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Transform Data to Insights
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?
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
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
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.
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.
