ETL
ETL is a data integration process that extracts data from multiple sources, transforms it into a usable format, and loads it into a target system, such as a database or data warehouse. It is a cornerstone of data warehousing and analytics workflows, enabling organizations to consolidate and analyze data effectively.
Also known as: Data pipeline, ETL process.
Comparisons
- ETL vs. ELT: In ETL, data is transformed before loading; in ELT, transformation occurs after loading into the target system.
- ETL vs. Data Integration: ETL is a specific method of data integration focused on preparation for analysis.
Pros
- Centralized data: Aggregates data from diverse sources into a single repository.
- Improved data quality: Cleans and transforms data for accuracy and consistency.
- Supports analytics: Prepares data for meaningful analysis and reporting.
Cons
- Time-consuming: Complex data transformations can slow down processes.
- Costly to scale: Requires significant resources for large datasets.
Example
A company consolidates customer data from multiple sources into a centralized database for reporting:
- Extract: Pull data from sources like CRM systems, sales platforms, and Excel files.
- Transform: Cleanse and standardize the data (e.g., fixing inconsistent date formats or removing duplicates).
- Load: Insert the cleaned data into a data warehouse for analysis and visualization using BI tools.
This process ensures the company has reliable, accurate, and actionable data for decision-making.