Transform Data to Insights
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.
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.