Organizations use ETL (Extract, Transform, and Load) to obtain quality data for expediting decision-making. But, the myriad of available ETL tools makes it challenging for organizations to evaluate and embrace the right tool. Today, ETL tools are divided into various types, making it even more difficult for companies to find the right fit. In this article, we will discuss the different types of ETL tools and some of the use cases of the tools to help you evaluate the right ETL tool for your business.

What are ETL Tools?

Types of ETL Tools: ETL
Image Source

ETL automation tools are the software that helps you perform ETL processes, where the data is extracted from a wide range of sources, transformed, and then loaded into a target database or a data warehouse. 

  • Extract: In its first stage, ETL tools help extract data from any source, like proprietary databases, cloud environments, CRM systems, and more. Organizations often use tools with user-friendly interfaces that allow them to extract data from various sources easily. Extraction can be done through APIs, SQL queries, different types of ETL automation tools, and more. 
  • Transform: After extraction, ETL tools transform raw information to enhance data quality. Generally, the transformation includes several steps for molding the data according to the business requirements. Businesses may perform various transformation processes like data cleansing, standardizations, removing duplicates, sorting, and more with the help of ETL tools.
  • Load: After transforming the data, organizations load the data into the destination system. This data is then connected with powerful business intelligence tools such as Google Analytics, Tableau, Power BI, and more for analysis. 

Imagine an ed-tech organization that uses several marketing and sales strategies to promote its courses. Usually, the company will have to rely on several marketing platforms like LinkedIn Ads, Facebook Ads, Google Ads, Google Analytics, and more. Data generated from campaigns on these platforms can be brought together using ETL tools and stored in a data warehouse. 

Centralizing data would allow analysts to analyze the ROI of different campaigns, optimize campaigns, and more. However, since data on these platforms are recorded in various formats, the company needs to ensure they have proper transformation steps before storing the data. This allows the standardization of data before it is centralized. The edtech organizations can go a step further and collect the data from its CRM and club it with the marketing and website engagement data to profile users. 

For instance, website engagement data of users can be tracked and shared with the marketing and sales team. The marketing team can send personalized offers through email about the course the users are exploring. And the sales team can connect over a call with users to ensure users purchase the courses. All of this can be possible if the company has a proper ETL tool to gather data from different sources, transform it, and create a single source of truth through centralization.

Types of ETL Tools

There are several types of ETL Tools available, including: 

1. Enterprise ETL Tools

Enterprise ETL tools are software applications designed for large organizations to handle a high volume of data from various sources. Enterprise ETL tools often have a unique set of features and capabilities, which has the potential to handle complex data transformations and manipulation tasks, schedule and automate ETL processes, and manage vast volumes of data sources and targets.

Enterprise ETL tools are mainly used for data integration, warehousing, and business intelligence applications. Examples of enterprise ETL tools include SAP Data Services and IBM DataStage.

  • IBM InfoSphere DataStage: IBM InfoSphere DataStage is an industry-leading data integration tool that helps businesses extract, transform, and load data. It is generally used by large-scale companies that handle massive volumes of data. IBM InfoSphere DataStage also supports mainframe computers that are still common among large organizations.
  • SAP Data Services: SAP Data Services is a software that helps businesses improve their data quality. With SAP Data Services, organizations can transform their data into a trusted and ever-ready resource for business insights. SAP Data Services allows businesses to access and integrate all enterprise data sources and third-party targets with built-in connectors. It can transform several types of data with a centralized business rule repository. Businesses can fulfill their high volume needs by enabling parallel processing, bulk data loading, and grid computing via SAP Data Services.

2. Open-Source/ Free ETL Tools

Open-source ETL tools are freely available software that can be modified to address your specific requirements. Since the source code of open-source ETL tools is publicly accessible, data scientists or data analysts can quickly inspect, modify, and enhance their ETL processes in organizations.

Many open-source ETL tools in the market simplify data management tasks while improving data warehousing. Some of them are as follows:

  • Pentaho Data Integration (PDI): PDI is an open-source ETL tool for its graphical interface, Spoon. It can generate XML files to represent pipelines and execute pipelines via the ETL engine.
  • Apache Kafka: Apache Kafka is an open-source, distributed event streaming platform businesses use to carry out high-performance ETL pipelines for streaming analytics. As Kafka can process trillions of events per day, it has become popular among companies that rely on real-time analytics for quick decision-making. 

3. Custom ETL Tools

Custom ETL tools are ETL tools designed to meet an organization’s requirements. Companies use general-purpose programming languages to create custom ETL tools. Custom ETL tools provide enough flexibility but also require extensive effort as you would be needed to build data pipelines from scratch. For custom ETL tools, organizations must perform maintenance, build documentation, and test and check ongoing development on their own. Organizations design and create custom ETL tools and pipelines using scripting languages like Python, SQL, and Java and technologies like Kafka, Hadoop, and Spark.

4. ETL Cloud Services

Cloud ETL services are cloud-based ETL tools that enable organizations to quickly and efficiently perform ETL operations in a cloud computing environment. Microsoft Azure, Google Cloud Platform, and Amazon AWS offer ETL cloud services. Some of the ETL cloud services are highly proprietary and can work only within the framework of the cloud vendor. Companies cannot use ETL cloud services in a different cloud vendor’s platform.

Some of the popular cloud ETL services include:

  • Hevo Data: Hevo Data is an end-to-end ETL tool helping businesses pull data from different sources, run transformations, and store it in the centralized repository. With Hevo Data, companies can get a 360-degree view of customers by extracting data from more than 150 sources, including SaaS, databases, applications, data warehouses, and more. Businesses use Hevo Data’s real-time data integration platform to simplify data integration challenges in any data analytics project.
  • AWS Glue: AWS Glue is a fully managed ETL service that can be tightly integrated into other AWS services like RDS, S3 Lambda, and Redshift. It can also connect to on-premises data sources to help users move their data into the cloud. ETL pipelines in Glue are written in Python and executed using PySpark and Apache Spark. 
  • Azure Data Factory: Azure Data Factory is a fully managed service that can connect to a wide range of cloud and on-premise data sources. It has the potential of copying, transforming, and enriching data and then writing it to Azure data services as a destination. Data Factory can support transformation steps in machine learning, Hadoop, and Spark.

Final Thoughts

Organizations have used different types of ETL models for more than 30 years to read data from multiple sources, apply transformations, and then save the results in another analytics system. Businesses can choose either paid or free ETL tools. Open-source ETL tools are easily accessible by companies but need technical expertise to deploy the ETL process. On the other hand, paid ETL automation tools usually have quality support, a better user interface, and up-to-date documentation with regular product updates. 

Getting data from many sources into destinations can be a time-consuming and resource-intensive task. Instead of spending months developing and maintaining such data integrations, you can enjoy a smooth ride with Hevo Data’s 150+ plug-and-play integrations (including 40+ free sources).

Visit our Website to Explore Hevo Data

Saving countless hours of manual data cleaning & standardizing, Hevo Data’s pre-load data transformations get it done in minutes via a simple drag n drop interface or your custom python scripts. No need to go to your data warehouse for post-load transformations. You can run complex SQL transformations from the comfort of Hevo’s interface and get your data in the final analysis-ready form. 

Want to take Hevo Data for a ride? Sign Up for a 14-day free trial and simplify your data

Manjiri Gaikwad
Freelance Technical Content Writer, Hevo Data

Manjiri loves data science and produces insightful content on AI, ML, and data science. She applies her flair for writing for simplifying the complexities of data integration and analysis for solving problems faced by data professionals businesses in the data industry.

All your customer data in one place.