Open source data engineering tools are becoming increasingly popular among organizations looking to manage and analyze their data more effectively. These tools provide a wide range of functionality and can be customized to meet the unique needs of different organizations. In this article, we will highlight the top 10 open source data engineering tools that are currently available.
Apache Hadoop
One of the most widely used open source data engineering tools, Apache Hadoop is a distributed file system that allows for the processing of large amounts of data. It is used for data storage and processing, and can be used in a variety of different environments, including on-premises, in the cloud, or in a hybrid environment.
You can learn more about Hadoop in our glossary entry.
Apache Spark
Another popular open source data engineering tool, Apache Spark is a fast and general-purpose cluster computing system. It can be used for big data processing, machine learning, and real-time streaming. Spark is designed to be highly extensible, making it easy to add new functionality and integrate with other tools.
Apache Kafka
A distributed streaming platform that can be used to process and analyze data in real-time. Kafka is designed to handle large amounts of data and can be used to build real-time data pipelines, real-time data processing systems, and real-time data analytics applications.
Apache Storm
A distributed, real-time computation system that can be used to process and analyze large amounts of data in real-time. Storm is designed to be fault-tolerant and can be used for a variety of different use cases, including real-time data processing, real-time data analytics, and real-time data visualization.
Apache Flink
A distributed data processing framework that can be used to process and analyze large amounts of data in real-time. Flink is designed to be highly scalable and can be used for a variety of different use cases, including real-time data processing, real-time data analytics, and real-time data visualization.
Apache Nifi
A data integration tool that can be used to automate the movement and transformation of data. Nifi is designed to be highly configurable and can be used for a variety of different use cases, including data integration, data warehousing, and data analysis.
Apache Cassandra
A distributed, NoSQL database that can be used to store and manage large amounts of data. Cassandra is designed to be highly available and can be used for a variety of different use cases, including real-time data processing, real-time data analytics, and real-time data visualization.
Apache Airflow
A platform to programmatically author, schedule, and monitor workflows. It is used to manage ETL pipeline, data pipeline and machine learning pipeline.
Learn more about Extract, Transform, Load (ETL) in our AI & big data glossary.
Apache Superset
A data visualization tool that can be used to create interactive dashboards and visualizations. Superset is designed to be highly configurable and can be used for a variety of different use cases, including data analysis, data warehousing, and data visualization.
Learn more about Data Visualization in our AI & big data glossary.
Apache Kylin
An open-source distributed analytical data warehouse for big data that is built on Apache Hadoop and Apache Hive. It can support extremely large datasets and enable SQL-like queries against petabytes of data.
Don’t know how large a petabyte is? Check out our AI & Big data glossary entry.
These are just some of the open source data engineering tools that are currently available. Each of these tools has its own unique set of features and capabilities, and the best tool for your organization will depend on your specific needs and requirements. It’s important to understand the skill set of your team to see if they need more beginner friendly tools. Open source community participation and seeing how active the community is are also important things to consider when picking the open source data engineering tool that’s right for your team.
