What is etl (extract, transform, load)?
ETL is a data integration process that combines data from multiple sources into a single, consistent data store that is loaded into a data warehouse or other target system.
Understanding etl (extract, transform, load)
ETL stands for Extract, Transform, and Load. It is the traditional backbone of data integration and warehousing.
The process consists of three distinct steps: 1. Extract: Reading data from various source systems (databases, APIs, flat files). 2. Transform: Converting the extracted data into the format required by the target system. This includes cleaning, filtering, validating, and aggregating data. 3. Load: Writing the transformed data into the target destination (data warehouse, data lake, or operational database).
While traditional ETL was batch-oriented and server-heavy, modern "Reverse ETL" and "Client-side ETL" (like Ivandt) are shifting where and when these transformations happen.
Key points
Extract: Pulls data from sources
Transform: Cleans and standardizes data
Load: Puts data into destination
Critical for business intelligence and analytics
Modern trends include ELT (Load then Transform) and Real-time ETL
Frequently asked questions
What is the difference between ETL and ELT?
In ETL, data is transformed before loading. In ELT (Extract, Load, Transform), raw data is loaded into the destination first, and transformations happen there. ELT is popular with modern cloud data warehouses like Snowflake.
Is data import the same as ETL?
Data import is often the "E" and "L" of ETL, sometimes with some "T". However, ETL usually implies a more complex, automated, and ongoing pipeline between systems, whereas data import often refers to user-initiated uploads.
What are common ETL tools?
Traditional tools include Informatica and Talend. Modern cloud tools include Fivetran and Airbyte. For user-facing file imports (which act as mini-ETL pipelines), tools like Ivandt are used.