ETL
Definition
Extract, Transform, Load - a crucial data integration process that moves and transforms data from various sources into a data warehouse for analysis.
Use Cases
- Netflix: Ingesting and transforming large volumes of application and viewing telemetry for analytics and operational monitoring. — Netflix has publicly described building data pipelines on AWS that collect events, transform/aggregate them, and load curated datasets into analytics platforms for reporting and decision-making. (Faster, more reliable analytics on platform usage and performance, enabling data-driven product and operational decisions at scale.)
- The Home Depot: Consolidating data from stores and digital channels to support enterprise analytics and reporting. — The company has publicly discussed using cloud-based data platforms and pipelines to move and standardize data from multiple operational systems into centralized analytics environments. (Improved visibility across channels and more consistent reporting for business stakeholders.)
- Spotify: Processing event and log data to support analytics on user behavior and service performance. — Spotify has publicly shared its use of large-scale data processing pipelines where raw events are transformed into curated datasets for analysis and experimentation. (Better experimentation and insights into user engagement, supporting product iteration and reliability improvements.)
Provider Equivalents
- AWS: AWS Glue
- Azure: Azure Data Factory
- GCP: Cloud Data Fusion
- OCI: OCI Data Integration
Frequently Asked Questions
- What's the difference between ETL and ELT?
- ETL transforms data before loading it into the target system (like a data warehouse). ELT loads raw data first and then transforms it inside the target system using its compute (common with modern cloud warehouses). ETL is often used when you need strict data quality checks or when the target system isn’t designed for heavy transformations.
- When should I use ETL?
- Use ETL when you need to clean, validate, standardize, or mask data before it reaches the destination (for example, enforcing schemas, removing duplicates, applying business rules, or protecting sensitive fields). ETL is also a good fit when integrating many legacy sources or when downstream systems require curated, consistent datasets.
- How much does ETL cost?
- ETL cost depends on data volume, transformation complexity, frequency (batch vs near-real-time), connector/licensing needs, and where compute runs. Managed services typically charge for orchestration and/or processing time, plus underlying storage and network egress. Costs rise with large scans, complex joins, frequent runs, and high-throughput streaming.
Category: data
Difficulty: advanced
Related Terms
See Also