In this era of Big Data, where the data is growing rapidly, the use of ETL tools has risen leading to a surge in demand for the best ETL tools in the market. An ETL tool extracts the required data from various sources and transforms the data by applying functions, calculations, etc., and then loads the data to the target.
MySQL is a widely used relational database used by various companies to store data from which insights can be drawn. With the right MySQL ETL tool in hand, one can perform a quick transfer of data while maintaining high accuracy.
This post is aimed at introducing you to the top 10 MySQL ETL tools being used to extract data from/to MySQL.
Table of Contents
What is MySQL?
MySQL is the first open-source RDBMS to hit the market. Although there are various competitors to MySQL, it has managed to preserve its reputation and popularity in the market. It’s worth noting that all MySQL variants have the same syntax, implying that MySQL served as the foundation for all of them.
MySQL was created in the mid-1990s to keep data organized and query all of the information in the database using SQL (Structured Query Language). It’s used in conjunction with PHP and Apache Web Server, both of which are Linux-based.
To know more about MySQL, visit this link.
Top 10 MySQL ETL Tools
Typically in an enterprise, the data is stored on legacy systems like RDBMS and it is often a requirement to move from legacy to Cloud or much more fault-tolerant and faster systems.
In a typical scenario, the data is first extracted from the source using connectors and stored in a staging area. Later, it is transformed by using the ETL tools by applying calculations like a filter, aggregation, ranking, etc, to achieve the desired outcome. Then it is loaded onto the target systems. MySQL could act as a source as well as a destination in an ETL operation depending on your use case.
In the below section, the MySQL ETL tools introduced earlier will be explained in-depth throwing light on some of the key features, pricing information, and suitable use cases. You could choose a MySQL ETL tool suited for your needs by going through the use cases under each tool.
Here is a list of the top 10 MySQL ETL tools available in the market. The navigation links below will help you easily traverse the post.
1. Hevo Data
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Key Features of Talend
- Talend has a user-friendly UI with custom-build functions to extract, transform, and load the data.
- Talend has its Open Studio edition for beginners, which is free of cost. The enterprise version is known as Talend Cloud.
- Talend has multiple features like Data Integration, Big Data Integration, Data Preparation, etc.
- Talend has an interactive space that allows Drag and Drop of various functions (called palette) which help in various MySQL ETL operations.
- Talend uses Java at the backend and hence a code is generated whenever there is an operation in Talend space.
- Users can easily connect to legacy systems like RDBMS using this palette and customize it based on their requirements.
- Talend also provides API Services, Data Stewardship, Data Inventory, and B2B.
- Users can design the workflows which may be cloud-to-cloud or hybrid integration and can publish them to AWS or Azure or GCP which are fully managed services.
Suitable Use Case
Talend has wide offerings and can be used in multiple sectors where there is a need to perform quick MySQL ETL with the data. It can be used with Financial Services, Government applications, Risk and Compliance, Data Privacy, etc.
Talend offers multiple categories for pricing and the basic package starts from $12,000 a year. Read more about their pricing here.
3. Apache Spark
Key Features of Apache Spark
- Apache Spark is an open-source lightning-fast cluster computing technology, developed by Apache and designed for fast computation and analysis.
- Apache Spark is based on the fundamentals of Hadoop MapReduce and performs the in-memory computation which makes it very fast as compared to the traditional Hadoop Map Reduce Model.
- The principle of Spark is its in-memory cluster computing. It can handle large volumes of data by parallelizing it against its executor and cores, which in turn the processing speed of an application.
- Apache Spark can be installed in Standalone mode and Clustered mode.
- It is also available via Cloudera, Hortonworks, MapR, Databricks as an integrated platform.
- With the help of JDBC connectors, it is very easy to connect to MySQL databases and extract the data to perform analysis.
- With Apache Spark, the user can connect to legacy databases and extract the data to perform wide transformations.
- Apache Spark uses completely functional programming and hence the user needs to be conversant with Java, Scala, and Python as the coding language.
