Real-time Analytics Database

Best practice report

Enabling the Real-Time, AI-Powered Future

Discover how CrateDB enables real-time AI solutions by unifying data, offering high performance, scalability, and flexibility. Harness the power of AI for instant decision-making.

Roundtable

Unlocking the Power of Real-Time Data and Analytics

Unlock the power of real-time data and analytics by watching this insightful roundtable discussion featuring experts from MongoDB, CrateDB, Decodable, and GridGain. Get practical strategies for implementing real-time data and analytics.

Customer Story

Driving Real-Time Industrial Insights

"Working with CrateDB brings positive outcomes. The ingestion and throughput have very good performance, with 1 million values/sec, the horizontal scalability where we can add as many nodes as we need and the automatic query distribution across the whole cluster."

Marko Sommarberg

Lead, Digital Strategy and Business Development

ABB

Webinar

Real-time monitoring for smart buildings

Learn how O-CELL real-time monitoring solution helps to reduce the environmental impact of infrastructures with CrateDB.

Customer Story

Intelligent Power Grid Management

"CrateDB is the only database that gives us the speed, scalability and ease of use to collect and aggregate measurements from hundreds of thousands of industrial sensors for real-time visibility into power, temperature, pressure, speed and torque."

Jürgen Sutterlütti

Vice President, Energy Segment and Marketing

Gantner Instruments

Webinar

How-Bitmovin improves-the-streaming-experiene-with-real-time-analytics-Cover

Real-time analytics for video streaming

Learn how Bitmovin improves the streaming experience with real-time analytics.

Real-time query performance

Experience fast in-memory SQL query performance with CrateDB's parallel query processing and distributed columnar field caches. This feature enhances real-time analytics by optimizing query execution speed, ensuring swift data retrieval for immediate analysis and decision-making.

Horizontal scalability

Seamlessly scale CrateDB to manage the continuous influx of massive data from diverse sources. CrateDB's ability to scale horizontally across multiple nodes facilitates uninterrupted operations, allowing you to expand resources effortlessly to accommodate growing data needs, ensuring consistent performance for real-time analytics.

Distributed shared-nothing architecture

Leverage CrateDB's fully distributed query engine for instant responsiveness, enabling real-time insights from billions of records. Harness CrateDB's distributed architecture to handle data-intense workloads efficiently. Process, ingest, and enrich large volumes of diverse data sources in real time.

Real-time indexing

Ensure immediate data availability for queries, enabling swift access crucial for real-time monitoring, accelerated decision-making, and enhancing operational efficiency.

Fast data ingestion and processing

Ingest, store, and process millions of data points per second thanks to distributed processing, data partitioning, multithreaded design, and shared-nothing distributed architecture with masterless clustering.

Built-in high availability

Ensure ultimate reliability and non-stop performance with automatic replication and self-healing. Instant availability of ingested data ensures immediate query access, delivering responses in milliseconds for intricate ad-hoc queries across vast datasets, ensuring real-time insights.

Columnar storage for data aggregations

Utilize CrateDB's columnar storage to compute data aggregations on demand without downsampling or pre-aggregation. This feature streamlines analytics workflows by facilitating dynamic and immediate analysis of large datasets.

Learn more

A real-time analytics database is a database system designed to process and analyze data as it is generated, providing insights and results in real-time. Some examples of databases for real-time analytics include CrateDB, SingleStore, StarTree, and InfluxDB. CrateDB excels in ingesting, indexing, storing, and querying large amounts of data within milliseconds, enabling organizations to make data-driven decisions and respond to dynamic trends quickly.

Real-time data refers to information that is gathered and processed immediately upon being generated. Examples include data from social media feeds, live customer interactions, and IoT devices. CrateDB's flexible data modeling allows for the collection and storage of a wide range of data types from diverse sources, such as enterprise applications, analytics platforms, and sensor networks.

Real-time data collection can be achieved through various methods such as streaming data ingestion, API integrations, or real-time data capture tools. CrateDB supports fast data ingestion and processing, handling millions of data points per second.

Real-time data is typically stored in databases or high-speed data stores designed to handle rapid data processing. CrateDB utilizes columnar storage to compute data aggregations on demand without downsampling or pre-aggregation, facilitating dynamic and immediate analysis of large datasets.

An example of real-time analysis could be a live dashboard displaying website visitor behavior or a system monitoring tool tracking server performance in real-time. Learn how O-CELL's real-time monitoring solution helps reduce the environmental impact with CrateDB >

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