15 Best Free & Open Source ETL Tools: The Complete List

Which factors are most important when choosing an ETL tool?

1. Data Transformation: The capacity to alter data in a variety of ways, including cleansing, filtering, aggregating, and enriching the data.

2. Data quality: It refers to the capacity to guarantee the accuracy and completeness of the data by identifying and fixing errors as well as by filling in blanks.

3. Data Connectivity: Connectivity to a variety of data sources, such as relational databases, flat files, and cloud-based data stores, is referred to as data connectivity.

4. Data Loading: The capacity to upload the modified data to a range of destination platforms, such as data lakes, data marts, and data warehouses.

5. User-Friendliness: The tool's simplicity of use, rapid learning potential, user-friendly user interface, and thorough documentation.

6. Scalability: The capacity to effectively manage huge amounts of data, processing data flows with high-performance, and to accommodating the evolving data needs of an organization.

7. Data governance: It is the capacity to keep track of, manage, and maintain the integrity of data through time.

8. Integration with other tools: The capacity to integrate with other tools and systems, including business intelligence (BI) and analytics tools, to facilitate fluid data analysis and reporting.

9. Data security: The capacity to keep data safe and secure while preventing unauthorized access.

15 best free ETL tools

Portable
Apache NiFi
AWS Glue
Google Cloud Data Fusion
Hevo Data
Pentaho Kettle
Apache Hive
Fivetran
Blendo
Dataddo
Stitch
Domo
Jaspersoft ETL/Talend Open Studio
CloverDX
Informatica PowerCenter

1. Portable

For teams working with long-tail data sources, Portable is the finest data integration tool. Portable is an ETL platform that provides long tail connectors for more than 300 obscure data sources.

In short, Portable provides the long tail ETL connectors you won't find with Fivetran.

Upon request, the Portable team will create and manage unique connectors with turnaround times as quick as a few hours.

Pros:

More than 300 data connectors are designed for niche applications.
Within days or hours, new data source connectors were created without additional charge.
Constant connector upkeep is free of charge.
Portable data integration tools can be easily moved between different environments, allowing you to use them on different devices or platforms as needed.

Cons:

Enterprise systems like Salesforce and Oracle are not connected to Portable; it only offers long-tail data sources.
No assistance with data lakes.
Only accessible within the USA.

Pricing:

For manual data processing, Portable provides a free plan with no restrictions on volume, connectors, or destinations. The monthly flat rate for automated data transfers at Portable is $200. Contact sales for corporate requirements and SLAs.

Best suited for:

Teams who need to link multiple data sources and want to concentrate on extracting insights from data rather than building and managing data pipelines should use portable.

2. Apache NiFi

The Apache Software Foundation created the open-source data integration technology which is web-based known as Apache NiFi, which stands for "Data Flow." The automated data flow between systems makes it simple to move and transform data from different sources to different targets.

NiFi comes with built-in processors for typical activities like filtering, aggregation, and enrichment. It is frequently utilized as a component of a broader data management and analytics solution, such as a data lake or a data warehouse, and it is frequently used in data integration, data management, and data analytics applications.

Pros:

NiFi was created with the ability to recover from errors without losing data.
To safeguard data in transit and at rest, NiFi has built-in security features like encryption, authentication, and authorization.
NiFi has built-in processors for typical activities like filtering, aggregation, and enrichment. It can integrate with a variety of data sources and targets.

Cons:

The flow.xml becomes invalid if a node is unplugged from the NiFi cluster while a user is making modifications to it.
When the primary node switches, Apache NiFi has a problem with state persistence, which occasionally prevents processors from being able to retrieve data from sourcing systems.

Pricing:

Depending on the configuration prices you need, Apache NiFi's pricing information may vary. In the AWS Marketplace, it is available for purchase. If you buy the Professional edition using an AWS account, it costs $0.25 per hour.

Best suited for:

For businesses that must process and analyze massive amounts of data in real-time or almost real-time, Apache NiFi is a good fit.

3. AWS Glue

A fully managed extract, transform, and load (ETL) solution called Amazon Web Services (AWS) Glue makes it simple to move data between data storage. It offers a straightforward and adaptable method for organizing ETL processes, and it can automatically find and classify data such that it is simple to search for and query.

