Best Relational Database for Apache Kafka

SingleStore, previously known as MemSQL, is a highly scalable and distributed SQL database that can operate in any environment. It is designed to provide exceptional performance for both transactional and analytical tasks while utilizing well-known relational models. This database supports continuous data ingestion, enabling operational analytics critical for frontline business activities. With the capacity to handle millions of events each second, SingleStore ensures ACID transactions and allows for the simultaneous analysis of vast amounts of data across various formats, including relational SQL, JSON, geospatial, and full-text search. It excels in data ingestion performance at scale and incorporates built-in batch loading alongside real-time data pipelines. Leveraging ANSI SQL, SingleStore offers rapid query responses for both current and historical data, facilitating ad hoc analysis through business intelligence tools. Additionally, it empowers users to execute machine learning algorithms for immediate scoring and conduct geoanalytic queries in real-time, thereby enhancing decision-making processes. Furthermore, its versatility makes it a strong choice for organizations looking to derive insights from diverse data types efficiently.

LeanXcale is a rapidly scalable database that merges the features of both SQL and NoSQL systems. It is designed to handle large volumes of both batch and real-time data pipelines, ensuring that this data is accessible through SQL or GIS for diverse applications, including operational tasks, analytics, dashboard creation, or machine learning processes. Regardless of the technology stack in use, LeanXcale offers users the flexibility of SQL and NoSQL interfaces. The KiVi storage engine functions as a relational key-value data repository, enabling data access not only via the conventional SQL API but also through a direct ACID-compliant key-value interface. This particular interface facilitates high-speed data ingestion, optimizing efficiency by eliminating the overhead associated with SQL processing. Furthermore, its highly scalable and distributed storage engine spreads data across the cluster, thereby enhancing both performance and reliability while accommodating growing data needs seamlessly.

Optimize your time and reduce expenses by simplifying data pipelines using hassle-free open source solutions. Covering everything from data ingestion to visualization, all components are seamlessly integrated, fully managed, and exceptionally reliable, ensuring your engineering team enjoys working with data. You can opt for any of DoubleCloud’s managed open source services or take advantage of the entire platform's capabilities, which include data storage, orchestration, ELT, and instantaneous visualization. We offer premier open source services such as ClickHouse, Kafka, and Airflow, deployable on platforms like Amazon Web Services or Google Cloud. Our no-code ELT tool enables real-time data synchronization between various systems, providing a fast, serverless solution that integrates effortlessly with your existing setup. With our managed open-source data visualization tools, you can easily create real-time visual representations of your data through interactive charts and dashboards. Ultimately, our platform is crafted to enhance the daily operations of engineers, making their tasks more efficient and enjoyable. This focus on convenience is what sets us apart in the industry.

Tiger Data reimagines PostgreSQL for the modern era — powering everything from IoT and fintech to AI and Web3. As the creator of TimescaleDB, it brings native time-series, event, and analytical capabilities to the world’s most trusted database engine. Through Tiger Cloud, developers gain access to a fully managed, elastic infrastructure with auto-scaling, high availability, and point-in-time recovery. The platform introduces core innovations like Forks (copy-on-write storage branches for CI/CD and testing), Memory (durable agent context and recall), and Search (hybrid BM25 and vector retrieval). Combined with hypertables, continuous aggregates, and materialized views, Tiger delivers the speed of specialized analytical systems without sacrificing SQL simplicity. Teams use Tiger Data to unify real-time and historical analytics, build AI-driven workflows, and streamline data management at scale. It integrates seamlessly with the entire PostgreSQL ecosystem, supporting APIs, CLIs, and modern development frameworks. With over 20,000 GitHub stars and a thriving developer community, Tiger Data stands as the evolution of PostgreSQL for the intelligent data age.

Apache Druid is a distributed data storage solution that is open source. Its fundamental architecture merges concepts from data warehouses, time series databases, and search technologies to deliver a high-performance analytics database capable of handling a diverse array of applications. By integrating the essential features from these three types of systems, Druid optimizes its ingestion process, storage method, querying capabilities, and overall structure. Each column is stored and compressed separately, allowing the system to access only the relevant columns for a specific query, which enhances speed for scans, rankings, and groupings. Additionally, Druid constructs inverted indexes for string data to facilitate rapid searching and filtering. It also includes pre-built connectors for various platforms such as Apache Kafka, HDFS, and AWS S3, as well as stream processors and others. The system adeptly partitions data over time, making queries based on time significantly quicker than those in conventional databases. Users can easily scale resources by simply adding or removing servers, and Druid will manage the rebalancing automatically. Furthermore, its fault-tolerant design ensures resilience by effectively navigating around any server malfunctions that may occur. This combination of features makes Druid a robust choice for organizations seeking efficient and reliable real-time data analytics solutions.

The enterprise database for time series, documents, and vectors. Store any type data and combine the simplicity and scalability NoSQL with SQL. CrateDB is a distributed database that runs queries in milliseconds regardless of the complexity, volume, and velocity.

XTDB is a unique SQL database that offers immutability to enhance application development and guarantee data compliance. Its automatic data history preservation feature allows for extensive time-travel queries, enabling users to execute as-of queries and audits seamlessly with SQL commands. This innovative tool is relied upon by a multitude of organizations to revolutionize their dynamic and temporal applications. Getting started with XTDB is straightforward, whether through HTTP, plain SQL, or various programming languages, as it only necessitates a client driver or Curl. Users can easily insert data in an immutable way, conduct time-based queries, and perform intricate joins. Furthermore, risk systems see direct advantages from its bitemporal modeling capabilities. By employing valid time, the database facilitates the correlation of out-of-sync trade data, simplifying compliance procedures. One of the significant challenges organizations face is the continuous change in data exposure; however, XTDB addresses this issue effectively by streamlining data exchange and enabling advanced temporal analysis. Additionally, modeling future changes in pricing, taxes, and discounts necessitates robust temporal query capabilities, which XTDB provides efficiently. Ultimately, XTDB stands out as a powerful solution for managing complex temporal data needs.