Best Data Management Software for Spark

Datameer is your go-to data tool for exploring, preparing, visualizing, and cataloging Snowflake insights. From exploring raw datasets to driving business decisions – an all-in-one tool.

HugeGraph is a high-speed, highly-scalable graph database. HugeGraph's excellent OLTP capability allows for the storage and querying of billions of edges and vertices. Gremlin, a powerful graph traversal and query language, can handle complex graph queries in compliance with Apache TinkerPop 3. It supports Gremlin and is compliant to Apache TinkerPop 3. Schema Metadata Management includes VertexLabel EdgeLabel PropertyKey and IndexLabel. Multi-type Indexes that support complex combination queries, range query, and exact query. Plug-in Backend Store Driver Framework. Supports RocksDB, Cassandra and ScyllaDB. It is easy to add another backend store driver if necessary. Integration with Hadoop/Spark. HugeGraph is built on the TinkerPop framework. We refer to the storage structure and schema definition of DataStax.

BDB is a modern data analysis and BI platform that can dig deep into your data to uncover actionable insights. It can be deployed on-premise or in the cloud. Our unique microservices-based architecture includes elements such as Data Preparation and Predictive, Pipeline, Dashboard designer, and Pipeline. This allows us to offer customized solutions and scalable analysis to different industries. BDB's NLP-based search allows users to access the data power on desktop, tablet, and mobile. BDB is equipped with many data connectors that allow it to connect to a variety of data sources, apps, third-party API's, IoT and social media. It works in real-time. It allows you to connect to RDBMS and Big data, FTP/ SFTP Server flat files, web services, and FTP/ SFTP Server. You can manage unstructured, semi-structured, and structured data. Get started on your journey to advanced analysis today.

DashDB Local is the newest edition of the IBM dashDB family. It provides organizations with the most flexible architecture to lower the cost of analytics in the dynamic worlds of big data and cloud. How is this possible? Analytics workloads can be easily moved and optimized by a common engine with different deployment options across public and private clouds. DashDB Local is an option if you prefer to deploy on a hosted private clouds or on-premises private clouds through a software defined infrastructure. DashDB Local is easy to deploy and manage from an IT perspective. It uses container technology with elastic scaling and simple maintenance. DashDB Local is user-friendly. It provides the speed to cycle through data acquisition, apply the right analytics to meet a particular use case, and then operationalizes the insights.

Atlas is a flexible and extensible set core foundational governance services that enable enterprises to efficiently and effectively meet their compliance requirements within Hadoop. It also allows integration with the entire enterprise data ecosystem. Apache Atlas offers open metadata management and governance capabilities that allow organizations to create a catalog of their data assets, classify, govern and provide collaboration capabilities around these assets for data scientists, analysts, and the data governance group. Pre-defined types to manage various Hadoop and non Hadoop metadata. Ability to create new types to manage metadata. Types can inherit from other types, and can have simple attributes, complex attributes, and object references. Type instances, also known as entities, are able to capture metadata object details and their relationships. REST APIs allow for easier integration with types and instances.