Free ETL Tools for MySQL: 9 Best Ones to Use

Don’t know where to start with ETL for MySQL?

If yes, keep reading!

You might have been using a tool that requires a massive setup, offers no community-driven support, and provides very niche features in the free plan. Many open-source or freemium options available in the market either lack key features, are difficult to scale, or don’t integrate well with MySQL. 

We understand how challenging it can be to find free MySQL ETL tools!

The solution, you ask?
In this blog, we explore the top free ETL tools for MySQL, their pros & cons, and factors to consider while choosing. If you are fed up with Googling “best free MySQL ETL tools”, here’s an overview of the top 5 tools:

Top ETL Tools for MySQL

1. Hevo Data

Hevo Data is a cloud-based, no-code ETL platform that supports log-based CDC, including MySQL BinLog ingestion. Hevo monitors and streams changes directly from the MySQL binary log to capture schema modifications. Using its intuitive UI, you can set up MySQL either as a source or as a destination within the pipeline creation process. 

Moreover, Hevo offers support for databases like PostgreSQL (logical replication), SQL Server (change tracking), MongoDB (OpLog), and Oracle (redo logs), making it a versatile solution for CDC across diverse systems.

Hevo’s ETL architecture is designed to help businesses automate and streamline data pipelines with minimal technical resources.

With Hevo Data, you create a connection out of 1000 objects, select 10 objects you really need, apply minimal transformations, and Hevo Data will take care of it. It will run every day, send you notifications, you can go back, you can restart the pipeline, and it will just refresh the data.

Karan Singh Khanuja

Data Scientist

View Customer Story

Key features:

• Data replication: Hevo supports real-time data streaming and CDC using binary log (binlog) replication. By reading MySQL’s binary logs, Hevo captures incremental changes, including insert, update, and delete from source databases, ensuring only updated records are replicated.

• Schema handling: Automatically detects and adapts to schema changes in MySQL, such as new columns or tables, to implement them in the destination. Unsupported MySQL data types are automatically flagged during pipeline creation.

• Scalability: With Hevo, you get a world-class fault-tolerant architecture that scales with zero data loss and low latency.

Pros:

• Drag-and-drop interface for non-technical users.
• Built-in transformation mechanism to deliver analysis-ready data.
• Offers in-built logging and alerting tools with real-time pipeline visibility.

Cons:

• Might experience latency issues with complex transformations.
• Provides limited on-premise support.

2. Apatar

Apatar is an open-source ETL platform that runs as a desktop application, known for its ease of use in connecting to MySQL databases. The platform enables you to build add-on connectors, enhancing flexibility for standard integration needs, such as APIs, legacy systems, and semi-structured data sources.

It is well-suited for users using CRM systems, as Apatar transports and synchronizes customer data into existing systems and third-party databases or applications. Moreover, its integration with data sources generates XML metadata files to feed pre-built DataMaps to the Apatar server engine.

Key features:

• Interface: It features a drag-and-drop interface that enables users with no technical background to build and manage data pipelines. You can directly access built-in app integration tools, de-duplication, and data quality tools through the interface.

• Data mapping: The visual job designer provides mapping functionality that maps data fields between the source and target systems as part of the ETL process.

• MySQL connector: Apatar offers pre-built connectors that facilitate connectivity with major data sources and formats, allowing users to transfer data across numerous platforms, including MySQL.

Pros:

• Enables two-way sync between MySQL and other platforms.
• Supports mapping of both database schemas and individual attributes.
• Enables quick installation with minimal system requirements.

Cons:

• Large databases might experience slow synchronization and processing speed.
• Lacks real-time data integration.
• Limited support for data sources and destinations.

3. OpenMRS

OpenMRS is a medical record system platform that helps healthcare companies perform swift analysis of sensitive data. The platform has a modular architecture and a metadata management system that supports local healthcare workflows without changing the underlying database structure.

The unique offering is its interoperability, which supports integration with other information systems. This allows organizations to share, consolidate, and analyze data across diverse platforms and systems. OpenMRS is specifically optimized for patient care by using MySQL as the backend database.

