Time Series Database for Real-Time Analytics

A time series database is optimized for workloads where each record represents a measurement or event associated with a timestamp. These records are often enriched with dimensions such as device ID, location, customer, or system state.

Unlike general-purpose databases, time series databases are designed for scenarios where:

• Data is continuously appended

• Write throughput is sustained and high

• Queries filter and aggregate over time ranges

• Large volumes of recent and historical data must be analyzed together

These characteristics distinguish time series data from other data types. A deeper explanation of time-based datasets and their structure is available in the overview of time series data.

Efficient storage and retrieval of this data also require specialized approaches. Our article on how time series data is stored explains common patterns for managing ingestion, indexing, and retention at scale.

Time series databases also manage data lifecycles, enabling recent data to remain readily accessible while retaining historical data efficiently for long-term analysis, reporting, or compliance.

Time series databases are used across industries wherever data is generated continuously and must be analyzed over time.

Common use cases include:

• IoT and device telemetry, where connected devices continuously emit sensor measurements and status updates.

• Industrial and manufacturing monitoring, where operational data must be analyzed in real time to detect anomalies and optimize processes.

• Observability and application metrics, including system performance, logs, and infrastructure monitoring

• Financial and market time series, such as price movements, trades, and risk indicators

• Energy, utilities, and infrastructure monitoring, where time-based measurements are used for forecasting and optimization

Many IoT-focused workloads combine large device fleets, high-cardinality identifiers, and evolving data schemas. Examples of these patterns are explored in more detail in our overview of IoT time series data.

Industrial environments often introduce additional requirements around reliability, scale, and integration with existing systems. Real-world IIoT scenarios and architectures are covered in our guide on industrial IoT time series data.

Traditional relational databases were designed for transactional workloads, not for continuous streams of measurements. As time series data scales, several limitations appear:

• High write rates overwhelm single-node systems

• High-cardinality dimensions cause index growth and performance degradation

• Time-based queries slow down as data volume increases

• Schema evolution becomes difficult as data structures change

• Pre-aggregation is required to maintain query performance, reducing flexibility

These challenges explain why time series workloads often exceed the limits of general-purpose databases. A deeper comparison of architectural trade-offs is covered in our article on time series databases vs relational databases.

In industrial environments, similar limitations appear with legacy systems designed for operational logging rather than real-time analytics. This is why many organizations transition from data historians to time series databases as data velocity and analytical requirements increase.

As data volumes and velocity grow, teams increasingly adopt a dedicated time series database to support scalable ingestion, flexible querying, and real-time analytics.

A distributed time series database architecture is essential as data volumes and ingestion rates grow. Instead of relying on a single node, data and queries are spread across multiple nodes, allowing the system to scale horizontally and avoid centralized bottlenecks.

In a distributed time series database, core architectural principles include:

• Horizontal data distribution, so ingestion and storage scale with additional nodes

• Parallel query execution, enabling faster analytics across large datasets

• Automatic replication, improving fault tolerance and availability

• Real-time indexing, allowing newly ingested data to be queried immediately

These design choices are especially important for high-volume workloads such as IoT telemetry and operational monitoring.

At a deeper level, time series systems must optimize how data is physically stored and accessed. The fundamentals of time series data storage explain how partitioning, sharding, and replication impact scalability and performance.

Efficient query performance also depends on how data is indexed and accessed over time. Common approaches are covered in the overview of time series indexing techniques.

Finally, continuous data streams place unique demands on ingestion pipelines. The mechanics of handling sustained write throughput are explored in the guide to time series data ingestion.

Together, these architectural elements allow a distributed time series database to scale from millions to billions of records while maintaining predictable ingestion and query performance.

Storing time series data is only the first step. The real value comes from analyzing it as it arrives.

CrateDB enables real-time analytics on time series data using standard SQL, including:

• Time-based filtering and grouping

• Aggregations over fixed and sliding time windows

• Joins between time series data and metadata

• Analysis across recent and historical data in a single query

These capabilities make it possible to run time series analytics directly on operational data, without exporting data to a separate analytics system or relying on batch pipelines.

Using SQL for time series analytics provides flexibility and accessibility. Common analytical patterns, functions, and query structures are explained in the overview of time series analytics with SQL.

As data volumes grow, query efficiency becomes critical. Practical guidance on improving performance is covered in the guide to optimizing time series queries.

For a more hands-on perspective, our time series analysis checklist outlines common analytical tasks and considerations when working with real-time and historical time series data.

Together, these techniques enable fast, flexible analytics on continuously ingested data, supporting dashboards, monitoring systems, and data-driven applications.

IoT and industrial systems represent some of the most demanding time series workloads. Millions of devices generate telemetry with unpredictable schemas, high cardinality, and strict latency requirements.

A time series database for IoT must support:

• Continuous ingestion from devices and gateways

• Rapid schema evolution as sensors and firmware change

• High-cardinality identifiers such as device IDs and asset tags

• Real-time analytics across fleets and systems

• Long-term storage for historical analysis

CrateDB is widely used for IoT and IIoT time series data, supporting use cases such as smart manufacturing, energy monitoring, fleet management, and infrastructure analytics. It also replaces or complements traditional data historians by enabling real-time analytics on fresh operational data.

Time series databases are available as managed cloud services, self-managed deployments, and open source platforms.

Cloud time series databases are typically offered as fully managed services. They reduce operational overhead by handling infrastructure, scaling, and maintenance, but they can introduce constraints around query flexibility, cost predictability, and data portability. A detailed overview of these trade-offs is covered in our guide to cloud time series databases.

Open source time series databases provide transparency, flexibility, and freedom from vendor lock-in. They allow teams to deploy and operate the database in environments that meet their compliance, performance, and integration requirements. However, operating an open source system at scale requires careful consideration of architecture and operations. These aspects are explored in more detail in our article on open source time series databases.

CrateDB is an open source time series database available both as a self-managed deployment and as a fully managed cloud service. This allows teams to choose the model that best fits their needs while maintaining a consistent data model and SQL query interface.

As time series data grows, performance becomes critical. Query latency, ingestion throughput, and resource efficiency directly impact system reliability and cost.

CrateDB is designed to sustain high ingestion rates while delivering fast analytical queries on large, high-cardinality datasets.

An external benchmark evaluating ingestion speed and analytical query performance is documented in the independent time series database benchmark.

These results demonstrate that a distributed time series database can sustain real-time ingestion while executing complex analytical queries on large datasets, making it suitable for demanding, always-on analytics workloads.

When evaluating the best time series database for real-time analytics, scalability, query flexibility, and operational simplicity become critical factors.

CrateDB combines distributed scalability with SQL simplicity, making it a strong foundation for time series analytics.

Key advantages include:

• Distributed SQL for scalable ingestion and analytics

• Real-time indexing with no manual tuning

• Support for structured and semi-structured data

• High-cardinality performance without pre-aggregation

• One platform for ingestion, analytics, and AI workloads

• Open source with cloud and self-managed options

These capabilities build on CrateDB’s underlying time series data model, which is designed to handle continuously evolving, high-volume datasets at scale.

Instead of stitching together multiple systems, teams can rely on CrateDB as a unified time series database that scales with their data and use cases. For readers looking to compare different approaches and solutions, we established a broader comparison of time series databases.

Whether you are building an IoT platform, modernizing operational analytics, or replacing legacy monitoring systems, CrateDB provides a scalable foundation for time series data.

Explore how CrateDB models, ingests, and analyzes time series data in depth, or get started with a managed cloud cluster to experience real-time analytics in action.