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

Finance & Insurance
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 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. Redis is the default choice. However, Apache Ignite or Hazelcast can also be good alternatives.
  • Operational storage. Apache 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.

Read about our stream processing case studies

Delivering actionable insights in real-time by moving from batch to stream processing
This stream processing guide presents a case study of increasing speed to insights by 10x by migrating batch processing to streaming using AWS cloud native data analytics services.
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How to achieve in-stream data deduplication for real-time bidding
Event duplication is a hard problem when trying to achieve exactly once delivery semantics, while using at least once semantics in message queues with checkpointing. In this stream processing guide we present a case study on how we solved the data duplication problem.
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How to create a serverless real-time analytics platform
Serverless architecture is a modern approach to building lightweight streaming applications. This stream processing guide presents a case study on how we implemented a real-time streaming platform for a mobile gaming company.
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