Use cases

Streaming & Real-Time Analytics

CrateDB ingests and analyzes continuously arriving data as it is generated. It enables sub-second queries on fresh events and time-series data, allowing teams to build dashboards, alerts, and applications that reflect what is happening right now, not minutes or hours later.

Modern systems produce constant streams of data from connected devices, sensors, machines, vehicles, applications, and infrastructure logs. Turning this flow of events into timely insight requires more than storage at scale. It requires the ability to ingest data continuously and query it immediately, without batch windows or pre-computed aggregates.

Streaming and real-time analytics enable teams to monitor live systems, detect anomalies as they occur, and act on current conditions with confidence.

Continuous Ingestion at Scale

CrateDB ingests high-velocity data streams from sources such as Kafka, Flink, MQTT, and CDC pipelines. Data is distributed across the cluster as it arrives, enabling sustained ingestion rates without bottlenecks or manual tuning.

Query Fresh Data Instantly

New events become queryable within milliseconds. Teams can run analytical queries on live data streams without waiting for batch jobs, pre-aggregation, or indexing delays, making fresh data immediately usable.

Real-Time Monitoring and Alerts

CrateDB powers live dashboards and alerting systems that reflect the current state of operations. This enables immediate detection of anomalies and rapid response to changes in system behavior or business conditions.

Built for Always-On Workloads

With horizontal scalability and built-in resilience, CrateDB supports continuous ingestion and querying without downtime. Systems remain responsive and reliable even as data volumes and event rates grow.

Where Traditional Systems Fall Short

Traditional databases and data warehouses struggle with continuous streams of events:

Time lag: Data must often be pre-aggregated or batch-processed, delaying insights.
Scale limits: Billions of events overwhelm OLTP databases like Postgres or MongoDB.
High cost: Warehouses become expensive at high ingestion rates and are not designed for low-latency queries on fresh data.

As a result, organizations miss anomalies, react too late to operational issues, and struggle to power real-time dashboards or downstream AI systems effectively.

Learn more about CrateDB

Webinar

From Sensors to Dashboards: Building Real-Time Analytics Pipelines That Actually Work

The promise of real-time data is everywhere, but for most engineering teams, the reality is a nightmare of stale data, system bottlenecks, and lagging dashboards that are useless for operational decisions. If your team is spending more time tuning infrastructure and reprocessing data than delivering new value, you are not alone. Watch this recording to learn the three principles for building real-time analytics that work.

TALK

Inside Bitmovin's Data Pipeline: Delivering Streaming Insights with CrateDB

Watch Bitmovin’s journey from a startup to a global leader in video analytics, handling real-time data streams for some of the world’s biggest live sporting events. Learn how CrateDB enabled Bitmovin to scale seamlessly, ensuring fast, reliable, and actionable insights while managing massive data volumes with confidence.

Talk

Smart Transport: How IoT Platforms Contribute for Real-Time E-Scooters Fleet Management

This talk given at the AI & Big Data Expo Amsterdam 2024 looks into specific problems faced in the management of e-scooter ride-sharing systems in major cities and demonstrates how through its IoT platform, CrateDB effectively tackles these challenges.

Talk

How-ABB-Ability-Genix-applies-AI-and-analytics-to-unlock-the-value-of-industrial-data-with-CrateDB-02

ABB: AI and Analytics applied to Industrial Data

In this talk, Marko Sommarberg, Lead Digital Strategy and Business Development at ABB, explaine how ABB Ability™ Genix applies AI and analytics to unlock the value of industrial data using CrateDB.

Talk

Not-All-Time-Series-Are-Equal_Challenges-of-Storing-and-Analyzing-Industrial-Data

TGW Logistics: Not All Time-Series are Equal

This talk at the IoT Tech Expo 2023 explores the complexities of industrial big data, characterized by its high variety, unstructured features, and different data frequencies. It also analyzes how these attributes influence data storage, retention, and integration when dealing with an IoT database.