Key features:

• Predictive modelling: OpenMRS integrates with external ML frameworks to track historical sources for gathering relevant data. The data is used to perform predictive modelling tasks, such as mortality risk prediction.

• ETL: Supports both batch and streaming data ingestion to enable real-time analytics and minimize the burden on production systems.

• Data model: The platform provides a customizable data model and REST APIs to interact with MySQL-stored data, enabling integration with external systems.

I like that I am able to use this application without having too much of a background in programming. Ease of use and flexible Review collected by and hosted on G2.com.

Verified User

Hospital & Health Care

Pros:

• Free to use and backed by a large open-source community.
• MySQL’s ACID compliance ensures reliable healthcare data management.
• Offers deployment flexibility by running on major operating systems.

Cons:

• Traditional design can lead to slow upgrades and customization.
• Required additional tools for advanced analytics.
• Complex to manage large-scale OpenMRS deployments.

4. Csv2db

Csv2db is a command-line tool developed to load CSV files into a database with robust MySQL support. The open-source model operates on plain text and multiple compressed files to enable data insertion into the database, followed by deep cleansing of CSV data. It lets you configure table creation options directly from the command line or script.

Its core value lies in its ability to read all the CSV names at once, helping users in loading data into the same database tables. The tool also enables you to load CSV files into MySQL databases from a Python environment, provided the appropriate MySQL drivers are installed.

Key features:

• Table creation: Automates scanning of CSV file headers to generate a CREATE TABLE statement for MySQL, simplifying setup for new CSV formats.

• File support: Csv2db extracts data from compressed CSV files (e.g., ZIP) without manual decompression and loads it directly into relational databases such as Oracle, MariaDB, PostgreSQL, and SQL Server via JDBC.

• Command-line options: Provides various CLI options, like customizing the column type, table name, and database type (including MySQL).

Pros: 

• Imports CSV files into MySQL without any complex setup.
• Ideal for batch processing and bulk imports.
• Suitable for scripting and pipeline automation.

Cons:

• Lacks built-in data transformation and validation.
• Not ideal for error handling in complex datasets.
• Not suitable for large-scale and incremental ETL processes.

5. Transformalize

Transformalize is an open-source ETL tool hosted on GitHub. It is designed to denormalize relational data and reduce procedural steps to post queries and retrieve results. The ETL tool puts normalized data into tables and automates data migration into search engines and data warehouses. 

Rather than relying on code, Transformalize uses configuration files to define ETL workflows. It aims at making relational data easy to operate in multiple ways:

• As an OLAP cube data source
• To feed a SOLR or Elasticsearch index
• To provide quicker, easier access to regular SQL queries and reports

Key features:

• YAML configuration: Users can define which fields to extract, how to transform them (including calculations and validation), and the output appearance using YAML configuration.

• Schema creation: During initialization, Transformalize automatically generates target schema structures based on the configuration file. This streamlines the creation of output tables and reduces manual setup.

• Metadata-driven transformations: It builds transformation logic based on metadata definitions to automate dependency tracking and simplify pipeline maintenance across multiple data sources.

Pros:

• Provides portable configuration files that can be version-controlled.
• Runs on various OS platforms (Windows, Linux, macOS).
• Enables easy integration into automated workflows, CI/CD pipelines, and scheduling systems.

Cons:

• Steep learning curve as configurations require familiarity with YAML and CLI.
• Limited integrations, plugins, and community resources.
• Needs additional setup for advanced monitoring and error handling.

6. Talend Big Data Open Studio

Talend Open Studio (owned by Qlik) for Big Data is an ETL and data integration tool designed to convert, combine, manipulate, and update data in various locations. Talend for MySQL enables metadata-driven development, allowing users to automate schema mapping and track data lineage for improved data governance.

The core offering includes built-in tools like Talend Pipeline Designer, Talend Cloud Data Integration, and Talend Data Fabric. These tools streamline extensive data transformations and complex ETL workflows across multiple databases, including MySQL.

Key features:

• Connector: Facilitates seamless integration through 900 pre-built connectors to foster smooth data migration from any source to various destinations. The supported sources include back-office applications, legacy databases, SaaS applications, and IoT devices.

