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.

1,000,000
events / second
Milliseconds
latency
World #1
media company trusts us to implement it’s stream analytics

Achieve real time customer intelligence

For many media and advertising companies, high performance clickstream processing is a vital business function. From websites to mobile devices, we help capture and immediately process customer interactions. Our streaming solutions have helped Fortune-1000 companies achieve real-time business intelligence, reporting, personalization, and dynamic pricing.

Process events from IoT devices

Smart factories, connected cars, smart cities, and other IoT devices generate a lot of data. Our stream processing solution captures IoT data streams and processes them in a centralized cloud platform.

Immediately detect and prevent suspicious activity

Be it financial transaction fraud or ad fraud, streaming is necessary to detect and automatically react to suspicious activity in real-time. We combine our state of the art machine learning anomaly detection algorithms with high performance stream processing engines to prevent fraud.

Decrease time to insights

Transform your data management by turning big data into fast data. Increase data freshness and the speed of data analytics by 10x. We help you identify batch processing use cases that can be migrated to real-time streaming analytics and implement the migration with modern cloud-native or open source technology. We help modernize data sources to move from batch database exports to event-driven stream processing. 

Start your journey to streaming

For companies starting the real-time data processing journey, we help understand the common architecture and design patterns, event sourcing, CQRS, event stream processing, and complex event processing. We use the detailed blueprints we have developed to build a high performance distributed stream processing engine that can transform transactional applications to operate in the event-driven paradigm.

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.

Workshop

We offer free half-day workshops with our top experts in big data and real time analytics to discuss your stream processing strategy, challenges, optimization opportunities, and industry best practices. 

Proof-of-Concept

If you have already identified a specific use case for stream processing or real time data analytics, we can usually start with a 4–8-week proof-of-concept project to deliver tangible results for your enterprise.

Discovery

If you are at the stage of looking for analysis and strategic development, we can start with a 2–3-week discovery phase to identify the correct use cases for stream processing, design your solution, and build an implementation roadmap.