10 Best Open Source ETL Tools for Data Integration

Every business today is data-driven. Almost 94% of enterprises agree that their business growth depends on data. Yet less than 40% of organizations can aggregate and analyze the data for their use. While the ETL process helps, it is pointless without proper tools. And finding the ETL and data integration system that works right for you is time-consuming, not to mention costly.

But free, open-source ETL tools exist to drive such worries away. Some options are enterprise-backed, hoping to offer you the best solution, while others are managed by a community of developers looking to make the ETL process easy and accessible.

We have curated a list of the 10 best open-source ETL tools in the current market scenario to help you decide on one or more for your work. After discussing all these free ETL tools, we will also provide a comparison table that you can go through.

What is ETL?

ETL stands for Extraction, Transformation, and Loading. It is a process that businesses use to gain critical and actionable insights from data collected from various sources, including social media platforms, emails, and customer support tools. ETL is a three-phase process:

Extraction

Extraction refers to unifying structured and unstructured data to draw important, business-critical information from them. With only a few clicks using ETL tools, you can withdraw necessary details from the collected data.

Transformation

The second phase of the ETL process is transformation. It means transforming the extracted information into a format understandable by the users, data warehouses, or Business Intelligence (BI). Some transformation techniques include data sorting, cleaning, deleting, and verifying procedures.

Loading

The third phase of the ETL process is loading, which translates to saving the transformed information into a data warehouse. Proper loading of data is essential as the BI tools work on the information to produce necessary reports and insights for users and business stakeholders.

10 Best Open Source ETL Tools

So far, we have understood the ETL process. Here we list down the 10 best open-source ETL tools, some of which you can employ to fulfil your data processing needs.

Keboola

With Keboola, ‘connect any data source in less than 20 minutes.’ It is your all-in-one data engineering platform for all your data needs.

It is an end-to-end ETL tool that runs a complete data platform as a service. If your data seems to be confusing and creating operational chaos, Keboola is the right choice to collaborate on analytics and automation. It offers extraction, transformation, data management, and pipeline orchestration solutions along with reverse ETL.

Design and deploy data pipelines, understand the science behind your business data, and integrate with several cloud, databases, collaboration, CRMs, and more platforms using Keboola.

CloverDX

CloverDX is a highly preferable platform that offers a centralized location for all your data publishing and processing needs. It is efficient, offers control over the data, and provides transparency in the processing. You can integrate it with in-cloud and on-premises data sources to handle multiple data formats.

As an all-in-one data management tool, CloverDX connects to multiple data sources, eliminates data silos, and avoids vendor lock-ins. It does everything from designing your data and automating repetitive tasks to combining with necessary third-party tools and publishing information in databases, files, messages, and more.

Logstash

A free and open source ETL tool, Logstash collects data from several sources, performs a transformation process, and sends the output back to your choice of data warehouse. It consists of pre-built filters and more than a hundred plugins to carry out the data process operations. No matter the format or the complexity of data, Logstash dynamically ingests, transforms, and ships the information to the ‘stash’ of your choice.

One of the best features of Logstash is the extensible plugin ecosystem. It has nearly 200 plugins available and a rich library of filters to create the pipeline that best suits your data process needs.

Apache Kafka

Apache Kafka is an open-source system developed by Apache Software Foundation. Written in Java and Scala, the platform aims to offer a unified and high-throughput pipeline for handling data feeds. The tool is a distributed event streaming platform for performing high-performance data pipelines, streaming analytics, data integration, and mission-critical applications.

Apache Kafka supports scalability with the capability of elastically expanding and contracting data storage and processing. Permanent storage, built-in stream processing, seamless integrations, and a set of rich client libraries are some of the useful features of the tool that make it a trusted ETL pipeline tool.

Pygrametl

An open-source data integration ETL tool, Pygrametl is a Python framework that offers commonly used functionality for executing ETL processes. It supports coding to run any ETL-based phase for managing and processing data. As the tool is compatible with both CPython and Jython, existing Java code and JDBC drivers can be used in the ETL program. Although coding for ETL phases might sound cumbersome, Pygrametl proves useful compared to script-less GUI tools because it saves time and effort in manually creating data pipelines.

The framework was released in 2009 and has evolved to provide users with high efficiency in creating effective ETL flows with full programming power. As discussed in the paper here, the latest addition to Pygrametl is Drawn Table Testing (DTT), which simplifies testing ETL flows by making it easy to define both preconditions and postconditions of the data processing run into a test. You can learn more about this framework and its recent releases in the attached link.

Singer

Singer is an open-source tool to extract and consolidate data for all your organizational needs. You can send data between databases, web APIs, files, queues, and more. It integrates with software that young enterprises use to fulfill their ETL requirements.

One thing to keep in mind is that Singer is a script-based ETL tool; you have to write specific codes to perform ETL duties. Data extraction scripts are called ‘tags,’ and data loading scripts are termed ‘targets.’ These scripts can be run in any sequence or combination to execute the ETL processes of your choice. Singer further allows you to create your own tags and targets if the existing ones do not match your demands.