- Apache Spark also supports real-time streaming and thus performs analytics on real-time data.
- Apache Spark has also good support for Machine Learning libraries.
Suitable Use Case
Spark can be used in standalone mode or the clustered mode with Yarn. Spark can be used where there is a high volume of data and there is a need to perform iterative algorithms and Machine learning on either batch data or real-time data.
Apache Spark is free to use. Users can download Apache Spark from here. However, the distributions like Cloudera, Hortonworks charge for the support. You can check the Pricing here.
4. Apache Hive
Key Features of Apache Hive
- Apache Hive is one of the open source ETL tools for MySQL from the Hadoop ecosystem which sits on top of the Hadoop framework and uses Map Reduce as its backend processing to perform analytics.
- Apache Hive is a distributed data warehouse that uses HDFS as the storage layer and SQL to perform a query on the dataset.
- Apache Hive with the combination of Apache Sqoop, can extract the data from the legacy sources and performs the transformation on the data.
- Apache Hive supports multiple file formats such as Parquet, JSON, Avro, XML, Text, CSV.
- Apache Hive provides an SQL interface to perform analytics and thus helps users who are from legacy backgrounds to shift to data analytics without much learning of coding.
Suitable Use Case
Hive sits on top of the Hadoop ecosystem and integrates with Yarn. Hive can be used to perform a quick transformation on the data with an SQL interface.
Apache Hive is an open-source MySQL ETL tool and is free to use. Users can download Apache Hive from here. However, the distributions like Cloudera, Hortonworks charges for the support.
Download the Guide to Evaluate ETL Tools
Learn the 10 key parameters while selecting the right ETL tool for your use case.
5. Apache NiFi
Key features of Apache NiFi –
- Apache NiFi is an open-source tool to provide the automated data flow between source and destination and is used to perform MySQL ETL while the data inflows.
- Seamless integration with MySQL via connectors is available.
- Apache NiFi allows us to perform transformations and analytics by pulling up the data from various sources via various connectors to create flow files.
- Apache NiFi is not coupled with any resources and hence can be used in standalone mode.
- Apache NiFi has a web-based interface that allows users for a seamless design, control, and monitoring.
Suitable Use Case
Apache NiFi can be used in Insurance, Telecommunication, Health care, and Financial Service Industries to perform Risk Analysis, Anti Money Laundering, Quality Control, etc.
Apache NiFi is provided by BatchIQ and it is integrated with AWS Marketplace. More pricing details can be seen here.
Key Features of Pentaho
- Pentaho is a MySQL ETL tool commonly known as Pentaho Data Integration. It has an innovative metadata-driven approach.
- It is used to transform the data and also generate reports in XML, CSV, HTML, PDF, and Text format.
- Pentaho provides an easy interface to connect to MySQL via JDBC connection, which allows us to extract the data and transform and migrate it to different applications.
- With Pentaho, users can perform data cleaning, data quality checks, and data standardization which includes simple to complex transformation.
- Pentaho is a fully managed service and can be installed in minutes.
- Pentaho can also perform Hadoop functions like MapReduce, AWS EMR integration.
Suitable Use Case
Pentaho is normally used when there is a need for a simple open-source tool in an on-premise setup. With Pentaho, one can easily manage, schedule, transform, and migrate data from one system to another.
Pentaho community edition is free to use. However, the enterprise version is paid and pricing is available on request.
7. GCP Dataflow
Key Features of Data Flow
- Google Cloud Dataflow is a service provided by Google Cloud Platform and is a fully managed MySQL ETL tool based on Apache Beam.
- GCP Dataflow allows users to get the data from the RDBMS particularly MySQL and users can set up flows to perform transformations and analytics over the data.
- GCP Dataflow works really well for batch and real-time.
- GCP Dataflow uses serverless MySQL ETL pipelines to deploy the code.
- GCP DataFlow provides an enriched UI to create the flow of the data.