The location, schema, and runtime metrics of data are stored and tracked using AWS Glue's single metadata repository, the Glue Data Catalog.

Pros:

Because AWS Glue is a fully managed service, users don't have to worry about setting up, maintaining, or updating the underlying infrastructure.
Users may create and manage data integration jobs with ease using AWS Glue's user-friendly interface.
Users of AWS Glue only pay for the resources they utilize because it is a pay-as-you-go service.
Includes JSON, CSV, Excel, Parquet, ORC, Avro, and Grok as supported output formats

Cons:

For consumers to utilize AWS Glue efficiently, they need to have an AWS account and be conversant with these other services.
Limited support for some data sources: AWS Glue offers support for a variety of data sources, however not all data sources will receive the same level of support.
Spark struggles to handle joins with high cardinality.

Pricing:

Users of AWS Glue only pay for the resources they utilize because it is a pay-as-you-go service. Using AWS Glue is free of any setup fees or minimum charges. $0.44 per hour of digital processing

Best suited for:

Organizations that wish to find, prepare, move, and combine data from many sources for analytics, machine learning (ML), and application development are the best candidates.

4. Google Cloud Data Fusion

A fully managed, cloud-native data integration platform, Google Cloud Data Fusion enables customers to quickly design, plan, and automate data pipelines.

It includes a variety of tools and capabilities for managing and manipulating data, including support for data cleansing, data quality checks, and data mapping.

Pros:

GCP-native
business-grade security
lineage and metadata integration
streamlined procedures

Cons:

With other Google Cloud services like BigQuery, Cloud Storage, and Cloud Pub/Sub, Google Cloud Data Fusion connects with them without any issues. However, this also means that to use Google Cloud Data Fusion efficiently, users must have a Google Cloud account and be conversant with these other services.
Because Google Cloud Data Fusion is a commercial software product, a license is necessary to utilize it.
Limited customization options

Pricing:

The following three editions are available for pipeline building with Cloud Data Fusion:

$0.35 (around $250 monthly) for Developer Edition
Basic Edition costs $1.80 (around $1100 per month).
$4.20 for the Enterprise Edition (around $3,000 per month)

The first 120 hours per month per account are free with the Basic edition.

Best suited for:

It enables the creation of flexible, cloud-based data warehousing solutions in BigQuery, making it ideal for businesses to better understand their consumers.

5. Hevo Data

Hevo Data is a platform for managing and integrating data that is made to assist businesses in integrating data from diverse sources.

Hevo Data is a cloud-based platform, thus customers do not have to bother about installing, configuring, or maintaining the underlying infrastructure because it is entirely managed. With Hevo, you can nearly real-time copy data from more than 150 sources, including Snowflake, BigQuery, Redshift, Databricks, and Firebolt.

Pros

Users don't have to bother about installing, configuring, or maintaining the underlying infrastructure because Hevo Data is a fully managed, cloud-based platform.
Users may easily create and manage data integration jobs with Hevo Data's user-friendly interface.
Hevo Data interfaces with various tools and platforms without difficulty, including reporting, data visualization, and business intelligence applications.
To address the problem before it completely halts the workflow, Hevo also enables you to monitor your workflow.

Cons:

Since Hevo Data is a commercial software application, a license is necessary to utilize it.
Hevo Data supports a variety of data sources, although not all of them may be supported or may not be supported to the same degree.
Excessive CPU Use

Pricing:

Free: Up to a million occurrences, but only from more than 50 data sources
Starter: At $239 per month.
Business: Individual quote

Best suited for:

Hevo Data is a strong and adaptable data management and integration solution that is ideal for businesses that want a scalable, completely managed, and user-friendly platform for moving and merging data. Hevo works well for data teams seeking a no-code platform with flexibility for Python programming and well-known data sources.

6. Pentaho Kettle

A potent open-source platform for data integration and transformation is Pentaho Kettle, commonly known as Pentaho Data Integration (PDI).

The Extract, Transform and Load (ETL) paradigm, upon which Pentaho Kettle is based, entails the extraction of data from one or more sources, its transformation to satisfy particular needs, and its loading into a destination.

