- Real-time Data Processing: Streaming databases are capable of processing and analyzing data as it is generated, allowing organizations to make immediate decisions based on real-time insights. This is crucial in applications like financial trading, monitoring IoT devices, or analyzing social media trends.
- Event-Driven Architecture: Data in streaming databases is often organized as a series of events or messages. These events can be anything from sensor readings, log entries, user interactions, or any other type of data change. The database can react to these events and trigger actions or notifications in response.
- Low Latency: Streaming databases are designed for low-latency data processing, ensuring that data is processed and made available for analysis or action as quickly as possible. This is essential for applications where delay can have a significant impact, such as fraud detection or real-time analytics.
- Integration with Streaming Platforms: Streaming databases are often used in conjunction with streaming data platforms, such as Apache Kafka or Apache Pulsar, to efficiently ingest and distribute data streams. These platforms help manage the flow of data to and from the database.
- Complex Event Processing (CEP): Many streaming databases include capabilities for complex event processing, which allows users to define custom rules and queries to identify patterns, correlations, or anomalies in the streaming data.
- Durability and Fault Tolerance: Streaming databases often provide mechanisms for ensuring data durability, replication, and fault tolerance to prevent data loss and ensure high availability.