ETL stands for Extract, Transform, Load, a process used to move and manage data between systems. It is a crucial part of data migration, ensuring that data is transferred efficiently, accurately, and in a usable format.
ETL is used to move data from outdated databases to modern systems (e.g., Oracle → PostgreSQL).
Helps transfer data from on-premise databases to cloud platforms like AWS, Azure, or Google Cloud.
Combines data from multiple sources into a single database or data warehouse.
Moves customer records, financial transactions, and other critical data to new ERP, CRM, or HR systems.
| Feature | ETL (Extract, Transform, Load) | ELT (Extract, Load, Transform) |
|---|---|---|
| Process Order | Transform before loading | Load data first, then transform |
| Best For | Traditional databases | Cloud-based systems (Big Data) |
| Speed | Slower due to transformation before loading | Faster, as transformation is done after loading |
| Examples | Informatica, Talend, Apache Nifi | Google BigQuery, Snowflake |
* Apache Nifi – Open-source ETL for real-time data migration.
* Talend Data Integration – A powerful tool for cloud and database migrations.
* Informatica PowerCenter – Enterprise-grade ETL for large-scale migrations.
* Microsoft SSIS – Best for SQL Server migrations.
* AWS Glue – A serverless ETL tool for AWS cloud migration.
* Ensures Data Quality – Cleans and standardizes data before migration.
* Automates the Process – Reduces manual effort and human errors.
* Handles Large Datasets – Works well for high-volume data migration.
* Improves Performance – Transforms data efficiently before storing it.
* Ensures Compliance – Helps meet GDPR, HIPAA, and other data regulations.