« Back to Glossary Index

What is the meaning of ETL migration?

ETL represents Extract, Transform and Load, which is a cycle used to gather data from different sources, change the data relying upon business rules/needs and burden the information into an objective data set. Data migration is the way toward moving information starting with one framework then onto the next. While this may appear to be clear, it includes an adjustment away and data set or application. With regards to the concentrate/change/load (ETL) measure, any data migration will include in any event the change and burden steps.

What is the difference between data migration and ETL?

Data migration and ETL are fairly comparable in that they include moving data starting with one source then onto the next. In any case, data migration doesn’t include changing the arrangement, while ETL does (that is the reason there is “extracted” in its name). As referenced before, ETL and data integration are both utilized when associations need to get more out of the data they have. Be that as it may, data integration doesn’t include changing data, all things considered.

What are the examples of ETL migration?

There are many ETL examples but the few important ones are listed below:

ETL in Data warehousing: The most well-known illustration of ETL will be ETL is utilized in Data warehousing. The client needs to bring the historical data just as current data for building up the data warehouse. The Data warehouse data is only a mix of historical data just as transactional data. Its data sources may be unique. The client needs to bring the data from various heterogeneous frameworks and burden it into a solitary objective framework which is likewise called a data warehouse.

As The ETL definition proposes that ETL is only Extract, Transform, and loading of the data; this interaction should be utilized in data warehousing broadly. A basic illustration of this is managing sale data in a shopping center. On the off chance that the client needs the recorded data, just as current data in the shopping center initial step, is consistently the client needs to follow the ETL cycle. At that point, that data will be utilized for announcing purposes.

Data Integration using Merger process: Presently a day’s large associations are gaining little firms. The data sources for the various associations might be extraordinary. We need to incorporate the data starting with one association then onto the next association. These sorts of mixed projects need the ETL cycle to separate the data change the data and load the data.

Third Party Data Management: The big associations consistently give diverse application improvements to various kinds of vendors. This implies not a solitary seller is overseeing everything. How about we take the case of a Telecommunication project where charging is overseen by one organization and CRM is overseen by another organization. On the off chance that CRM Company needs some data from the organization that is dealing with the Billing, that organization will get a data feed from the other organization. To stack the data from the feed ETL measure is utilized.

These are the important examples of ETL migration in which you can easily understand what ETL migration is.

What are the important ETL tools and their use?

Choosing the ETL tool is troublesome. You need to think about a lot of variables while picking the right ETL tool as indicated by the venture. Choosing the ETL tool for a particular task is an exceptionally essential move even you need it for a little venture. Ensure that ETL tool movements are no little endeavors.

  1. Data connectivity: The ETL tool ought to speak with any wellspring of data regardless of where it comes from. This is basic.
  2. Performance: Moving and changing data requires some genuine handling power. So you need to check the presentation factors. Transformation.
  3. Flexibility: Coordinating, Merging, and changing the data is basic. ETL data ought to give these and numerous change bundles which permit adjustments to the data in the change stage with basic simplified.
  4. Data Quality: Your data isn’t perfect. The best way to use your information when your data is reliable and clean.
  5. Flexible data Acquisition options: When the ETL is ready you need to watch that ETL will deal with past data just as new coming data.
  6. Committed ETL Vendor: You are playing with the association data while doing the ETL interaction. So pick a vendor who is very notable in the business and whose help is truly extraordinary.

These are distinctive list items you need to recollect while picking your ETL tool. Expectation you will find out about the ETL definition just as various genuine instances of ETL.


A typical ETL measure gathers and refines various kinds of information, at that point conveys the data to a data warehouse like Redshift, Azure, or Big Query. ETL likewise makes it conceivable to migrating data between an assortment of sources, objections, and investigation devices. Accordingly, the ETL process plays a basic part in delivering business knowledge and executing broader data management strategies.