Best Real-Time Analytic Databases for Mid Size Business

StarTree Cloud is a fully-managed real-time analytics platform designed for OLAP at massive speed and scale for user-facing applications. Powered by Apache Pinot, StarTree Cloud provides enterprise-grade reliability and advanced capabilities such as tiered storage, scalable upserts, plus additional indexes and connectors. It integrates seamlessly with transactional databases and event streaming platforms, ingesting data at millions of events per second and indexing it for lightning-fast query responses. StarTree Cloud is available on your favorite public cloud or for private SaaS deployment. StarTree Cloud includes StarTree Data Manager, which allows you to ingest data from both real-time sources such as Amazon Kinesis, Apache Kafka, Apache Pulsar, or Redpanda, as well as batch data sources such as data warehouses like Snowflake, Delta Lake or Google BigQuery, or object stores like Amazon S3, Apache Flink, Apache Hadoop, or Apache Spark. StarTree ThirdEye is an add-on anomaly detection system running on top of StarTree Cloud that observes your business-critical metrics, alerting you and allowing you to perform root-cause analysis — all in real-time.

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.

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.

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.

Real-time analytics on raw data. Live ingest from S3, DynamoDB, DynamoDB and more. Raw data can be accessed as SQL tables. In minutes, you can create amazing data-driven apps and live dashboards. Rockset is a serverless analytics and search engine that powers real-time applications and live dashboards. You can directly work with raw data such as JSON, XML and CSV. Rockset can import data from real-time streams and data lakes, data warehouses, and databases. You can import real-time data without the need to build pipelines. Rockset syncs all new data as it arrives in your data sources, without the need to create a fixed schema. You can use familiar SQL, including filters, joins, and aggregations. Rockset automatically indexes every field in your data, making it lightning fast. Fast queries are used to power your apps, microservices and live dashboards. Scale without worrying too much about servers, shards or pagers.

Materialize is an innovative reactive database designed to provide updates to views incrementally. It empowers developers to seamlessly work with streaming data through the use of standard SQL. One of the key advantages of Materialize is its ability to connect directly to a variety of external data sources without the need for pre-processing. Users can link to real-time streaming sources such as Kafka, Postgres databases, and change data capture (CDC), as well as access historical data from files or S3. The platform enables users to execute queries, perform joins, and transform various data sources using standard SQL, presenting the outcomes as incrementally-updated Materialized views. As new data is ingested, queries remain active and are continuously refreshed, allowing developers to create data visualizations or real-time applications with ease. Moreover, constructing applications that utilize streaming data becomes a straightforward task, often requiring just a few lines of SQL code, which significantly enhances productivity. With Materialize, developers can focus on building innovative solutions rather than getting bogged down in complex data management tasks.

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.

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.

Timeplus is an efficient, user-friendly stream processing platform that is both powerful and affordable. It comes packaged as a single binary, making it easy to deploy in various environments. Designed for data teams across diverse sectors, it enables the quick and intuitive processing of both streaming and historical data. With a lightweight design that requires no external dependencies, Timeplus offers comprehensive analytic capabilities for streaming and historical data. Its cost is just a fraction—1/10—of what similar open-source frameworks charge. Users can transform real-time market and transaction data into actionable insights seamlessly. The platform supports both append-only and key-value streams, making it ideal for monitoring financial information. Additionally, Timeplus allows the creation of real-time feature pipelines effortlessly. It serves as a unified solution for managing all infrastructure logs, metrics, and traces, which are essential for maintaining observability. Timeplus also accommodates a broad array of data sources through its user-friendly web console UI, while providing options to push data via REST API or to create external streams without the need to copy data into the platform. Overall, Timeplus offers a versatile and comprehensive approach to data processing for organizations looking to enhance their operational efficiency.

Aerospike is the global leader for next-generation, real time NoSQL data solutions at any scale. Aerospike helps enterprises overcome seemingly impossible data bottlenecks and compete with other companies at a fraction of the cost and complexity of legacy NoSQL databases. Aerospike's Hybrid Memory Architecture™ is a patented technology that unlocks the full potential of modern hardware and delivers previously unimaginable value. It does this by delivering unimaginable value from huge amounts of data at both the edge, core, and in the cloud. Aerospike empowers customers with the ability to instantly combat fraud, dramatically increase shopping cart sizes, deploy global digital payment networks, and provide instant, one-to-1 personalization for millions. Aerospike customers include Airtel and Banca d'Italia as well as Snap, Verizon Media, Wayfair, PayPal, Snap, Verizon Media, and Nielsen. The company's headquarters is in Mountain View, California. Additional locations are in London, Bengaluru, India, and Tel Aviv in Israel.

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.

The Databricks Data Intelligence Platform empowers every member of your organization to leverage data and artificial intelligence effectively. Constructed on a lakehouse architecture, it establishes a cohesive and transparent foundation for all aspects of data management and governance, enhanced by a Data Intelligence Engine that recognizes the distinct characteristics of your data. Companies that excel across various sectors will be those that harness the power of data and AI. Covering everything from ETL processes to data warehousing and generative AI, Databricks facilitates the streamlining and acceleration of your data and AI objectives. By merging generative AI with the integrative advantages of a lakehouse, Databricks fuels a Data Intelligence Engine that comprehends the specific semantics of your data. This functionality enables the platform to optimize performance automatically and manage infrastructure in a manner tailored to your organization's needs. Additionally, the Data Intelligence Engine is designed to grasp the unique language of your enterprise, making the search and exploration of new data as straightforward as posing a question to a colleague, thus fostering collaboration and efficiency. Ultimately, this innovative approach transforms the way organizations interact with their data, driving better decision-making and insights.

