Accelerate implementation with our stream processing blueprint
- 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.
- 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.
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.
Read about our stream processing case studies
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Get started with stream processing
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.