Best Real-Time Analytic Databases for Linux of 2025

RaimaDB, an embedded time series database that can be used for Edge and IoT devices, can run in-memory. It is a lightweight, secure, and extremely powerful RDBMS. It has been field tested by more than 20 000 developers around the world and has been deployed in excess of 25 000 000 times. RaimaDB is a high-performance, cross-platform embedded database optimized for mission-critical applications in industries such as IoT and edge computing. Its lightweight design makes it ideal for resource-constrained environments, supporting both in-memory and persistent storage options. RaimaDB offers flexible data modeling, including traditional relational models and direct relationships through network model sets. With ACID-compliant transactions and advanced indexing methods like B+Tree, Hash Table, R-Tree, and AVL-Tree, it ensures data reliability and efficiency. Built for real-time processing, it incorporates multi-version concurrency control (MVCC) and snapshot isolation, making it a robust solution for applications demanding speed and reliability.

InfluxDB is a purpose-built data platform designed to handle all time series data, from users, sensors, applications and infrastructure — seamlessly collecting, storing, visualizing, and turning insight into action. With a library of more than 250 open source Telegraf plugins, importing and monitoring data from any system is easy. InfluxDB empowers developers to build transformative IoT, monitoring and analytics services and applications. InfluxDB’s flexible architecture fits any implementation — whether in the cloud, at the edge or on-premises — and its versatility, accessibility and supporting tools (client libraries, APIs, etc.) make it easy for developers at any level to quickly build applications and services with time series data. Optimized for developer efficiency and productivity, the InfluxDB platform gives builders time to focus on the features and functionalities that give their internal projects value and their applications a competitive edge. To get started, InfluxData offers free training through InfluxDB University.

Regardless of whether your project involves a single table or numerous tables, StarRocks guarantees an impressive performance improvement of at least 300% when compared to other widely used solutions. With its comprehensive array of connectors, you can seamlessly ingest streaming data and capture information in real time, ensuring that you always have access to the latest insights. The query engine is tailored to suit your specific use cases, allowing for adaptable analytics without the need to relocate data or modify SQL queries. This provides an effortless way to scale your analytics capabilities as required. StarRocks not only facilitates a swift transition from data to actionable insights, but also stands out with its unmatched performance, offering a holistic OLAP solution that addresses the most prevalent data analytics requirements. Its advanced memory-and-disk-based caching framework is purpose-built to reduce I/O overhead associated with retrieving data from external storage, significantly enhancing query performance while maintaining efficiency. This unique combination of features ensures that users can maximize their data's potential without unnecessary delays.

Apache Doris serves as an advanced data warehouse tailored for real-time analytics, providing exceptionally rapid insights into large-scale real-time data. It features both push-based micro-batch and pull-based streaming data ingestion, achieving this within a second, along with a storage engine capable of real-time updates, appends, and pre-aggregations. The platform is optimized for handling high-concurrency and high-throughput queries thanks to its columnar storage engine, MPP architecture, cost-based query optimizer, and vectorized execution engine. Moreover, it supports federated querying across various data lakes like Hive, Iceberg, and Hudi, as well as traditional databases such as MySQL and PostgreSQL. Doris also accommodates complex data types, including Array, Map, and JSON, and features a variant data type that allows for automatic inference of JSON data types. Additionally, it employs advanced indexing techniques like NGram bloomfilter and inverted index to enhance text search capabilities. With its distributed architecture, Doris enables linear scalability, incorporates workload isolation, and implements tiered storage to optimize resource management effectively. Furthermore, it is designed to support both shared-nothing clusters and the separation of storage and compute resources, making it a versatile solution for diverse analytical needs.

Scale from zero to millions of events every second with Arroyo, which is delivered as a single, streamlined binary. It can be run locally on either MacOS or Linux for development purposes and easily deployed to production using Docker or Kubernetes. Arroyo represents a revolutionary approach to stream processing, specifically designed to simplify real-time operations compared to traditional batch processing. From its inception, Arroyo has been crafted so that anyone familiar with SQL can create dependable, efficient, and accurate streaming pipelines. This empowers data scientists and engineers to develop comprehensive real-time applications, models, and dashboards without needing a dedicated team of streaming specialists. Users can perform transformations, filtering, aggregation, and joining of data streams simply by writing SQL, achieving results in under a second. Furthermore, your streaming pipelines shouldn’t trigger alerts just because Kubernetes opted to reschedule your pods. With the capability to operate in contemporary, elastic cloud environments, Arroyo is suitable for everything from basic container runtimes like Fargate to extensive, distributed systems managed by Kubernetes. This versatility makes Arroyo an ideal choice for organizations looking to optimize their streaming data processes.