Suitable Use Case
Dataflow is tightly coupled with the Google Cloud Platform and it can be used when one has the Google Cloud. Dataflow is a very enriched tool and provides out-of-the-box functions to perform transformations and analytics.
Google Cloud Platform offers $300 worth of credits to a new user to try out their platform. The pricing is based on utilizable parameters like CPU usage, memory, data storage, data processed, etc. For complete pricing, you can refer here.
8. AWS Glue
Key Features of AWS Glue
- Amazon Web Services is a cloud-based service with wide offerings including Compute, ETL, Realtime Ingestion, ML, Containerization, etc.
- AWS Glue is a cloud-based real-time MySQL ETL tool that uses Python as its base language.
- AWS Glue has its pre-built transformation which can be readily used and also allows users to create their custom functions.
- AWS Glue mostly works with batch data, but in combination with other offerings of AWS like Lambda or Step, a near-real-time scenario can be achieved.
- AWS Glue is nicely packed with features such as Schema Discovery, Data Catalog, Connection to data sources, and more.
- With AWS serverless orchestration, one can easily use Glue to perform extract and transformation without worrying about other parameters.
Suitable Use Case
AWS Glue is useful when you want to go completely serverless and are primarily using the AWS ecosystem. It provides serverless orchestration and manages infrastructure on its own. In today’s era of cloud technology, AWS Glue is gaining popularity.
AWS Glue has a pay-as-you-go pricing model. It charges an hourly rate, billed by the second. Check about AWS Glue pricing here.
9. Azure Data Factory
Key Features of Azure Data Factory
- Microsoft Azure Data Factory is the MySQL ETL counterpart for AWS Glue and Google Cloud Dataflow.
- Microsoft Azure Data Factory is a fully managed service with all the components tightly coupled with Microsoft architecture.
- Data Factory supports batch and real-time data to fetch the data from MySQL and perform the transformation and analytics.
- Data Factory can run a completely serverless MySQL ETL pipeline using Azure components.
Suitable Use Case
Data Factory is a good alternative for people well invested in the Azure ecosystem. Customers who are comfortable with data being on the Azure cloud and do not have multi-cloud or hybrid cloud requirements can prefer this.
Azure Data Factory is priced based on the number of activity runs per month. You can find more details about the pricing here.
10. GCP Data Fusion
Key Features of Google Data Fusion
- GCP Data Fusion is another MySQL ETL tool offered by Google under its Google Cloud Platform.
- GCP Data Fusion has a hub that offers multiple connectors to connect to legacy data sources.
- It has a very nicely presented UI with all the functionality as the block.
- Users can use drag-and-drop functions to connect from legacy sources, transform the data, and migrate it to a target destination.
- Google offers this package under its Cloud platform and can be used in conjecture with its other tools like BigQUery, Data Proc, Big Tables, etc.
- It runs Dataproc at the backend to perform the analysis which in turn runs on Spark. GCP Data Fusion offers custom coding with Python and Spark.
Suitable Use Case
GCP Data Fusion can be used when you don’t want to manage the architecture and perform the MySQL ETL on the go. It is tightly coupled with the Google platform and can be used with related applications. It is also one of the popular MySQL ETL tools.
GCP offers $300 worth of credit for a new sign-up and allows free usage of all the applications. Data Fusion comes with two pricing plans: $1.8 per hour (Basic) and $4.2 per hour (Enterprise). You can check more about pricing here.
There are a plethora of MySQL ETL tools available in the market and one may suit you better than the other depending on your particular use case, data sources, existing applications, etc. If you are looking for a hassle-free, fully-managed solution to your data integration problems instead of writing MySQL ETL script yourself, Hevo can be your go-to option.
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If you’re looking for an all-in-one solution, that will not only help you transfer data but also transform it into analysis-ready form, then Hevo Data is the right choice for you! It will take care of all your analytics needs in a completely automated manner, allowing you to focus on key business activities.
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Which is your preferred ETL tool for MySQL? Share your thoughts in the comments section.