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What is ETL?

What are ETL Tools?

ETL is the process of transferring data from the source database to the destination data warehouse. In the process, there are 3 different sub-processes like E for Extract, T for Transform, and L for Load. The data is extracted from the source database in the extraction process which is then transformed into the required format and then loaded to the destination data warehouse. For performing all these functions there are certain tools that are called the ETL tools.

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Mention List of ETL Tools

The lists of the ETL tools are given below.
• Open Text Integration Center
• Relational Junction ETL Manager (Sesame Software)
• CloverETL
• PowerCenter Informatica
• Talend Studio for Data Integration
• Oracle Warehouse Builder (OWB)
• Oracle Data Integrator (ODI)
• Data Migrator (IBI)
• Cognos Data Manager
• IBM Infosphere Warehouse Edition
• SQL Server Integration Services (SSIS)
• IBM Infosphere Information Server
• Pervasive Data Integrator
• Pentaho Data Integration
• 19Adeptia Integration Server
• 4SAS Data Management
• 16Centerprise Data Integrator
• 20Syncsort DMX
• 10Sagent Data Flow
• 21QlikView Expressor
• 2SAP Data Services
• 6Elixir Repertoire for Data ETL
What is ETLSelection of the ETL Tools for your Data warehouse Tasks and its Importance
Go to the ordering of the ETL tools & data integration survey in Google and find out the best ETL tool for your enterprise. You can find the best ETL tools suitable for your organization in this survey. To experience a successful ETL functioning, proper selection of the ETL tools is almost mandatory. The proper transfer of data between databases is possible only when we have the proper tools for the process. If the procedure is done with less appropriate tools, then there will be a problem in the functioning of the complete transfer process. For proper functioning of data, you will have to take care of the sub-processes and each tool used in the sub-processes. Hence, carefulness has to be maintained by a proper selection of the ETL tools.

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A lot of ETL tools are developed formerly in order to turn the tasks of data warehouse professionals simpler and more enjoyable. Without the ETL tools, people working in the data warehouse department did all the extraction, transformation, and loading processes manually. With the coming of the ETL tools, professionals started finding their job easier because all that they have to do is to learn how to make use of the ETL tools.  In the present technology world, the use of the ETL tools is not only bounded within the use of the data warehouse but also in the field exterior to it. ETL tools are vastly used in the Areas of filtering, cleansing, and profiling of data and also in EAI, processing of huge sets of data through the help of data analytics, controlling the flow of data from node to node, and also in the data management.

Become a Business Intelligence Architect

The ETL tools are mainly related to performing the processes that appear during the transmission of data between databases. When the data reaches the data warehouse it needs to be analyzed for which we face the need for a newer technology called as business intelligence.

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How ETL works

Acronym of Extract, Transform and Load, ETL is the core process for building and working with the data warehouse. Right from pulling the data from multiple data sources to storing it in the final data warehouse in the most integrated form, ETL takes care of each and every movement and processing of data from source to destination. This ETL sequence also comprises the cleaning of data after extraction.


The first step is to pull or dig out data from multiple/heterogeneous desired sources and applications, making it available for further processing. During data extraction, it is not decided as to what actual or relevant data is required by the end-user and thus, more data than needed is extracted and the filtering is performed later. However, some sources like operational systems allow some transformations even during the extraction phase.

More importantly, the extraction process should be carried out in a way that does no harm to the original data source with respect to performance and response time. There are two key ways to perform the extract step.  The type of extraction that users choose relies on the source system and the destination business requirements in the warehouse.

Full Extraction:

When the systems are not able to recognize what data needs have been modified, full extraction is the only way to mine data. It gives back all the data stored in the data source and thus, there isn’t any necessity of tracking the changes in the data source since the last extraction. For instance, a SQL statement presenting the complete table with all records and values.

Incremental Extraction:

While some source systems are capable of identifying the changes made in the DB, they allow extraction of the modified data either in the form of a new changing table or a source application column. This approach requires keeping a track of data source changes performed after the last extraction.

Not many of the data warehouses allow transformations during the extraction phase but promote full extraction, especially when dealing with massive amounts of data.


Next is the transformation step, which implements some rules on the extracted data to transform data as per requirements. In simple terms, it is the application of various data queries and functions on the current DB to fetch a required ordered set of records, with no redundancy. The transformation operations include joining data from two different data sources, aggregating, concatenating, sorting, and many advanced validation rules using SQL SELECT statements.


The last step is loading the cleansed and transformed data to the data warehouse for further analysis. While you are allowed to make alight transformations during the loading if required, it is advised to perform and complete them before the loading process. The efficient and successful loading of data to the destination DB ensures data integrity and appropriateness.

Although the ETL process looks straightforward, some of them may fail due to missing extracts or misusing values in reference tables, or simply a connection failure. Hence, it is significant for the ETL tool to maintain referential integrity and implement fault-tolerance while the data is loaded to the data warehouse.

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About the Author

Data Analyst & Machine Learning Associate

As a Data Analyst & Machine Learning Associate, Nishtha uses a combination of her analytical skills and machine learning knowledge to interpret complicated datasets. She is a passionate storyteller who transforms crucial findings into gripping tales that further influence data-driven decision-making in the business frontier.