Deployment of Pipeline with Google Cloud DataFlow

In Google Cloud Platform, Cloud Data Flow is a fully managed streaming analytics service which minimizes latency, cost and processing time. It has the facilities of autoscaling and batch processing which helps in cost optimization. Cloud Data Flow is a serverless approach to resource provisioning and management, which has the immense capacity  to solve complex data processing challenges. You also get the advantage of pay as you use model. 

The features of Cloud Data Flow are :

• Stream and Batch Processing

• AutoScaling in a horizontal manner

• Fast streaming in Analytics

• Dataflow SQL

• Inline Monitoring

• Managed encryption keys

Cloud Dataflow Pipeline

Cloud Dataflow service performs automatically and optimizes many aspects of distributed parallel processing. The processes are:

1. Optimization

2. Parallel Distribution

3. Auto Tuning Features

Cloud Data Flow Pipeline creates a graphical formation of the code that constructs  Pipeline objects, which includes all transformation and their associated processing functions. This process is known as Graph Construction Time. Cloud Dataflow checks for errors and ensures that the pipeline graph doesn't contain any illegal data formats. This operation contains JSON files which are converted into JSON graph which connects with Dataflow service points. In the execution graph, Apache SDK extracts the data to read from it to make a pipeline object. Aggregation operation includes large scale data processing which is very useful for correlation between objects.