Best Data Management Software for Spark

Compare the Top Data Management Software that integrates with Spark as of September 2025

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This a list of Data Management software that integrates with Spark. Use the filters on the left to add additional filters for products that have integrations with Spark. View the products that work with Spark in the table below.

What is Data Management Software for Spark?

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 Spark currently available using the table below. This list is updated regularly.

1

Datameer

Datameer

Datameer revolutionizes data transformation with a low-code approach, trusted by top global enterprises. Craft, transform, and publish data seamlessly with no code and SQL, simplifying complex data engineering tasks. Empower your data teams to make informed decisions confidently while saving costs and ensuring responsible self-service analytics. Speed up your analytics workflow by transforming datasets to answer ad-hoc questions and support operational dashboards. Empower everyone on your team with our SQL or Drag-and-Drop to transform your data in an intuitive and collaborative workspace. And best of all, everything happens in Snowflake. Datameer is designed and optimized for Snowflake to reduce data movement and increase platform adoption. Some of the problems Datameer solves: - Analytics is not accessible - Drowning in backlog - Long development

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2

HugeGraph

HugeGraph

HugeGraph is a fast-speed and highly-scalable graph database. Billions of vertices and edges can be easily stored into and queried from HugeGraph due to its excellent OLTP ability. As compliance to Apache TinkerPop 3 framework, various complicated graph queries can be accomplished through Gremlin (a powerful graph traversal language). Among its features, it provides compliance to Apache TinkerPop 3, supporting Gremlin. Schema Metadata Management, including VertexLabel, EdgeLabel, PropertyKey and IndexLabel. Multi-type Indexes, supporting exact query, range query and complex conditions combination query. Plug-in Backend Store Driver Framework, supporting RocksDB, Cassandra, ScyllaDB, HBase and MySQL now and easy to add other backend store driver if needed. Integration with Hadoop/Spark. HugeGraph relies on the TinkerPop framework, we refer to the storage structure of Titan and the schema definition of DataStax.

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3

BDB Platform

Big Data BizViz

BDB is a modern data analytics and BI platform which can skillfully dive deep into your data to provide actionable insights. It is deployable on the cloud as well as on-premise. Our exclusive microservices based architecture has the elements of Data Preparation, Predictive, Pipeline and Dashboard designer to provide customized solutions and scalable analytics to different industries. BDB’s strong NLP based search enables the user to unleash the power of data on desktop, tablets and mobile as well. BDB has various ingrained data connectors, and it can connect to multiple commonly used data sources, applications, third party API’s, IoT, social media, etc. in real-time. It lets you connect to RDBMS, Big data, FTP/ SFTP Server, flat files, web services, etc. and manage structured, semi-structured as well as unstructured data. Start your journey to advanced analytics today.

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4

dashDB Local

IBM

As the newest edition to the IBM dashDB family, dashDB Local rounds out IBM's hybrid data warehouse strategy, providing organizations the most flexible architecture needed to lower the cost model of analytics in the dynamic world of big data and the cloud. How is this possible? Through a common analytics engine, with different deployment options across private and public clouds, analytics workloads can be moved and optimized with ease. dashDB Local is now an option when you prefer deployment on a hosted private cloud or on-premises private cloud through a software-defined infrastructure. From an IT standpoint, dashDB Local simplifies deployment and management through container technology, with elastic scaling and easy maintenance. From a user standpoint, dashDB Local provides the speed needed to quickly cycle through the process of data acquisition, applies the right analytics to meet a specific use case, and operationalizes the insights.

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5

Apache Atlas

Apache Software Foundation

Atlas is a scalable and extensible set of core foundational governance services – enabling enterprises to effectively and efficiently meet their compliance requirements within Hadoop and allows integration with the whole enterprise data ecosystem. Apache Atlas provides open metadata management and governance capabilities for organizations to build a catalog of their data assets, classify and govern these assets and provide collaboration capabilities around these data assets for data scientists, analysts and the data governance team. Pre-defined types for various Hadoop and non-Hadoop metadata. Ability to define new types for the metadata to be managed. Types can have primitive attributes, complex attributes, object references; can inherit from other types. Instances of types, called entities, capture metadata object details and their relationships. REST APIs to work with types and instances allow easier integration.

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