TimeBase was initially designed for 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: financial markets (MBO/ITCH), IoT (MQTT), software metrics and signals, real-time events, logging etc.
TimeBase combines multiple solutions into single package:
- Persistent message broker
- Message-oriented time-series database
- Schema-based data modeling and serialization framework
Unified streaming API for both history and live time-series data.
High performance: system may be configured to stream data with microsecond latencies or 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 disk, which allows 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 with embedded data serialization and 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 comparing with column-based databases.
Data replication framework: use multiple out-of-the box integrations or open multi-language API to create custom integrations.
Aggregation of massive volumes of heterogeneous time-series data history or real-time from multiple sources with superior latency and throughput
Reliable data storage for heterogeneous time-series data
Rapid retrieval/streaming of time-series data both history and real-time. TimeBase has a sophisticated time-series engine, capable of efficient on-the-fly merging of multiple data streams with arbitrary temporal characteristics into a unified query response.
Live data streaming provided by a simultaneous work of readers and writers
Framework for data processing and enrichment (foundation for building normalization and validation frameworks)
Statistical models and machine learning: warm-up mode (initialization with historical data), parameters estimation, online forecasting, recurring learning (on the fly adjustment with the up-to-date parameters)