Another differentiator of Singer is its ability to support modular data transfer pipelines. These modular data transfer and loading options are easy to maintain and execute.

Scriptella

Yet another popular and quite usable open-source ETL tool is Scriptella. Written in Java, it is a script execution tool whose prime focus is simplicity. The tool has quite an active community and is licensed under Apache, version 2.0.

As Scriptella is a script-based tool, you would need to perform ETL functions using codes. But don’t worry! You won’t have to learn any new coding language for that. You can use SQL for accessing and data transfer. Besides SQL, it also supports JavaScript, JEXL, and Velocity. Other tool features include Interoperability with LDAP, JDBC, XML, and other data sources and cross-database ETL operations. But if you are looking for a GUI tool to execute ETL processes, Scriptella might not be the best bet as it does not support GUI.

Here are all the different features of the tool for you to explore.

Pentaho Kettle

A free and open-source ETL data integration tool, Kettle is now Pentaho Data Integration. It is popular among its users as a comprehensive software with the ability to access, blend, and analyze data from multiple sources.

The term Kettle stands for Kettle Extraction Transformation Transport Load Environment. It is known as an ideal data blending, integration, and business analytics platform. Pentaho Kettle offers to extract data from data sources such as MySQL, PostgreSQL, Oracle, SQL Server, a variety of NoSQL APIs, text files, and more. As opposed to the above-discussed two tools, Kettle is codeless and extremely helpful in extracting actionable insights from business data.

It is an ETL tool that offers an in-house data and files storage repository. So, you can use the Pentaho repository if you are looking for a collaborative ETL environment.

Talend Open Studio

Talend Open Studio is a data integration and ETL platform that allows users to build basic data pipelines within minutes. All you need is the latest OS version, 8 GB of internal memory, and more than 20 GB of internal disk space to start the installation.

If your project is ready to launch, you need Talend Open Studio with its easy-to-use interface and integration capabilities. Thereafter, you can monitor and schedule the ongoing project requirements. You can further leverage the tool to easily add data quality, big data integration, and processing resources. And take advantage of the latest data sources, analytics technologies, and elastic capacity from AWS or Azure as and when you need it.

Apache Camel

If data integration and collection is your top priority and business requirement, Apache Camel should be your go-to tool. It is an open-source framework that enables you to readily integrate with sources producing and consuming data useful to you. It supports more than 50 data formats from across different industries, including finance, health, telecom, and more.

Popular for its data integration capabilities, Apache Camel supports most of the Enterprise Integration Patterns and newer integration patterns from microservice architectures. The idea is to help you solve your business integration problems using the best industry practices. It is also interesting to note that the tool runs standalone and is embeddable as a library within Spring Boot, Quarkus, Application Servers, and popular cloud platforms.

Open Source ETL Tools Comparison Criteria

ETL Tools	Format Supported	Integrations	Automation	Codeless/Code-based	Installation & Deployment	Subscription
Keboola	All data formats	Salesforce, project management, AWS, and more	Yes	Codeless	Deploy as a service	Free and enterprise plan
CloverDX	All data formats	All 3rd party Java libraries	Yes	Codeless	On-premises and cloud-based	Available on the website
Logstash	XML, JSON, CSV, logs, and more	Cloud platforms, Kubernetes, Confluence, and CRMs	Yes	Codeless	On-premises and cloud-based	Free
Apache Kafka	Event-record format	integrates with hundreds of event sources and event sinks including Postgres, JMS, Elasticsearch, AWS S3, and more.	Yes	Codeless	Can be deployed on virtual machines, containers, and on-premises, and on the cloud	Free
Pygrametl	SQL, CSV, TypedCSV, Pandas, and more	Python code	Yes	Code-based	On-premises	Free
Singer	Multiple sources	Python-based libraries	Yes	Code-based	Virtual environment or on-premises	Free
Scriptella	LDAP, JDBC, XML and other datasources	Java EE, Spring Framework, JMX, JNDI and JavaMail	Yes	Code-based	On-premises	Free
Pentaho Data Integration	Multiple data formats	Java-based libraries	Yes	Codeless	On-premises	Enterprise Edition/community Project
Talend Open Studio	All big data formats	RDMS,SaaS connectors, CRMs	Yes	Codeless	On-premises and cloud-based	Free
Apache Camel	JSON, XML, SOAP, ZIP, and more (50+ types)	Spring, Quarkus, and CDI	Yes	Code-based	On-premises and as an embeddable library	Free

Open Source ETL Tools: Key Features

Open source has gained popularity because they come with an active community of developers and users ready to guide you through the process of using the tools. Although an experienced person helping you with your requirements is plenty useful, there are several other key features of open-source ETL tools you must know about:

Scalable

An extensible, open-source ETL tool effectively collects and processes data for our business. They are also less complex and easy to use when handling enormous amounts of data.

