At a very basic level, ETL is a process in which data from disparate sources is extracted from a source database, transformed (or processed), and then loaded onto a target store or data warehouse.
Over the years, the way ETL works, and the business functions it serves have gone through fundamental changes to reach the current stage of its evolution – a highly effective technique to process and integrate real-time data stored across multiple entities.
Traditional ETL systems were developed in the early 1970s, when large organizations needed to integrate data generated by their different departments like sales, inventory, and marketing, and use this data to gain insights into their business and operations.
Another challenge was that data could only be moved in batches, which meant that large volumes of data could be moved only when network traffic was low.
It is now possible to query and process voluminous data in real-time from multiple sources that need to be tracked.
Modern ETL also supports most cloud-based operations which fetch and process data – today’s ETL solutions are flexible enough to handle multiple data sources along with both structured and unstructured data.
Code free and hassle-free ETL tools.
Ability to create aggregate and join on data warehouse for faster query processing.
SSIS import/export wizard helps to move data from source to respective destination.
SSIS has inbuilt scripting environment available for writing programming code with efficiency.
It acts as a fulcrum for data warehousing, integration, and profiling.
Data flow inculcates user to send, receive, transfer, filter, and move data.