Pros:

Pentaho Kettle is an open-source platform, which implies that users can access the source code and that it is available for free.
To assist users in extracting, transforming, and loading data, Pentaho Kettle offers several capabilities and tools. It has a standard architecture and a graphical drag-and-drop user interface for creating and managing ETL operations and supports a wide variety of data sources and transformations.
Pentaho Kettle has a sizable and active user and developer community that contributes to the platform and offers assistance and direction.
Strong DBA services include database replication, data migration, and support for dimensions and schemas in data warehousing that change gradually.

Cons:

In order to function, it depends on some third-party software elements, such as Java.
Due to server load, data integration takes too much time.
Depending on the complexity of the model, data modeling can take an excessive amount of time.
many business links are absent, such as any SaaS app.

Pricing:

Pentaho Kettle currently offers a 30-day free trial period. No specific pricing information is provided.

Best suited for:

It is typically best suited for businesses trying to automate and streamline their data management procedures and in need of a flexible, open-source solution for data integration and transformation.

It is simple to utilize Pentaho Kettle as a component of a larger data management and analysis process because it interfaces smoothly with a wide variety of other products and platforms.

7. Apache Hive

The Hadoop distributed file system (HDFS) and other big data systems use the data warehousing and SQL-like query language Apache Hive. For managing and executing queries on enormous datasets kept in Hadoop and other big data systems, such as Apache Spark and Apache Impala, it offers an intuitive user interface.

Hive's capability to convert SQL-like queries into MapReduce tasks that can be executed on a Hadoop cluster is one of its core advantages.

Pros:

Since HQL is a declarative language like SQL, it lacks procedural functionality.
Hive is a trustworthy batch-processing framework that may act as a data warehouse on top of the Hadoop Distributed File system.
Hive can handle Petabyte-sized datasets, which are incredibly huge.
The 100 lines of Java code we presently need to query the contents of a structure may be cut down to 4 with HQL.

Cons:

Apache Hive only supports OLAP; it does not allow online transaction processing (OLTP).
Hive is not used for real-time data querying because it takes time to return a result.
Subqueries cannot be used.
The apache hive query has a very high latency.

Pricing:

Price information has not yet been published by the Apache Software Foundation.

Best suited for:

Apache Hive is a query language for data warehousing and data analysis that is suitable for a range of data processing and analytical operations.

Hive is an effective tool for processing and analyzing enormous amounts of data that are stored in Hadoop and other big data systems, in general.

8. Fivetran

Fivetran is a cloud-based data integration tool which aids businesses in automating the transfer of data from numerous sources to a central data warehouse or other location.

Fivetran employs a completely managed, zero-maintenance architecture, which means that duties like data translation, data quality checks, and data deduplication are handled automatically.

Pros:

managed services strategy
Data analytics pre-built schemas
Low ownership costs

Cons:

Limited Support for Data Transformation
Enterprise data management capabilities are lacking

Pricing:

Three editions of Fivetran range in price from $1 to $2.

The Starter edition costs one credit at $1.
The standard edition costs $1.5 for each credit.
$2 per credit is charged for the Enterprise edition.

Best suited for:

Organizations that seek to do away with the requirement for manual data integration procedures and cut back on the time and resources needed to manage data pipelines will find it to be very useful.

9. Blendo

Rudderstack includes Blendo, a no-code ELT cloud data platform. It expedites the setup procedure using automation scripts so you may start importing Redshift data right away.

Pros:

Supported 45+ data sources.
The platform is simple to use and doesn't require any programming experience.
Monitoring and warnings are built-in capabilities.

Cons:

Very few supported data sources are available.
Data transformations have a limited feature set.
Teams cannot independently connect additional data sources to Blendo.

Pricing:

Three sources are available for free only.
The Pro package is available for $750 per month and includes changes.
Enterprise plans are offered with customizable pricing

Best suited for:

For data, teams searching for a no-code platform and with a limited number of data sources, Blendo is the ideal option.

10. Dataddo

Dataddo is an ETL platform for data integration that enables you to move data between any two cloud services. This comprises products and services like CRM tools, data warehouses, and dashboarding software.

