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Transform Data to Insights

The current business imperative is not just to make decisions more data driven but to make them at a fast clip in response to changing marketplace dynamics. Traditional systems by themselves are inadequate to cost-effectively process the surging volumes of hybrid data and serve the complex analytics requirements of modern-day businesses. From interactive data processing and machine learning to visualization, the analytics ecosystem is fast evolving along with advances in the software ecosystem. Channel QBurst’s expertise in big data to shape your big technology decisions and build scalable and fault-tolerant big data solutions.

Our Service Portfolio

  • Big data strategy and roadmap
  • Deployment of Hadoop infrastructure
  • Enterprise data management services
  • Big data analytics on Hadoop infrastructure
  • IIoT applications
  • Social media sentiment analysis
  • Predictive analytics
  • Data migration to big data platforms
  • Data visualization

Batch or Stream Processing?

The answer boils down to the nature of incoming data and the expected response time. Stream processing is required if you want to provision ad hoc or interactive querying and you want those results in seconds. In instances like dynamic retail pricing or sentiment analysis, low latency is vital for business operations. If complex computations are required on large volumes of pre-existing data and the process is not interactive, batch processing is the best option. These models require different computational capabilities and technologies.

hadoop
Hadoop

With its distributed file system and MapReduce parallel computing engine, Hadoop offers a powerful big data framework for processing data on a massive scale. Fundamentally a batch processing system, Hadoop has evolved to support real-time computing with the help of tools such as Storm and Spark.

spark
Spark

What Hadoop’s MapReduce is to batch processing, Spark is now to stream processing. Spark’s in-memory stream data processing is superior to Hadoop’s MapReduce model with 100x in-memory and 10x disk performance. Spark’s processing model is ideal for real-time interactive querying, graph computation analysis, and machine learning.

Kafka

Applications that require large-scale message processing benefit from Apache Kafka, a highly scalable and durable distributed messaging system. Kafka is a viable messaging and integration platform for Spark streaming. Low latency and data partitioning capabilities make Kafka useful in IoT, multi-player gaming, and website activity tracking.

kafka

Applications that require large-scale message processing benefit from Apache Kafka, a highly scalable and durable distributed messaging system. Kafka is a viable messaging and integration platform for Spark streaming. Low latency and data partitioning capabilities make Kafka useful in IoT, multi-player gaming, and website activity tracking.

Hybrid Processing: Lambda Architecture

Hybrid Processing Lambda Architecture

Hybrid Processing: Lambda Architecture

White paper on hybrid processing in near real-time sentiment analysis

The Lambda architecture, which combines different data storage and processing techniques, can satisfy varying latency requirements of business applications. The batch layer processes data in batches using a cluster approach to provide aggregate information. The speed layer processes live-stream data. The serving layer provides views for low-latency queries on the processed data.

Facing a data processing challenge?

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