Your Comprehensive Guide To Understanding ETL Migration


Translating to Extract, Transform and Load, ETL data migration involves three core processes that extract data from the source system, transform it is based on various concatenations, and eventually, leverage a new data warehouse system to load it.

ETL is a complex process and involves numerous steps along with expert proficiency. From developers to analysts, testers to technicians, a skilled technical team is required to carry out these far from simple processes. A lot of organizations also depend on renowned third-party data migration services for ETL data migration. Moreover, it is a recurring process and changes with the ever-growing business requirements.

Every organization on this globe looks forward to an efficient data migration process, which also doesn’t burn a hole in their pockets. ETL data migration offers numerous benefits to the organizations. From analyzing their core data to making it more efficient, it assists them in making crucial business decisions and takes their business a notch higher.

Understanding the three core processes: Extract, Transform and Load

Before you go forward with ETL data migration for your business, it is essential to know more about the core processes involved in it.

Check below:

·         Extract: When we say ‘extract,’ we refer to a process that includes the collection of various types of data currently present in a system. This data can be extracted from one or multiple sources. In other words, a process that involves the extraction of data from one or more systems is known as ‘Extract’ in ETL data migration.

·         Transform: Once the data has been extracted and then collected, several advantageous variations are introduced to it by applying efficient methodologies. After the transformation process gets completed, the older data gets converted to a new form, followed by setting a custom standard in accordance with varies business requirements.

‘Transform’ majorly involves processes like data mapping, cleaning, and other alterations.

·         Load: The ‘transformed’ data is then loaded to the target warehouse. It is a process in which the data is written into a data lake or warehouse.

There are mainly three types of loading:

Initial Load: Here, all the data warehouse tables are filled.
Incremental Load: If any changes are required, they are applied here.
Full Refresh: Deleting the not so useful data from the existing tables and refreshing it.

Most often, people use ETL in correlation with the data warehouse. However, it is not just limited to data warehousing but also embraces various crucial components, including data lakes and data marts and the migration of data between them and other applications.  

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