Pros:

Countless Possibilities for Data Extraction
Simple Dashboard
The enormous number of destinations

Cons:

Only pre-built connectors are available in the free edition.
Only 3 data flows are available in the free product version. In Dataddo's service, a data flow is a link between a source and a destination.

Pricing:

Dataddo offers four plans.

Free offers Every week, sync data with any visualization tools, such as contains three data flows
For $129/month, Data to Dashboards offers hourly data syncing to any visualization software.
For $129/month, Data Anywhere offers Sync data between any sources and any destinations.
enables Headless Data Integration Create new payment methods and build your own data products on top of the unified Dataddo API.

Best suited for:

A non-technical user that does not require many changes and would like to integrate data from applications into their business intelligence tools.

11. Stitch

Talend includes the data pipeline tool Stitch. Using a built-in GUI, Python, Java, or SQL, it controls data extraction and straightforward manipulations. Talend Data Quality and Talend Profiling, are extra services.

Pros:

Automations, such as alarms and monitoring.
Supported 130+ data sources.

Cons:

No option for deployment on-premise.
Every plan of Stitch has restrictions on sources and destinations.

Pricing:

Available 14-day free trial
Standard package with up to 5 million active rows per month, one destination, and 10 sources, beginning at $100 per month (limited to "Standard" sources)
Advanced plan for up to 100 million rows and three locations at $1,250 per month
Premium package for up to 1 billion rows and five locations at $2,500 per month

Best suited for:

Teams that use common data sources and require a straightforward tool for fundamental Redshift data import should use Stitch.

12. Domo

Domo Business Cloud is a specialized cloud-based SaaS that enables you to create ETL pipelines and integrate your data from many sources.

Between your data sources and your data destination (data warehouse), Domo Business Cloud serves as an intermediate and enables you to extract data from the former and load it into the latter.

Pros:

You can extract data with the use of over 1,000 pre-built connectors.
Domo can function across on-premises deployments and many cloud vendors (AWS, GCP, Microsoft, etc.).
ETL pipelines can be created on the dashboard using SQL code or no-code visualization tools.

Cons:

Since pricing models are customized for each customer, you will need to get in touch with sales to acquire a price.
Some users claim that Domo stops working effectively as soon as you start changing the scripts and leave the pre-built automated extractions.

Pricing:

Domo offers three price tiers, ranging from $83 to $190. Additionally, Domo offers a free trial.

The standard plan is $83.00.
An expert plan costs $160.00.
A business strategy costs $190.00.

Best suited for:

Users in the enterprise who want to use Domo as their primary cloud provider for data integration and extraction.

13. Jaspersoft ETL/Talend Open Studio

Users can create, develop, and carry out data integration and data transformation processes using the open-source data integration platform known as Jaspersoft ETL (formerly known as Talend Open Studio for Data Integration).

Pros:

Talend Open Studio reduces developer rates by cutting data handling time in half.
Working with massive datasets requires the efficiency and dependability of Talend Open Studio. Additionally, functional errors happen considerably less frequently than they do with manual ETL.
Several databases, including Microsoft SQL Server, Postgres, MySQL, Teradata, and Greenplum, can be integrated with Talend Open Studio.

Cons:

For businesses searching for a free or inexpensive data integration and transformation solution, a license may be a drawback.
Third-party software dependency: Jaspersoft ETL needs Java and other third-party software components to function.

Pricing:

Depending on the scale, standard plans might cost anywhere from $100 and $1,250 per month; annual payments are discounted.

Best suited for:

Organizations that need to a reliable, scalable solution for data integration and transformation are typically the best fit. Organizations that require data integration with reporting, data visualization, and business intelligence tools will benefit from using Jaspersoft ETL.

14. CloverDX

One of the first Open-Source ETL Tools is CloverDX. It has a data integration framework built on Java that can transform, map, and deal with data in a variety of forms.

Pro:

Automate challenging procedures
Verify data before transferring it to the target system.
Create feedback loops for data quality in your processes.

