Compare the Top Columnar Databases for Cloud as of December 2025

What are Columnar Databases for Cloud?

Columnar databases, also known as column-oriented databases or column-store databases, are a type of database that store data in columns instead of rows. Columnar databases have some advantages over traditional row databases including speed and efficiency. Compare and read user reviews of the best Columnar Databases for Cloud currently available using the table below. This list is updated regularly.

  • 1
    Sadas Engine
    Sadas Engine is the fastest Columnar Database Management System both in Cloud and On Premise. Turn Data into Information with the fastest columnar Database Management System able to perform 100 times faster than transactional DBMSs and able to carry out searches on huge quantities of data over a period even longer than 10 years. Every day we work to ensure impeccable service and appropriate solutions to enhance the activities of your specific business. SADAS srl, a company of the AS Group , is dedicated to the development of Business Intelligence solutions, data analysis applications and DWH tools, relying on cutting-edge technology. The company operates in many sectors: banking, insurance, leasing, commercial, media and telecommunications, and in the public sector. Innovative software solutions for daily management needs and decision-making processes, in any sector
  • 2
    OpenText Analytics Database (Vertica)
    OpenText Analytics Database is a high-performance, scalable analytics platform that enables organizations to analyze massive data sets quickly and cost-effectively. It supports real-time analytics and in-database machine learning to deliver actionable business insights. The platform can be deployed flexibly across hybrid, multi-cloud, and on-premises environments to optimize infrastructure and reduce total cost of ownership. Its massively parallel processing (MPP) architecture handles complex queries efficiently, regardless of data size. OpenText Analytics Database also features compatibility with data lakehouse architectures, supporting formats like Parquet and ORC. With built-in machine learning and broad language support, it empowers users from SQL experts to Python developers to derive predictive insights.
  • 3
    CrateDB

    CrateDB

    CrateDB

    The enterprise database for time series, documents, and vectors. Store any type of data and combine the simplicity of SQL with the scalability of NoSQL. CrateDB is an open source distributed database running queries in milliseconds, whatever the complexity, volume and velocity of data.
  • 4
    Google Cloud Bigtable
    Google Cloud Bigtable is a fully managed, scalable NoSQL database service for large analytical and operational workloads. Fast and performant: Use Cloud Bigtable as the storage engine that grows with you from your first gigabyte to petabyte-scale for low-latency applications as well as high-throughput data processing and analytics. Seamless scaling and replication: Start with a single node per cluster, and seamlessly scale to hundreds of nodes dynamically supporting peak demand. Replication also adds high availability and workload isolation for live serving apps. Simple and integrated: Fully managed service that integrates easily with big data tools like Hadoop, Dataflow, and Dataproc. Plus, support for the open source HBase API standard makes it easy for development teams to get started.
  • 5
    kdb+

    kdb+

    KX Systems

    A high-performance cross-platform historical time-series columnar database featuring: - An in-memory compute engine - A real-time streaming processor - An expressive query and programming language called q kdb+ powers kdb Insights portfolio and KDB.AI, together delivering time-oriented data insights and generative AI capabilities to the world’s leading enterprise organizations. Independently benchmarked* as the fastest in-memory, columnar analytics database available, kdb+ delivers unmatched value to businesses operating in the toughest data environments. kdb+ improves decision-making processes to help navigate rapidly changing data landscapes​.
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