Best Database Software for BigBI

Find and compare the best Database software for BigBI in 2025

Use the comparison tool below to compare the top Database software for BigBI on the market. You can filter results by user reviews, pricing, features, platform, region, support options, integrations, and more.

  • 1
    Apache Cassandra Reviews

    Apache Cassandra

    Apache Software Foundation

    1 Rating
    The Apache Cassandra database provides high availability and scalability without compromising performance. It is the ideal platform for mission-critical data because it offers linear scalability and demonstrated fault-tolerance with commodity hardware and cloud infrastructure. Cassandra's ability to replicate across multiple datacenters is first-in-class. This provides lower latency for your users, and the peace-of-mind that you can withstand regional outages.
  • 2
    MongoDB Reviews
    Top Pick
    MongoDB is a distributed database that supports document-based applications and is designed for modern application developers. No other database is more productive. Our flexible document data model allows you to ship and iterate faster and provides a unified query interface that can be used for any purpose. No matter if it's your first customer, or 20 million users worldwide, you can meet your performance SLAs in every environment. You can easily ensure high availability, data integrity, and meet compliance standards for mission-critical workloads. A comprehensive suite of cloud database services that allows you to address a wide range of use cases, including transactional, analytical, search, and data visualizations. Secure mobile apps can be launched with native, edge to-cloud sync and automatic conflicts resolution. MongoDB can be run anywhere, from your laptop to the data center.
  • 3
    SQL Server Reviews
    Microsoft SQL Server 2019 includes intelligence and security. You get more without paying extra, as well as best-in-class performance for your on-premises requirements. You can easily migrate to the cloud without having to change any code. Azure makes it easier to gain insights and make better predictions. You can use the technology you choose, including open-source, and Microsoft's innovations to help you develop. Integrate data into your apps easily and access a rich set cognitive services to build human-like intelligence on any data scale. AI is built into the data platform, so you can get insights faster from all of your data, both on-premises or in the cloud. To build an intelligence-driven company, combine your enterprise data with the world's data. You can build your apps anywhere with a flexible platform that offers a consistent experience across platforms.
  • 4
    Oracle Database Reviews
    Oracle database products offer customers cost-optimized, high-performance versions Oracle Database, the world's most popular converged, multi-model database management software. They also include in-memory NoSQL and MySQL databases. Oracle Autonomous Database is available on-premises via Oracle Cloud@Customer and in the Oracle Cloud Infrastructure. It allows customers to simplify relational databases environments and reduce management burdens. Oracle Autonomous Database reduces the complexity of operating and protecting Oracle Database, while delivering the highest levels performance, scalability and availability to customers. Oracle Database can also be deployed on-premises if customers have network latency and data residency concerns. Customers who depend on Oracle database versions for their applications have full control over which versions they use and when they change.
  • 5
    ArangoDB Reviews
    Natively store data for graphs, documents and search needs. One query language allows for feature-rich access. You can map data directly to the database and access it using the best patterns for the job: traversals, joins search, ranking geospatial, aggregateions - you name them. Polyglot persistence without the cost. You can easily design, scale, and adapt your architectures to meet changing needs with less effort. Combine the flexibility and power of JSON with graph technology to extract next-generation features even from large datasets.
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