A Short List of Open-Source Databases for Analytics Profiling 3 Tools

Open-Source Databases for Analytics

Solutions Review editors compiled this short list of the best open-source databases for analytics to consider right now.

Searching for data management and database software can be a daunting (and expensive) process, one that requires long hours of research and deep pockets. The most popular enterprise database tools often provide more than what’s necessary for non-enterprise organizations, with advanced functionality relevant to only the most technically savvy users. Thankfully, there are a number of options we profile in this open-source database list. Some of these solutions are offered by vendors looking to eventually sell you on their enterprise product, and others are maintained and operated by a community of developers looking to democratize the data management space.

In this article, we will examine free and open-source databases for analytics, first by providing a brief overview of what to expect and also with short blurbs about each of the currently available options in the space.

Open-Source Databases for Analytics

Apache Hive

Apache HiveApache Hive is an open-source data warehouse built on top of the Apache Hadoop ecosystem. It was designed to facilitate data summarization, ad-hoc queries, and the analysis of extremely large data volumes stored in various databases and file systems that integrate with Hadoop. Hive offers an excellent package for applying structure to large amounts of unstructured data and perform batch SQL-like queries. It integrates with traditional data center solutions that use the JDBC/ODBC interface.


Neo4j 106Neo4j is an open-source graph database management system that is designed for optimizing fast management, storage, and traversal of nodes and relationships. Neo4j provides real-time performance, and features a flexible schema, drivers for popular languages and frameworks, cloud connectivity, hot backups, and data import capabilities. Common use cases for this tool include software analytics, network management, matchmaking, scientific research, and project management.


TitanTitan is a scalable graph database designed for storing and querying graphs containing hundreds of billions of vertices and edges distributed across multi-machine clusters. It is a transactional database and can support thousands of concurrent users. Key features include data distribution and replication for performance and fault tolerance, multi-datacenter high availability and hot backups, and support for ACID and eventual consistency. Titan also offers support for various storage backends and global graph data analytics.

Timothy King
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