batch processing, Big Data, continuous flow operator, Flink, Hadoop, micro-batch model, Real-time processing, Spark

Big Data Computational Models

Most of the distributed processing engine comes along with either one or more of three different types of computational models, namely batch, micro-batch, and continuous flow operator.
  • Batch model: Batch model processes the data at rest, taking a large amount of data at once and then processing it and then writing out the output to some file system or data stores.
  • Micro-batch model: Micro-batch combines the aspects of both batch and the continuous flow operator. In this model data gets gathered at perticular time interval and gets executed. Micro-batches are an essentially collect and then process” kind of computational model.
  • Continuous flow operator model: This model processes the data when it arrives, without any delay in collecting or processing the data.
To have better understanding about Micro-batch and Continuous flow model, lets assume you wants to supply water into a water tank situated on top of a building. So there are two ways to do this, first you can collect it in lower basement water tank and then supplying it into the top most water tank from there but in second way you can directly supply the water to top most water tank using the pipe lines. That is essentially the basic difference between micro-batch and continuous flow operator model.

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