Extract, transform, and load (ETL) improves business intelligence and analytics by making the process more reliable, accurate, detailed, and efficient.
Historical context
ETL gives deep historical context to the organization’s data. An enterprise can combine legacy data with data from new platforms and applications. You can view older datasets alongside more recent information, which gives you a long-term view of data.
Consolidated data view
ETL provides a consolidated view of data for in-depth analysis and reporting. Managing multiple datasets demands time and coordination and can result in inefficiencies and delays. ETL combines databases and various forms of data into a single, unified view. The data integration process improves the data quality and saves the time required to move, categorize, or standardize data. This makes it easier to analyze, visualize, and make sense of large datasets.
Accurate data analysis
ETL gives more accurate data analysis to meet compliance and regulatory standards. You can integrate ETL tools with data quality tools to profile, audit, and clean data, ensuring that the data is trustworthy.
Task automation
ETL automates repeatable data processing tasks for efficient analysis. ETL tools automate the data migration process, and you can set them up to integrate data changes periodically or even at runtime. As a result, data engineers can spend more time innovating and less time managing tedious tasks like moving and formatting data.