• MySQL components: Includes dedicated connectors such as tMysqlConnection, tMysqlInput, and tMysqlOutput for seamless reading and writing to MySQL databases.

• Security: The tool incorporates advanced security features and complies with industry standard regulations, including SOC 2 Type 2, HIPAA, GDPR, ISO/IEC 27001:2013, and ISO/IEC 27701:2019.

Pros:

• Supports both batch and real-time processing for various MySQL scenarios.
• Drag-and-drop UI eliminated manual coding.
• Provides 24/7 uptime and comprehensive transactional support with high-availability solutions.

Cons:

• Basic debugging and error handling features.
• Advanced configurations require technical expertise.
• Bulk data loads into MySQL are slower than native MySQL tools.

UI of Talend Open studio is straightforward to use and understand. Easily users can set up big queries and join the tables, which is amazingly helpful and a time-saver when using big data for operations.

Archana J.

Application Analyst

7. Pentaho Kettle

Pentaho Data Integration (PDI) is a codeless ETL tool used for designing, orchestrating, and automating data pipelines. Using its user-friendly UI, users can set up MySQL connections by specifying the host, database, username, and password.

Besides MySQL, PDI connects with flat files, XML, Excel, NoSQL databases, and cloud sources, integrating diverse data sets into a unified view for analysis. The integrated environment helps you operate in close coordination with business users and develop BI solutions efficiently.

Key features:

• MySQL connectivity: PDI provides out-of-the-box support for connecting to MySQL databases using JDBC drivers. This native integration fosters a stable, performant, and secure workflow between PDI and MySQL.

• ETL designer: PDI features a GUI called “Spoon”, which empowers users to build ETL pipelines visually without manual coding. This reduces complexities in building data pipelines to promote data movement into, out of, or between MySQL tables.

• Error-handling: PDI offers row-level logging to capture failures in MySQL-related transformations. These logs can be saved to text files, MySQL tables, or the PDI repository for later review.

Pros:

• Has real-time and batch data integration capabilities.
• Built-in error handling, monitoring, and logging features.
• Automates ETL jobs for MySQL pipelines.

Cons:

• Connectors can experience portability issues.
• Doesn’t provide detailed product documentation.
• Modifying complex jobs is time-consuming.

Pentaho is one of the best etl tool to extract ,transform and load the data among various sources ,it just requires connections of the database and transfers data very fast.

Sandeep C.

Data Analyst

8. Domo

Domo is a cloud-based data integration and BI platform that supports connecting, transforming, and visualizing MySQL data. You can simply create ETL pipelines using the tool’s SQL dataflow features and create MySQL expressions directly within the tool. 

The tool enables organizations to seamlessly integrate with diverse data sources and automated workflows for analytics. Once connected, you can build interactive dashboards, charts, and reports using Domo’s visualization tools and make informed business decisions. This is useful for optimizing large data pulls.

Key features:

• MySQL connector: Domo features a dedicated MySQL connector that easily connects with MySQL databases and imports data for analysis and visualization. It supports both on-premise and cloud-hosted MySQL scenarios.

• Data writeback: Domo facilitates bi-directional data flows that write back processed or enriched data to MySQL or other systems. This is used to feed processed data into operational systems and update CRM records.

• Security: Domo offers encryption, granular access controls, and SSO integration to protect MySQL data throughout its lifecycle.

Pros:

• Has real-time data accessibility and collaboration features.
• Offers a drag-and-drop interactive dashboard to simplify navigation.
• Provides 1000+ pre-built connectors.

Cons:

• Application of complicated dashboards requires programming knowledge.
• Poor customization of workflows.
• Low responsiveness of customer support.

Domo is a robust and highly scalable platform that offers a lot of flexibility to meet our business needs. It performs reliably and provides out-of-the-box integrations with many data services.

John G.

Managing Member

9. Apache Spark

Apache Spark is a multi-language engine that integrates with MySQL to execute large-scale analytics, ETL jobs, and data engineering workflows. The platform is known to run data workloads 100 times quicker than other options. 

