Key features include:
- Embedded data modeling and serialization functionality
- Streaming system
- Messaging middleware
TimeBase was initially designed for the very fast aggregation and retrieval of massive volumes of high-frequency financial market data. The same TimeBase technology excels at processing any time-series data, such as financial markets (MBO/ITCH), IoT (MQTT), software metrics and signals, real-time events, logging, and so on.
TimeBase runs standalone or in a cluster, processes millions of messages per second, stores terabytes of data, and offers microsecond latencies.
TimeBase combines multiple solutions into a single package, including:
- Persistent message broker
- Message-oriented time-series database
- Schema-based data modeling and serialization framework
The following features set TimeBase apart from other time series databases:
- Unified streaming API: Available for both history and live time-series data.
- High performance: Configure the system to stream data with microsecond-long latencies or to read/write millions of messages per second on each data producer and consumer.
- Low latency: When streaming live data, TimeBase can feed real-time consumers from memory rather than from the disk, allowing for a significant latency reduction.
- Complex message structure: TimeBase can store complex message structures that reflect data in your business domain (no need for intermediate DTO objects).
- Schema-based database: Contains embedded data serialization and a modeling framework allowing for better visibility and data migration. Smooth transition from rapid data prototyping to production solution.
- Row-based design: Offers better latency and throughput for streaming use cases compared to column-based databases.
To learn more about TimeBase's advantages, please visit the Key Features page.
Typical use cases for TimeBase include:
- Using the data replication framework with multiple out-of-the box integrations or the open multi-language API to create custom integrations.
- Aggregating massive volumes of heterogeneous time-series data history or real-time data from multiple sources with superior latency and throughput.
- storing data reliably for heterogeneous time-series data.
- Rapidly retrieving and streaming of historical and real-time time series data. TimeBase has a sophisticated time series engine, capable of efficiently merging multiple data streams with arbitrary temporal characteristics into a unified query response on-the-fly.
- Streaming live data provided by readers and writers working simultaneously.
- Providing a framework for processing and enrichming data, serving as a foundation for building normalization and validation frameworks.
- Providing statistical models and machine learning with features including:
- Warm-up mode: Initialization with historical data
- Parameters estimation
- Online forecasting
- Recurring learning: On-the-fly adjustment with up-to-date parameters
To learn more about TimeBase's use cases, visit the Why TimeBase page.