Extract Transform Load (ETL)- Best Platforms

ETL or Extract Transform Load Tools play a crucial role for enterprises to manage and handle their data from various data sources. Data Management and integration are very crucial in any medium to large organization and require specific tools that will help in better management and loading of data. These tools can be used for other purposes such as data cleansing, analysis data profiling, and data pipe-lining. Data Extract Transform Load tools also help other companies to provide Data as a Service (DaaS).

Data Extract Transform Load Platforms

Here are the top 5 ETL tools for your enterprise and businesses.


ETL is developed so that data integration across can be done effectively and efficiently than the traditional data warehouses. Advanced ETL tools like SAS allow us to load and convert structured and unstructured data into a given platform. SAS Data Management Tool can be used for many purposes. SAS ETL provides capabilities to transform, manage, secure and integrate data from legacy systems including cloud based sources. Some great features are Decision Management, SAS Big Data Metabridge, ETL pipelines support, decision management, jobs processing & scheduling, SAS Hadoop Loader, single development environment for all tools. Apart from that, SAS is very secure and comes with metadata management.

 Oracle Enterprise Data Quality

Oracle Enterprise Data Quality is a great tool for various data operations including ETL. Oracle Enterprise Data Quality is useful in various cases where large data is to be loaded, analysed as well as loaded into a different environment. EDQ also provide intra platform dependencies under the Oracle umbrella. Some important features and benefits of EDQ include its ability to handle different types of data files, multi-user project support, JDBC and ODBC Connection., Integration of SQL Commands to retrieve data from a database, project tracking and single a repository and a Service-oriented architecture (SOA).

IBM InfoSphere DataStage

Data integration platform comes with offerings that help you understand, cleanse, monitor, and transform data. InfoSphere Information Server provides massively parallel processing (MPP) capabilities for a highly scalable and flexible integration platform that handles all data volumes, big and small. With InfoSphere, many critical tasks such as extracting information from multiple sources, direct connection with enterprise applications, easy deployment due to prebuilt functions can be achieved.

AWS Glue

AWS Glue is a specially made tool for Extract Transform and Load data operations, which is a part of Amazon Web Services. AWS Glue is also provided great ETL solutions. Glue is easy to use and can be setup for data processing and setup using the minimal infrastructure. AWS Glue can extract data from the Amazon Web Services platform to incorporate into data warehouses and lakes. Many APIs are also included that helps it to integrate and monitor processing jobs. Job schedulers are easy to use and data to be loaded into Amazon S3/Redshift. Some important benefits that we get from AWS Glue include high fault tolerance, filtering, support, cost-effective, and maintenance. Other core features of AWS Glue include Automatic Schema Discovery, Auto code generation, and parallel job scheduling.

Azure Data Factory

Microsoft Data Factory is a great server-less tool for all ETL operations with the integration of Microsoft Azure Cloud Infrastructure. It provides a complete package of data integration services. Azure boasts an integrated platform that is fully managed with data solutions such as data preparation, transformation, and scaling. It also provides data warehousing capabilities. It is fast and reliable when it comes to connecting to multiple data sources to transform and process data. This makes not only the product delivery easy but also makes the whole operation super-efficient. Azure Data factory can also be used to migrate data with real-time scenarios.

Wrapping it up..

Data Extract Transform Load tools are ideal for managing any business data and are can be used for loading data into a specific environment or application. These tools are built by companies that focus on a wide range of enterprise applications, so these can be used to provide inter-application data transfer as well as data extraction capabilities. The above tools are also used by many service-oriented firms to provide data solutions to other companies in domains such as banking, manufacturing, etc.

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