etl

ETL stands for Extract, Transform, Load, a data integration approach used to move data from multiple source systems into a centralized repository such as a data warehouse or data lake. The extract step retrieves data from heterogeneous sources, the transform step applies cleansing, normalization, deduplication, aggregation, and business rules, and the load step writes the processed data to the target system.

Historically, ETL occurred as a batch process scheduled during off-peak hours, often using dedicated ETL tools. In cloud and modern architectures, ELT (extract, load, transform) has gained prominence, especially when the target supports powerful processing, enabling transformations to run inside the data warehouse or data lake engines.

Typical sources include relational databases, files, APIs, and streaming platforms. Transformations can include cleaning, type casting, joining, pivoting, and calculations. Processes may support incremental loading and change data capture to minimize data transfer and keep targets updated.

Deployment can be on-premises, in the cloud, or hybrid. ETL can be batch-oriented or near real-time when paired with streaming technologies. In ELT, data is loaded first and transformed afterward inside the target system.

Data quality and governance considerations include validation, lineage, metadata, auditing, and error handling, as well as monitoring and exception management to ensure reliability.

Common tool categories include commercial platforms such as Informatica, IBM DataStage, and Microsoft SQL Server Integration Services; open-source projects such as Apache NiFi and Apache Airflow; and cloud-native services from major providers.