Best Data Management Software for Apache DataFusion
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Compare the Top Data Management Software that integrates with Apache DataFusion as of October 2025
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This a list of Data Management software that integrates with Apache DataFusion. Use the filters on the left to add additional filters for products that have integrations with Apache DataFusion. View the products that work with Apache DataFusion in the table below.
What is Data Management Software for Apache DataFusion?
Data management software systems are software platforms that help organize, store and analyze information. They provide a secure platform for data sharing and analysis with features such as reporting, automation, visualizations, and collaboration. Data management software can be customized to fit the needs of any organization by providing numerous user options to easily access or modify data. These systems enable organizations to keep track of their data more efficiently while reducing the risk of data loss or breaches for improved business security. Compare and read user reviews of the best Data Management software for Apache DataFusion currently available using the table below. This list is updated regularly.
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SQL
SQL
SQL is a domain-specific programming language used for accessing, managing, and manipulating relational databases and relational database management systems.Starting Price: Free -
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Apache Arrow
The Apache Software Foundation
Apache Arrow defines a language-independent columnar memory format for flat and hierarchical data, organized for efficient analytic operations on modern hardware like CPUs and GPUs. The Arrow memory format also supports zero-copy reads for lightning-fast data access without serialization overhead. Arrow's libraries implement the format and provide building blocks for a range of use cases, including high performance analytics. Many popular projects use Arrow to ship columnar data efficiently or as the basis for analytic engines. Apache Arrow is software created by and for the developer community. We are dedicated to open, kind communication and consensus decisionmaking. Our committers come from a range of organizations and backgrounds, and we welcome all to participate with us. -
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Apache Parquet
The Apache Software Foundation
We created Parquet to make the advantages of compressed, efficient columnar data representation available to any project in the Hadoop ecosystem. Parquet is built from the ground up with complex nested data structures in mind, and uses the record shredding and assembly algorithm described in the Dremel paper. We believe this approach is superior to simple flattening of nested namespaces. Parquet is built to support very efficient compression and encoding schemes. Multiple projects have demonstrated the performance impact of applying the right compression and encoding scheme to the data. Parquet allows compression schemes to be specified on a per-column level, and is future-proofed to allow adding more encodings as they are invented and implemented. Parquet is built to be used by anyone. The Hadoop ecosystem is rich with data processing frameworks, and we are not interested in playing favorites. -
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SDF
SDF
SDF is a developer platform for data that enhances SQL comprehension across organizations, enabling data teams to unlock the full potential of their data. It provides a transformation layer to streamline query writing and management, an analytical database engine for local execution, and an accelerator for improved transformation processes. SDF also offers proactive quality and governance features, including reports, contracts, and impact analysis, to ensure data integrity and compliance. By representing business logic as code, SDF facilitates the classification and management of data types, enhancing the clarity and maintainability of data models. It integrates seamlessly with existing data workflows, supporting various SQL dialects and cloud environments, and is designed to scale with the growing needs of data teams. SDF's open-core architecture, built on Apache DataFusion, allows for customization and extension, fostering a collaborative ecosystem for data development.
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