Kafka Integration and Stream Processing
Take Apache Kafka to the Next Level
Scalable Low-Impact Real-Time Integration for Kafka
Apache Kafka has seen increasing adoption among enterprises of all sizes as a high-performance, fault-tolerant messaging system. To get the most value from your Kafka solutions you need to ingest data into Kafka, prepare it for different consumers, and distribute to a broad range of systems on-premises and in the cloud. Also, many Kafka users choose to analyze and visualize the data flowing through Kafka to gain timely intelligence.
The Striim platform enables you to integrate, process, analyze, visualize, and deliver high-volumes of streaming data for your Kafka environments with an intuitive UI and SQL-based language for easy and fast development.
- High performance, and scalable data ingestion into Kafka from enterprise sources, including databases with low-impact change data capture
- Filter, aggregate, transform, enrich data for different users and targets using SQL
- Join and correlate multiple data streams without coding
- Analyze and visualize data running in Kafka in real time
- Deliver data from Kafka to targets such as Hadoop, enterprise databases, and cloud with sub-second latency
Why Striim For Kafka
Striim completes Apache Kafka solutions by delivering high-performance real-time data integration with built-in SQL-based, in-memory stream processing, analytics, and data visualization in a single, patented platform. Using its drag-and-drop UI, pre-built wizards for Kafka integration, and SQL-based development language, you can significantly accelerate Kafka integration and stream analytics application development.
Striim provides the key pieces of in-memory technology to enable enterprise-grade Kafka solutions with end-to-end security, recoverability, reliability (including exactly once processing), and scalability. Striim also ships with Kafka built-in so you can harness its capabilities without having to rely on coding.
Continuous SQL-Based Stream Processing
Striim ingests real-time data in to Kafka from a wide variety of sources including databases, log files, IoT devices, message queues, for different data types such as JSON, XML, delimited, binary, free text, change records. For transactional databases, it uses non-intrusive change data capture (CDC). Striim runs SQL-based continuous queries to filter, transform, aggregate, enrich, and analyze the data-in-motion before delivering it to virtually any target with sub-second latency. Striim offers multi-threaded delivery to Kafka with automated partitioning, and a broad range of metrics to monitor streaming data pipelines in real time.