A cloud database that can scale to handle large streaming data sets. Kinetica harnesses modern vectorized processors to perform orders of magnitude faster for real-time spatial or temporal workloads. In real-time, track and gain intelligence from billions upon billions of moving objects. Vectorization unlocks new levels in performance for analytics on spatial or time series data at large scale. You can query and ingest simultaneously to take action on real-time events. Kinetica's lockless architecture allows for distributed ingestion, which means data is always available to be accessed as soon as it arrives. Vectorized processing allows you to do more with fewer resources. More power means simpler data structures which can be stored more efficiently, which in turn allows you to spend less time engineering your data. Vectorized processing allows for incredibly fast analytics and detailed visualizations of moving objects at large scale.

Apache Druid is a powerful open-source distributed data storage solution that integrates principles from data warehousing, timeseries databases, and search technologies to deliver exceptional performance for real-time analytics across various applications. Its innovative design synthesizes essential features from these three types of systems, which is evident in its ingestion layer, storage format, query execution, and foundational architecture. By individually storing and compressing each column, Druid efficiently accesses only the necessary data for specific queries, enabling rapid scanning, sorting, and grouping operations. Additionally, Druid utilizes inverted indexes for string values to enhance search and filtering speeds. Equipped with ready-to-use connectors for platforms like Apache Kafka, HDFS, and AWS S3, Druid seamlessly integrates with existing data workflows. Its smart partitioning strategy greatly accelerates time-based queries compared to conventional databases, allowing for impressive performance. Users can easily scale their systems by adding or removing servers, with Druid automatically managing the rebalancing of data. Furthermore, its fault-tolerant design ensures that the system can effectively navigate around server failures, maintaining operational integrity. This resilience makes Druid an excellent choice for organizations seeking reliable analytics solutions.

VeloDB, which utilizes Apache Doris, represents a cutting-edge data warehouse designed for rapid analytics on large-scale real-time data. It features both push-based micro-batch and pull-based streaming data ingestion that occurs in mere seconds, alongside a storage engine capable of real-time upserts, appends, and pre-aggregations. The platform delivers exceptional performance for real-time data serving and allows for dynamic interactive ad-hoc queries. VeloDB accommodates not only structured data but also semi-structured formats, supporting both real-time analytics and batch processing capabilities. Moreover, it functions as a federated query engine, enabling seamless access to external data lakes and databases in addition to internal data. The system is designed for distribution, ensuring linear scalability. Users can deploy it on-premises or as a cloud service, allowing for adaptable resource allocation based on workload demands, whether through separation or integration of storage and compute resources. Leveraging the strengths of open-source Apache Doris, VeloDB supports the MySQL protocol and various functions, allowing for straightforward integration with a wide range of data tools, ensuring flexibility and compatibility across different environments.

Oxla is a new-generation Online Analytical Process (OLAP) Database engineered for high-speed processing and efficiency. Its all-in one architecture allows rapid deployment without external dependencies and allows users to insert data and query it seamlessly. Oxla is compatible both with the PostgreSQL SQL dialect and wire protocol, making it easy to integrate with existing tools and workflows. The platform excels at both real-time processing as well as handling large, complex query, making it ideal for diverse analytical tasks. Oxla's design is optimized for modern hardware, including multi-core architectural capabilities, delivering superior performance to traditional analytical databases. It offers flexible deployment, including self hosted and cloud-based options, and provides a 1-core license that grants access to core functionality. Oxla's pay as you go pricing model ensures cost effectiveness, allowing users only to pay for the resources that they use.

Imply is a cutting-edge analytics platform that leverages Apache Druid to manage extensive, high-performance OLAP (Online Analytical Processing) tasks in real-time. It excels at ingesting data instantly, delivering rapid query results, and enabling intricate analytical inquiries across vast datasets while maintaining low latency. This platform is specifically designed for enterprises that require engaging analytics, real-time dashboards, and data-centric decision-making on a large scale. Users benefit from an intuitive interface for exploring data, enhanced by features like multi-tenancy, detailed access controls, and operational insights. Its distributed architecture and ability to scale make Imply particularly advantageous for applications in streaming data analysis, business intelligence, and real-time monitoring across various sectors. Furthermore, its capabilities ensure that organizations can efficiently adapt to increasing data demands and quickly derive actionable insights from their data.

In-Memory Computing Platform. Digital world is different. Microseconds are important. The world's most important organizations rely on us for powering their most sensitive applications at scale. If they meet the current requirement for immediate access, new data-enabled apps can transform your business. Hazelcast solutions can be used to complement any database and deliver results that are much faster than traditional systems of record. Hazelcast's distributed architecture ensures redundancy and continuous cluster up-time, as well as always available data to support the most demanding applications. The capacity grows with demand without compromising performance and availability. The cloud delivers the fastest in-memory data grid and third-generation high speed event processing.

Pinot is engineered to efficiently handle OLAP queries with minimal latency on static datasets. It incorporates various pluggable indexing methods, including Sorted Index, Bitmap Index, and Inverted Index. While it currently does not support joins, this limitation can be addressed by utilizing Trino or PrestoDB for query execution. The system features an SQL-like language that accommodates selection, aggregation, filtering, grouping, ordering, and distinct queries on the dataset. It consists of both offline and real-time tables, with real-time tables utilized specifically to address segments lacking available offline data. Additionally, users can tailor the anomaly detection process and notification system to accurately identify relevant anomalies. This flexibility ensures that users can maintain high data integrity while effectively managing their 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.