Compare the Top Distributed Databases that integrate with sqlmap as of June 2025

This a list of Distributed Databases that integrate with sqlmap. Use the filters on the left to add additional filters for products that have integrations with sqlmap. View the products that work with sqlmap in the table below.

What are Distributed Databases for sqlmap?

Distributed databases store data across multiple physical locations, often across different servers or even geographical regions, allowing for high availability and scalability. Unlike traditional databases, distributed databases divide data and workloads among nodes in a network, providing faster access and load balancing. They are designed to be resilient, with redundancy and data replication ensuring that data remains accessible even if some nodes fail. Distributed databases are essential for applications that require quick access to large volumes of data across multiple locations, such as global eCommerce, finance, and social media. By decentralizing data storage, they support high-performance, fault-tolerant operations that scale with an organization’s needs. Compare and read user reviews of the best Distributed Databases for sqlmap currently available using the table below. This list is updated regularly.

  • 1
    CockroachDB

    CockroachDB

    Cockroach Labs

    CockroachDB: Cloud-native, distributed SQL. Your cloud applications deserve a cloud-native database. Cloud-based apps and services deserve a database that scales across clouds, eases operational complexity, and improves reliability. CockroachDB delivers resilient, distributed SQL with ACID transactions and data partitioned by location. Automate operations for mission-critical applications by pairing CockroachDB with orchestration tools like Kubernetes and Mesosphere DC/OS. Every node can service both reads and writes so that you can scale query throughput and database capacity by simply adding more endpoints. Just add new nodes to CockroachDB, and it automatically rebalances data, completely removing the pain of manual sharding. As demand shifts, CockroachDB detects hotspots and intelligently distributes data to maintain performance. Tune your database at the row level so that data lives close to your users and you can minimize query latency.
  • 2
    Greenplum

    Greenplum

    Greenplum Database

    Greenplum Database® is an advanced, fully featured, open source data warehouse. It provides powerful and rapid analytics on petabyte scale data volumes. Uniquely geared toward big data analytics, Greenplum Database is powered by the world’s most advanced cost-based query optimizer delivering high analytical query performance on large data volumes. Greenplum Database® project is released under the Apache 2 license. We want to thank all our current community contributors and are interested in all new potential contributions. For the Greenplum Database community no contribution is too small, we encourage all types of contributions. An open-source massively parallel data platform for analytics, machine learning and AI. Rapidly create and deploy models for complex applications in cybersecurity, predictive maintenance, risk management, fraud detection, and many other areas. Experience the fully featured, integrated, open source analytics platform.
  • 3
    TiDB

    TiDB

    PingCAP

    An open-source, cloud-native, distributed SQL database for elastic scale and real-time analytics. Supported by a wealth of open-source data migration tools in the ecosystem, TiDB gives you the freedom to choose your own vendor and avoid lock-in. Purposely built to deliver SQL at scale, TiDB eliminates the scaling problems of traditional relational databases without intrusion to your application. HTAP database platform that enables real-time situation awareness and decision making on live transactional data and eliminates friction between IT and business goals. TiDB is ACID-compliant and strongly consistent. You can use TiDB as a scale-out MySQL database with familiar SQL syntaxes and ecosystem tools. TiDB automatically shards your data so you don’t have to do it manually. You can simply add new nodes to scale horizontally and elastically to meet your business growth. TiDB simplifies the ETL process and automatically recovers from errors.
  • 4
    Apache Ignite

    Apache Ignite

    Apache Ignite

    Use Ignite as a traditional SQL database by leveraging JDBC drivers, ODBC drivers, or the native SQL APIs that are available for Java, C#, C++, Python, and other programming languages. Seamlessly join, group, aggregate, and order your distributed in-memory and on-disk data. Accelerate your existing applications by 100x using Ignite as an in-memory cache or in-memory data grid that is deployed over one or more external databases. Think of a cache that you can query with SQL, transact, and compute on. Build modern applications that support transactional and analytical workloads by using Ignite as a database that scales beyond the available memory capacity. Ignite allocates memory for your hot data and goes to disk whenever applications query cold records. Execute kilobyte-size custom code over petabytes of data. Turn your Ignite database into a distributed supercomputer for low-latency calculations, complex analytics, and machine learning.
  • 5
    Yugabyte

    Yugabyte

    Yugabyte

    The Leading High-Performance Distributed SQL Database. Open source, cloud native relational DB for powering global, internet-scale apps. Single-Digit Millisecond Latency Build blazing fast cloud applications by serving queries directly from the DB. Massive Scale. Achieve millions of transactions per second and store multiple TB’s of data per node. Geo-Distribution. Deploy across regions and clouds with synchronous or multi-master replication. Built for Cloud Native Architectures. Develop, deploy and operationalize modern applications faster than ever before with YugabyteDB. Gain Developer Agility. Leverage full power of PostgreSQL-compatible SQL and distributed ACID transactions. Operate Resilient Services. Ensure continuous availability even when underlying compute, storage or network fails. Scale On-Demand. Add and remove nodes at will. Say no to over-provisioned clusters forever. Lower User Latency.
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