Tutorial

Setup HiveMQ using CrateDB as consumer

This blog post gives you an overview of how to set up HiveMQ using CrateDB as a consumer.

White Paper

TGW Logistics redefines warehouse intelligence using CrateDB

TGW simplifies aggregating massive volumes of diverse data with CrateDB, gaining valuable insights to improve customer experience and competitive advantage

Video

Unstoppable Insights: Resilient Data Streaming with CrateDB

Explore the resilience of CrateDB for real-time data streaming in a demonstration of node failure scenarios. This video illustrates the capabilities of a distributed CrateDB cluster to maintain operational continuity during both a graceful shutdown and an abrupt unavailability of a database node within a 3-node configuration. Witness firsthand how continuous data ingestion is sustained, underscoring CrateDB's inherent high availability.

Webinar

From Data Overload to Actionable Insights: Mastering Data Management in Manufacturing

This session delves into the challenges of managing vast amounts of data and demonstrates how real-time analytics can transform raw data into valuable insights. Learn how modern data management solutions enable manufacturers to optimize production, improve operational efficiency, and drive innovation. From harnessing data streams in real-time to leveraging AI-powered analytics, this session will equip you with the tools to master data management and make faster, data-driven decisions.

Sensor data queries with SQL

Hyper-fast. Results in milliseconds.

        
/* Based on IoT devices reports, this query returns the voltage variation over time

   for a given meter_id */
WITH avg_voltage_all AS (
    SELECT meter_id,
           avg("Voltage") AS avg_voltage,
           date_bin('1 hour'::INTERVAL, ts, 0) AS time
    FROM iot.power_consumption
    WHERE meter_id = '840072572S'
    GROUP BY 1, 3
    ORDER BY 3
)
SELECT time,
       (avg_voltage - lag(avg_voltage) over (PARTITION BY meter_id ORDER BY time)) AS var_voltage
FROM avg_voltage_all
LIMIT 10;

        
+---------------+-----------------------+
|          time |           var_voltage |
+---------------+-----------------------+
| 1166338800000 | NULL                  |
| 1166479200000 |   -2.30999755859375   |
| 1166529600000 |    4.17999267578125   |
| 1166576400000 |   -0.3699951171875    |
| 1166734800000 |   -3.7100067138671875 |
| 1166785200000 |   -1.5399932861328125 |
| 1166893200000 |   -3.839996337890625  |
| 1166997600000 |    9.25               |
| 1167044400000 |    0.4499969482421875 |
| 1167174000000 |    3.220001220703125  |
+---------------+-----------------------+

        
/* Based on IoT devices reports, this query returns the voltage corresponding to

   the maximum global active power for each meter_id */
SELECT meter_id,
       max_by("Voltage", "Global_active_power") AS voltage_max_global_power
FROM iot.power_consumption
GROUP BY 1
ORDER BY 2 DESC
LIMIT 10;

        
+------------+--------------------------+
| meter_id   | voltage_max_global_power |
+------------+--------------------------+
| 840070437W |                   246.77 |
| 840073628P |                   246.69 |
| 840074265G |                   246.54 |
| 840070238E |                   246.35 |
| 840070335K |                   246.34 |
| 840075190M |                   245.15 |
| 840072876X |                   244.81 |
| 840070636M |                   242.98 |
| 84007B113A |                   242.93 |
| 840073250D |                   242.28 |
+------------+--------------------------+

Log analysis, Media

Bitmovin is a leading video streaming company. They use CrateDB to store 140 terabytes of storage, both user events and user interactions. Every day, there is one billion of new lines of data, with the largest tables containing around 60 billion playback events.

"It is through the use of CrateDB that we are able to offer our large-scale video analytics component in the first place. Comparable products are either not capable of handling the large flood of data or they are simply too expensive."

