Stream processing

Achieve real time insights with stream analytics. Increase decision making speed by migrating from batch processing to streaming for customer intelligence, IoT, and fraud detection use cases. Transform big data into fast data with our stream processing solutions.

Our clients

Retail
Hi-tech
Manufacturing
Finance
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. Stream Processing Blueprint - Grid Dynamics
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.
Technology stack
  • Message queue. Apache Kafka with Lenses.io is the default choice. In case of cloud deployment, services such as Amazon Kinesis, Google Pub/Sub, or Microsoft Events Hub can be used. In some use cases, Apache NiFi may be preferred.
  • Stream processing engine. A choice of Apache Spark, Apache Flink, or Apache Beam are the primary choices. In some use cases, Apache NiFi may be preferred.
  • Lookup database. Apache Redis is the default choice. However, Apache Ignite or Hazelcast can also be good alternatives.
  • Operational storage. Cassandra is the default choice. In case of cloud deployment, cloud NoSQL databases such as Azure CosmosDB or Amazon DynamoDB can also be used.
  • Data lake and EDW. The stream processing engine supports integrations with modern data lakes and EDWs to store the processed data for later reporting.

We develop stream processing platforms for technology startups and Fortune-1000 enterprises across a range of industries including media, retail, brands, payment processing, and finance.

We provide flexible engagement options to design and build stream processing use cases, decrease time from data to insights, and augment your big data with real time analytics. Contact us today to start with a workshop, discovery, or PoC.