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Grid Dynamics recognized as Google Cloud leader by Everest Group

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.

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

INTERNET OF THINGS

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

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

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 ARCHITECTURE

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.

Our clients

Retail
Hi-tech
Manufacturing
Finance & Insurance
Healthcare

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. 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. 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.

Industries

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.

hi-tech icon
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. We have helped #1 media company in the world design and developed a stream processing platform to expand their digital business.

retail and brands icon
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 icon
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.

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.
Read more

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.

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