Types of ETL Tools: The Complete Guide 101

Organizations use ETL (Extract, Transform, and Load) to obtain quality data to expedite decision-making. However, 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 their use cases to help you evaluate the right one for your business.

What is the ETL approach?

The software that helps you perform the ETL processes is known as the ETL automation Tool. These tools extract data from various sources, transform them, and load them into target destinations, like databases or warehouses. 

• Extraction: In this first step of an ETL process, data is extracted from different sources, such as proprietary databases, cloud environments, CRM systems, etc. This step can be done using APIs, writing SQL queries, or using ETL automation tools with user-friendly interfaces. 
• Transformation: The next step is transforming the raw data and enhancing its quality for better business use. Generally, transformation includes data cleansing, filtering, standardization, removing duplicates, sorting, and more.
• Loading: The last step is to load the data into your desired destination. This data is then connected to powerful business intelligence tools such as Google Analytics, Tableau, Power BI, and more for analysis.

What is the need for ETL Tool?

Imagine an ed-tech organization that uses several marketing and sales strategies to promote its courses. The company will usually have to rely on several marketing platforms, such as LinkedIn Ads, Facebook Ads, Google Ads, Google Analytics, and more. 

Data generated from campaigns on these platforms can be combined using ETL tools and stored in a data warehouse. Since data on these platforms is recorded in various formats, an ETL tool will ensure proper data standardization before it is centralized. Using the tool, they can also collect data from their CRM and club it with marketing and website engagement data to profile users.

Centralizing data would allow analysts to analyze the ROI of different campaigns, optimize campaigns, and more. 

For instance, website engagement data can be tracked and shared with the marketing and sales teams. The marketing team can send personalized offers through email about the course the users are exploring. The sales team can connect with users over a call to ensure they 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

The development of ETL Tools began in the 1990s with the emerging need for data warehouses. With time, these tools started to fall under different categories, catering to different organizations’ needs and preferences. Today, the market has the following types of ETL Tools: 

1. Enterprise ETL Tools

Enterprise ETL tools are software applications designed for large organizations to handle massive volumes of data from multiple sources. These are the oldest types of ETL tools present in the market. 

These tools often have a unique set of features and capabilities. They have the potential to handle complex data transformations and manipulation tasks. 

Enterprise ETL tools are mainly used for data integration, warehousing, and business intelligence applications. Most of these tools support both relational and non-relational databases, such as XML and JSON. 

Some examples of Enterprise ETL Tools are: 

• IBM InfoSphere DataStage: It is an industry-leading data integration tool that helps large businesses with their ETL processes. It also supports mainframe computers, which are still common among large organizations.
• SAP Data Services: This software helps businesses improve their data quality by transforming their data into a trusted and ever-ready resource for business insights. They allow the access and integration of all enterprise data sources and third-party targets with built-in connectors. Businesses can fulfill their high volume needs by enabling parallel processing, bulk data loading, and grid computing via SAP Data Services.

2. Open-source ETL Tools

Open-source ETL tools are freely available software that enhances your organization’s ETL process. The source code of such tools is publicly available, so you can inspect and modify it to meet your specific needs. 

These tools are suitable for small firms as well as large enterprises. Open-source ETL tools are easy to use and can perform advanced data orchestration and operations features. They simplify data management tasks while improving data warehousing. 

Some examples of Open-source ETL Tools are: 

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

3. Custom ETL Tools

Custom ETL tools are designed by self-coding to meet an organization’s requirements. These tools provide enough flexibility but require extensive effort, as you would need to build data pipelines from scratch. 

For custom ETL tools, organizations must perform maintenance, build documentation, and test and check ongoing development independently. Finding help in fixing bugs outside of the development team is often difficult. 

Custom ETL Tools can be built using: 

• Python: It is a versatile and popular programming language. Its extensive library, including Pandas, Numpy, and SQLAlchemy, and ease of use help build highly customizable ETL workflows. 
• SQL: It is an effective tool for custom ETL if the source and destination are the same. It is powerful for performing basic transformations but not for performing complex transformations. It is inherently designed to work with relational databases. 

4. Cloud-based ETL Tools

Cloud-based ETL tools enable organizations to quickly and efficiently perform ETL operations in a cloud computing environment. The advantage of cloud-based ETL tools is that they are highly scalable, flexible, and cost-efficient. 

Their user-friendly interfaces and managed services reduce the complexity of ETL processes and allow your team to focus on delivering insights instead of managing infrastructure. Cloud-based ETL Tools also allow collaboration between geographically dispersed teams, making it a modern solution for efficient data management.

Some popular examples of Cloud-based ETL Tools are: 

• Hevo Data: Hevo Data is a no-code data pipeline solution that facilitates seamless data extraction, loading, and transformation from any source with zero maintenance. It allows you to immediately transfer data from over 150 plug-and-play connectors, including a diverse array of SaaS applications and databases. With this tool, pipelines can be scheduled down to the minute.
• AWS Glue: It 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: It lets you connect to a wide range of cloud and on-premise data sources with its fully managed service. It has the potential to copy, transform, and enrich data and then write it to Azure data services as a destination. It can also support transformation steps in machine learning, Hadoop, and Spark.

You can read “What is ETL Tool?” to learn about the factors to consider while evaluating an ETL tool. 

Final Thoughts

Organizations have used ETL models for over 30 years to read data from multiple sources, apply transformations, and save those results in another analytics system. Businesses can choose either paid or free ETL tools. 

Companies can easily access open-source ETL tools but need technical expertise to deploy the ETL process. On the other hand, paid ETL automation tools like Hevo 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 time-consuming and resource-intensive. Instead of spending months developing and maintaining such data integrations, you can enjoy a smooth ride with Hevo Data’s data integration and transformation capabilities. Get a 14-day Free Trial of Hevo’s full features! 

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