What Is ETL And Its Data Integration Methods?
ETL (extract, transform, and load) is a data integration technique that combines data from multiple sources into a single, consistent data store, which is subsequently loaded into a data warehouse or other target system.
As databases became more prominent in the 1970s, ETL was introduced as a mechanism as the principal method for integrating and loading data for calculation and analysis
for data processing in data warehousing operations.
ETL And Data Integration Methods
ETL and ELT are only two data integration strategies; additional methodologies are also utilised to help with data integration operations. Some examples are:
1)Change Data Capture (CDC) identifies and captures the altered source data before transferring it to the target system. The CDC can be used to save resources needed during the ETL "extract" step and separately to move transformed data into a data lake or other repository in real-time.
2)Data replication replicates modifications in data sources to a central database in real-time or in batches. Data replication is frequently mentioned as a data integration approach. In reality, it is most commonly used to create backups for disaster recovery.
3)Data virtualisation employs a software abstraction layer to generate a unified, integrated, and fully usable representation of data—all without physically copying, changing, or loading the source data to the target system. Data virtualisation capabilities allow a company to build virtual data stores, data lakes, and data marts for data storage from the same source data without incurring the price and complexity of establishing and administering separate platforms for each. While data virtualisation can be used with ETL, it is increasingly viewed as a viable option for ETL and other physical data integration approaches.
4)Stream Data Integration (SDI) does exactly what it says: it continually absorbs real-time data streams, transforms them, and feeds them into a destination system for analysis. The crucial term here is "constantly." SDI integrates data continuously as it becomes available rather than integrating snapshots of data taken from sources at a specific moment. SDI permits the creation of a data repository that can be used to fuel analytics, machine learning, and real-time applications for better customer experience, fraud detection, and other purposes.
Thus, some ETL integration methods are changing data capture, replication, virtualisation and stream data integration. To know more about ETL, join Etl Testing Online Training at FITA Academy.
