Stream Processing

Stream processing is a continuous flow of data from sources such as point-of-sale systems, mobile apps, e-commerce websites, GPS devices, and IoT sensors. In batch processing, by contrast, data is bundled up and processed at regular intervals.

Whether your business needs real-time latency depends on what you need to do with your data. If you’re a book retailer checking a dashboard for inventory, you’re probably fine with data that’s hours old. If you’re analyzing data from a heart monitoring implant, you might want no more than a second’s latency. If you’re doing algorithmic trading in the financial markets, you’ll want up-to-the-microsecond pricing information.

Stream processing features

• High throughput Battle tested in production workloads, handling over 1,000,000 events/second during peak.
• Low latency Millisecond latency of ingestion with seconds end-to-end latency.
• Highly scalable and robust Distributed cloud-native architecture enables up to 5 nines of availability.
• Exactly once delivery With message queues configured with at-least-once delivery and deduplication and checkpointing built into the streaming platform, we can achieve exactly once end-to-end semantics.
• Deduplication The lookup database helps avoid duplicates in each data stream.
• Zero data loss Smart checkpointing prevents data loss.
• Integrations Seamlessly integrate with microservices and transactional applications to consume or publish data.

Accelerate implementation with our stream processing blueprint

We created our blueprints based on large-scale computation implementations in public clouds and on-premise for Fortune-1000 companies. We focus on open source and cloud native software as well as cloud services to enable seamless deployment irrespective of the underlying infrastructure. We partner with AWS, Google Cloud, and Microsoft Azure cloud providers to ensure the highest efficiency and best practices.