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Data Processing

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.

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.

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.

Hadoop

Spark

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.

In the world of big data processing, Apache Flink is in a league of its own. While adept at both batch and stream processing, its more distinguishing qualities, such as exactly-once guarantees and event time processing make it ideal for fault-tolerant and highly scalable streaming applications. It furnishes accurate results regardless of interruptions to data streams and the delayed/disorderly arrival of data. It achieves consistency in large-scale computation with negligible tradeoff between reliability and latency, spending minimal resources.

Derived from the concepts of flow-based programming, NiFi automates data flow management and helps address challenges that typically arise in the context of processing data from multiple enterprise systems. Its user-friendly graphical interface makes it easy to create, monitor, and control data flows. It can be configured to achieve different needs, such as loss tolerance versus guaranteed delivery, low latency versus high throughput. NiFi’s loosely coupled component-based architecture further makes it easy to develop reusable modules and carry out more effective tests.

Cloud Enablement

Data & AI

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Transform Data to Insights

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Batch or Stream Processing?

Hybrid Processing: Lambda Architecture

Hybrid Processing: Lambda Architecture