Cons:

The learning curve is a tad steep at first. Just a bit steep, neither very steep nor really steep.
If the graph is poorly built, having enough memory for huge multi-step issues may become a problem.

Pricing:

The two pricing tiers for CloverDX are CloverDX Designer and CloverDX Server. There is a 45-day trial period for each, followed by set prices.

Best suited for:

This software is ideal for all extract, convert, and load tasks and is well suited for big data processing.

15. Informatica PowerCenter

It is an ETL tool that Informatica Corporation has made available. This tool offers the ability to connect data from numerous data sources and retrieve it. The best implementation ratio, according to Informatica, tends to be 100%. Compared to previous ETL processes, the instructions and software accessibility are much simpler.

Pros:

It has intelligence built in to improve performance.
It offers assistance with updating the Data Architecture.
It offers an error-logging system that is distributed and logs errors.

Cons:

With Informatica PowerCenter, workflow and mapping debugging are quite difficult.
On huge tables, lookup transformation uses more CPU and memory.

Pricing:

Informatica comes in two editions.

Professional Edition - This is an expensive edition that requires a license, and the cost per user each year is $8000.
Personal Edition - You can use it for free and according to your needs.

Best suited for:

Any firm can profit by lowering training costs, and using this software makes it simple to add new personnel.

Other free open source ETL tools exist in addition to the ones mentioned above. Apache Spark, Scriptella, Spring Batch. In the end, you can be confident that your data's quality won't be impacted whether you choose a Paid ETL Tool or an Open-Source Tool.

ETL Overview: What is ETL?

ETL is a procedure that entails gathering information from numerous sources, converting it into a format appropriate for analysis or other uses, and then transferring it to a destination database or data warehouse.
To extract data from operational databases, transactional systems, and other sources, transform the data into a format that is appropriate for analysis and load the data into a data warehouse or other target system, ETL is frequently used in data warehousing and business intelligence (BI) applications.
The Data extraction phase includes unstructured data from a variety of data sources, including databases, flat files, and application log files, is done during the extraction phase.
The data is subjected to numerous transformations throughout the transformation phase, including cleaning, filtering, aggregating, and enriching the data.
The converted data is loaded into a target database or data warehouse during the load phase.
The ETL process is automated to increase its effectiveness using ETL tools and frameworks.

What are some common ETL use cases?

1. Data Migration: ETL can be used for data migration, such as moving data from an on-premises database to a cloud-based data warehouse, from one database or system to another.

2. Data Consolidation: For reporting and analysis, ETL can be used to combine data from several sources into a single repository, such as a data warehouse.

3. Data Integration: To give a complete picture of an organization's data, ETL can be used to combine data from many sources and systems.

4. Data Lake: A data lake is a centralized repository that enables data to be stored in its raw format, making it easier to execute big data analytics. ETL can be used to extract, transform, and load data into a data lake.

5. Data Mart: A data mart is a subset of a data warehouse that is created to meet particular business needs or departments. ETL can be used to extract, convert, and load data into a data mart.

6. Data Quality: By eliminating duplicates, fixing mistakes, and adding missing values, ETL can be used to clean and enhance data.

7. Data auditing: ETL can be used to keep tabs on data modifications and to guarantee that the data remains accurate over time.

8. Data Warehousing: ETL is a critical step in the data warehousing process since it collects, transforms, and loads data from diverse sources into the data warehouse.

Here are 5 examples of ETL processes:

Obtaining customer information from a CRM system, modifying the information by adding additional fields like "customer lifetime value" and "customer segment," and then transferring the improved information to a data warehouse for analysis.
Data extraction from an HR system, data transformation (adding new fields, such as employee tenure and job level), and data loading into a data warehouse for reporting and analysis.
Using an e-commerce platform to extract sales data, transform the data by computing metrics like average order value and customer retention rate, and then load the changed data into a data mart for reporting and analysis.
Financial data is extracted from many sources, like bank statements and invoices, converted by computing metrics like gross margin and net income, and then loaded into a data warehouse for analysis.
Obtaining log data from a web server, parsing the logs to extract pertinent information such as the user agent and IP address, then converting the data before loading it into a data lake for big data analysis- List of the X best free ETL tools.