ETL stands for Extract, Transform, and Load. An ETL procedure is a combination of these three disparate data management workflows into a single platform. ETL analytics software is essentially used by data warehousing teams to unify the three processes into a consolidated, usable / reusable format. In this article, we will focus on the top trending ETL software tools that you can be used to build a project with an analytics training institute in Bangalore, Hyderabad, and Gurgaon.

Let’s start…


Hevo is a fully managed no-coding ETL tool for data warehousing and data sourcing techniques. It is built on Python programming base, so if you learn the basics of Python, you can further enrich the Hevo Data suite and workflows to automate cleaning, transforming, and enriching data schema before moving to the warehouse.

Microsoft Azure HD Insight

Microsoft Azure provides one of the strongest foundations of ETL pipelines. ETL analysts often choose Azure HDInsight to understand the flow of data from various sources, and train AI ML tools to take care of the other two operations, that is – Transform and Load.

The intermediate processes in data transformation with the Azure ETL tool involve filtering, sorting, aggregating, and splicing of data. These steps can be fully understood by further focusing on analytics training courses involving HDFS and Azure Data Lake Store. Technologies like PolyBase, Hive, and Spark can be used as SQL to read directly from open-source data storage, before moving it to the proprietary storage platforms. Therefore, it gives additional authority on custom built ETL projects, used extensively in building Big Data Business Intelligence suites.


A very powerful Big Data ETL tool for custom Cloud setups, Talend has carved a niche for itself in the Cloud computing domain. Analysts can learn Talend management from its wide array of project documents and resources published on various open source and enterprise platforms.

Talend Data Fabric is a powerful Cloud transformation suite for ETL management teams that enables users to intensify and accelerate the adoption of multi-cloud environments at any point, anytime with limited resources.

Big Data analytics teams in healthcare, transport, logistics, Marketing and Sales, Manufacturing, and Government sectors extensively use Talend to simplify ETL / ELT during the adoption of Data Science applications and procure intelligence to master complex data warehousing challenges.


Personally, I am a big fan of Elasticsearch. A JSON driven ETL for big data and analytics operations, Elasticsearch can handle queries in multiple content types and data formats. Easy to integrate with both Python and non-Python programming languages like Java, C, NET, and Groovy, this ETL management software is a top draw for a majority of AIOPs and Machine Learning Operating System providers around the world.

There are other options – R, IBM SPSS, Informatica, Amazon RedShift, Jaspersoft, JBoss, and Content ETL. If you are looking to build an ETL for Cloud Security and Data Recovery operations, Elasticsearch-Hadoop combination is the most preferred option in the analytics domain.