Economical

Companies looking for ETL tools that get the work done but are still economical for their business can choose open-source options. These organizations receive the best data integration capabilities with higher quality within their budget.

Secure

Open-source ETL tools offer encryption facilities that are essential for companies that work in the finance and healthcare industry. With an open community backing such tools, you also have the option to turn to the developers taking care of such groups for assistance.

Data Integration

Open-source ETL tools are the best for businesses looking for embeddable data integration options. These tools offer Data Integration, Migration, and Transformation services at a reasonable price compared to their commercial counterparts.

Real-time Processing

You can instantly send data through the pipeline using real-time processing available in ETL tools, which proves extremely useful in fraud detection. You can detect and prevent IT breaches by having access to real-time data transfer insights.

Limitations of Open Source ETL Tools

Certainly, ETL tools offer a solid foundation for performing Extraction, Transformation, and Loading pipelines. But they are still developing and becoming a fully-grown version of themselves. Currently, open-source ETL tools have certain limitations, especially in terms of after-customer support. These are some of the limitations of open-source ETL tools:

• They still lack proper integration and connectivity with in-house software that enterprises use.
• These open-source tools lack error-handling capabilities.
• Most of these ETL tools are interface-driven, which makes them difficult to navigate and debug, thereby introducing reproducibility issues.
• Some of the tools can analyze large data sets, but they can process data only in small batches, which gives rise to efficiency problems in the pipeline.
• A few of the discussed tools are not compatible with data management software or RDBMS systems, hindering the data pipeline performance after data is sourced from various platforms.
• Enterprises with complex data analyzing and process needs cannot fulfill all their requirements by using open-source ETL tools even if their budget is low.

What are the Steps of the ETL Data Integration Process?

ETL is predominantly a three-step process. But simply Extracting, Transforming, and Loading data will not be useful to your business. You also need to structure and analyze the available information. Hence, there are 5 necessary key steps to follow for the ETL data integration process:

Extract:

Gathers raw data from unstructured data set or pool and stores it in a repository for further use.

Clean:

Polishes and cleans the collected data to ensure quality before moving on to the transformation phase.

Transform:

Converts unstructured data into structured data; the transformation process translates the available data into an understandable format.

Load:

This phase loads the structured data into a data warehouse for analysis and gaining valuable insights

Analyze:

The analysis is run on the data stored in the warehouse, allowing businesses to extract insights for their use.

Every step comes after the other in the sequence, as mentioned above. Usually, data engineers and developers perform these processes as they carry the necessary knowledge to deal with data warehouses and their capabilities.

Which among the Open Source ETL Tools is the Best?

Every ETL tool functions differently and carries features unique to them and the requirements they fulfill. And with varied business needs in collecting, storing, and manipulating data, picking one tool out of many is not the right course of action. For instance, if you are looking for a data pipeline tool that offers codeless data processing, then Talend Open Studio, one of the most used and popular ETL tools, may be challenging for you.

The best ETL tool is the one that aligns with your demands and provides the solution that you are looking for. Perhaps, you can choose Keboola, Pentaho Kettle, CloverDX, Logstash, and Apache Kafka. However, you must go for Scriptella or Talend Open Studio if your team wants to save time manually creating and connecting data pipelines. These tools are perfect for technically adept businesses that would rather take the option to code and perform data processing.

Conclusion

When finalizing an ETL tool, time, cost, and ease of use are the top factors for many companies. The process is fairly time-consuming and necessitates businesses to understand their data integration and processing requirements to search for the tool that does the job.

While some data pipeline tools offer features that go beyond your business needs, others are technically developed but require the right skills for usage. You are again left with confusion. Which ETL tool is the right fit for your organization and data processing needs?

This list is our attempt to help you choose a tool that is the latest in the market, equipped with all the essential capabilities to power your big data processing and analysis for gaining the right actionable insights. You can check out our comparison table to better understand all these platforms and their features.

Frequently Asked Questions (FAQs)

Which Open Source ETL Tool is Used Most?

Apache NiFi is a well-known open-source ETL (Extract, Transform, Load) tool. It gives you a scalable and powerful data integration, processing, and workflow automation platform. Other notable open-source ETL solutions are Apache Airflow, Talend Open Studio, and Pentaho Data Integration.

Which Open source tool is in demand in 2023?

 It is difficult to anticipate the exact demand for open-source tools in 2023 because it depends on various factors and emerging trends. However, open-source solutions such as Kubernetes for container orchestration, TensorFlow for machine learning, Apache Kafka for real-time data streaming, and Prometheus for monitoring and observability are expected to grow in prominence in 2023. Specific tool needs may change depending on industry trends, technological improvements, and community support.

What is the fastest ETL tool?

Among the ETL tools discussed in this article, Apache is one of the fastest in the market. It allows for seamless data integration and manipulation. It is an open-source tool available for users and has been updated over time to support upcoming data integration requirements.