Big data Analytical data platform Stream processing Data quality Machine learning Ops Data governance ML platform
events / second
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.

fraud detection, stream processing - Grid Dynamics

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.

Batch to Stream Migration - Grid Dynamics

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. 

Event Driven Stream Processing Architecture - Grid Dynamics

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 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.

Technology and media

Building modern IoT use cases and capturing customer interactions with web and mobile interfaces are the focal points of the technology and media industry. And high throughput, low latency, and zero data loss are paramount to these goals. Get access to our case study, which describes how we helped the #1 media company in the world design and develop a stream processing platform to expand their digital business.

Retail and brands
Personalized customer experience is the key success factor in retail. We have helped Fortune-1000 retailers and brands implement streaming use cases in personalized user experience, dynamic pricing, real time offers, and customer intelligence. We helped clients migrate from batch processing and augment their big data platforms with real- time processing, which boosted conversions and generated millions of dollars in additional revenues.
Finance and insurance
From payment processing to banking and insurance, data analytics stream processing is gaining importance in the financial services industry. We have helped financial services clients onboard event-driven architecture, integrate with applications using event sourcing and CQRS patterns, and securely set their data in motion by migrating from batch processing to streaming to increase freshness of data and achieve near real-time decision making.

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.


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. 


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.


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.

Want to get in touch with us? We are pleased to begin helping you.

Let’s talk