Its key offering is that it combines the transactional MySQL storage with Spark’s scalable analytics and ETL capabilities to speed up analytics. The platform’s speed makes it an excellent solution for processing large datasets. Moreover, Spark supports both batch ETL and real-time data analytics depending on the setup.

Key features:

• Connectivity: Spark connects to MySQL using the JDBC protocol, allowing both reading from and writing to MySQL tables directly from Spark jobs.

• Query processing: Spark leverages cluster resources to process large datasets faster than running queries directly in MySQL. This runs the SQL queries on MySQL data in a distributed manner.

• SQL support: You can load MySQL tables as Spark DataFrames to use Spark SQL for advanced analytics, filtering, and transformations. It runs distributed analytics, performs aggregations on the loaded data, and uses Spark’s in-memory for faster performance.

Pros:

• Offers multilingual support (Python, Java, Scala) to expand accessibility.
• Supports complex analytics and stream processing on MySQL datasets.
• Enables large-scale data processing beyond a single MySQL server.

Cons:

• No built-in support for event-based processing.
• Poor streaming of large datasets.
• Has a steep learning curve with limited resources.

What I like best about Spark Streaming is its ability to handle real-time data processing efficiently while maintaining high throughput. It enables seamless integration with the Apache Spark ecosystem, providing access to a wide range of libraries and tools.

Sai Kiran S.

Specialist Programmer

How to Choose an ETL Tool for MySQL Database?

You must choose an ETL tool based on your particular set of requirements which may be a combination of various critical factors specific to your database. A few of these critical factors are considered as follows:

1. Ability to Integrate With Various Data Sources

While finalizing an ETL tool, you should be aware of the presence of comprehensive connector support, which covers the database systems, marketing entities, management software and other controlled services. The selection of comprehensive support is necessary for future-proofing of the ETL pipeline, regardless of the number of SaaS providers and databases.

2. Complete Monitoring and Management

Ensure that the tool includes a feature that serves as a checkpoint for quality control. The quality control feature should specify the system’s behavior when errors are detected or when standard conditions are not met during execution. Defining the execution process step-by-step is essential for selecting the ETL tool. 

3. Real-Time Data Streaming

ETL tools should provide the accessibility to transport data from internal to external sources and transform them in real-time. It should also enhance its ability to provide data within an integration batch. During consideration of the best-suited ETL tool, the mechanisms for the determination of system variances should be brought to light.

4. Reliable Data Load

Since ETL tools are closely coupled with active data warehouses, any imperfection can result in inefficient pipelines and unreliable systems, causing service-level agreement violations. Therefore, establish a strong relationship between the tool and the data warehouse.

Understanding MySQL ETL

What is MySQL Data Integration?

MySQL Data Integration is the process of combining data from one or more MySQL databases—or integrating MySQL data with other data sources—into a cohesive system for analysis, reporting, or application development. This typically involves an ETL (Extract, Transform, Load) process:

• Extract: Pull data from MySQL databases and other sources.
• Transform: Clean, standardize, and convert the data to a common format.
• Load: Insert the transformed data into a target system, such as another MySQL database or a data warehouse.

Data integration can be performed in batch mode at scheduled intervals or in real time for up-to-date synchronization. Tools like Talend, Pentaho, and MySQL’s built-in replication features help in ETL automation and streamline these processes.

Advantages of MySQL ETL Tools

• A good MySQL ETL process is easy to automate and user-friendly.
• The advantages of MySQL ETL tools are outsourced complexity and accelerated time-to-value.
• You can configure your own Python-based data integrations by following tutorials, but this can be overly complex and not very user-friendly, especially when dealing with multiple data flows and data pipelines.

Conclusion

You have now seen various free tools for MySQL. While most of them provide an open-source edition, it may be beneficial to subscribe to the paid edition for added benefits. This article seeks to familiarize you with the different available tools to enable you to make the right choice.

Hevo is a cloud-based, no-code ETL platform built with the focus of data ingestion and ETL services. It offers a full-scale database replication system that is incremental and integrated with additional features such as timestamp and changing the data capture system.

Sign up for a 14-day free trial now.

FAQs on ETL Tools for MySQL