Daniel Hölbling-Inzko
Senior Director of Engineering - Analytics
Bitmovin

IoT, Energy

ABB's OPTIMAX® Cloud for Smart Charging is a state-of-the-art power management system designed for EV charging stations and heavy vehicle depots in the logistics and bus industries. It provides smart load management for ABB and non ABB EV chargers, and integrates with external assets like battery storages, PV systems, and interfaces to grid operators’ systems.

"CrateDB is a critical piece of our OPTIMAX® Cloud platform. Its ability to handle vast amounts of time-series data from diverse sources, while delivering real-time insights, has allowed us to scale our operations seamlessly. With CrateDB, we’ve empowered our customers with smarter energy management, reduced costs, and supported a more sustainable future."

Christian Kohlmeyer
Product Owner Mobility & Sites
ABB

IoT, Digital twins, Logistics

TGW Logistics Group is one of the leading international suppliers of material handling solutions. As systems integrator, TGW plans, produces and implements complex logistics centres, from mechatronic products and robots to control systems and software.

Using CrateDB, TGW accelerates data aggregation and access from warehouse systems worldwide, resulting in increased database performance. The system can handle over 100,000 messages every few seconds.

"CrateDB is a highly scalable database for time series and event data with a very fast query engine using standard SQL".

Alexander Mann
Owner Connected Warehouse Architecture
TGW Logistics Group

IoT, Mining & Oil

SPGo! is part of PETROMIN, which has more than 23 years of experience in the mining and oil industries. They build applications for monitoring all material conveyor belt idlers every minute 24 hours a day through online sensors. They use CrateDB as a central database to capture and query data from 30,000 sensors per mine, representing 760 million records a day.

"With CrateDB, we can continue designing products that add value to our customers. We will continue to rely on CrateDB when we need a database that offers great scalability, reliability and speed."

Nixon Monge Calle
Head of IT Development and Projects
SPGo! Business Intelligence

AI/ML, Smart cities

Gantner Instruments collaborates with the University of Cyprus to operate a state-of-the-art Smart Micro Grid, dedicated to investigating the control capabilities of renewable energy sources in the power grid and propelling the energy transition forward. They leverage CrateDB to analyze the vast amount of data generated in real time, enhancing their processes through machine learning (ML). With CrateDB, they gain access to their extensive data within microseconds at the frontend, ensuring optimal performance.

"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 Sutterlüti
Vice President, Energy Segment and Marketing at Gantner Instruments.

Streaming analytics refers to real-time processing and analysis of data as it flows in (e.g. from sensors, logs, events). It allows organizations to detect anomalies, trigger actions, and make decisions immediately, rather than relying on batch processing that introduces latency.

CrateDB is engineered for scalable ingestion from sources such as Kafka, MQTT, CDC streams, and logs. It features automatic indexing and distributed architecture that let it scale horizontally and sustain large throughput while serving sub-second queries.

Yes, CrateDB supports native SQL. You can query streaming and time-series data using familiar SQL semantics (aggregations, windowing, joins) without needing to learn a proprietary query language.

CrateDB supports flexible schemas, allowing you to ingest semi-structured data (e.g. JSON fields) and evolve your schema over time. This makes it easier to adapt as new sources and fields emerge.

It’s particularly effective in scenarios such as IoT (sensor monitoring), log and event analytics, anomaly detection, fleet/transport monitoring, real-time dashboards, AI/ML feature pipelines, and any system requiring near-instant insight from high-volume continuous data.

Streaming & Real-Time Analytics

Continuous Ingestion at Scale

Query Fresh Data Instantly

Real-Time Monitoring and Alerts

Built for Always-On Workloads

Where Traditional Systems Fall Short

Is CrateDB the Right Database for Your Workloads?

Sensor data queries with SQL

Want to know more?

User stories

Additional resources

Blog

Articles about streaming analytics

Blog

The IoT Database Built for Real Time Analytics at Scale

Blog

Which Database for Digital Twin Projects?

Blog

Articles about IoT

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