Transform) - Load raw data into the warehouse first, then transform using the warehouse's compute power. Modern approach enabled by cloud data warehouses (Snowflake, BigQuery, Redshift).
ELT is the modern default for most enterprises due to flexibility, scalability, and the ability to re-transform historical data without re-extraction.
Architecture Layers
Ingestion - Extract data from source systems via APIs, CDC (Change Data Capture), file exports, or streaming.
Storage - Land raw data in a data lake (S3, ADLS). Load into a data warehouse for analytics.
Transformation - Clean, denormalize, aggregate, and model data for consumption. Tools: dbt, Spark, or warehouse-native SQL.
Serving - Expose transformed data via BI tools (Tableau, Looker), embedded analytics, or APIs.
Orchestration - Schedule and monitor pipeline execution. Tools: Airflow, Dagster, or Prefect.
Data Quality
Build data quality checks into every pipeline stage:
Schema validation at ingestion
Null/duplicate detection during transformation
Row count reconciliation between source and warehouse
Freshness monitoring (SLAs for data availability)
Real-Time vs Batch
Batch - Most enterprise reporting can tolerate hourly or daily refreshes. Simpler and cheaper.
Real-time - Required for operational dashboards, fraud detection, and personalization. Use streaming pipelines (Kafka + Flink or Spark Streaming).
Conclusion
Start with batch ELT using dbt and a cloud data warehouse. Add real-time capabilities only for use cases that genuinely require sub-minute data freshness.