Top Google Cloud Dataflow Alternatives

With increased cloud-based services in different fields like education, entertainment, data analysis and processing, data processing software have also come to garner heed across the web. There is no shortage of data over the internet, but what we need to make sense out of the data is a data processing software such as Google Cloud Dataflow.

Although data processing varies according to the business requirements, there is little that Google Cloud Dataflow (GCD) doesn’t have to offer. Stream Analytics feature offered by Google can be used to organize data efficiently as well as the autoscaling helps in proper resource management and deployment. Furthermore, Cloud Dataflow offers an exceptional error handling interface to manage errors that might otherwise cause permanent damage. This is done by dividing data in arbitrary bundles and erasing the ones which throw an error.

Effortless functionality and management

Google Cloud Dataflow operates on and executes dataflow pipelines. A pipeline-based data processing essentially means that it reads data and then transforms it into something usable before writing it out. Cloud Dataflow, through autoscaling, divides the task into small chunks for various virtual machines to work simultaneously, thus making it quick. Moreover, its serverless approach unburdens you of managing computer resources, thereby automating the dataflow in the best way possible.

Structured documentation

Cloud Dataflow is designed to process data in both, batch as well as streaming modes with the same programming model, which almost no other entrant offers. It enables the user to manipulate aspects of dataflow services by inserting execution parameters in pipeline codes, so as to adjust dataflow accordingly. To further enhance and optimize the process, Dataflow creates an execution graph for you to monitor the pipeline’s log and data aggregation.

Auxiliary Dataflow services

Cloud Dataflow offers its users miscellaneous services ranging from Shuffle to SQL to Templates. Shuffle helps in grouping and joining of data using the back end for batch pipelines, while templates enable you to share these pipelines across the platform with different members. For secure processing, Private IPs are used by Dataflow, also lowering the number of public IP addresses. Moreover, troubleshooting batch and streaming pipelines are also made easy by means of Inline monitoring while the Notebooks integration offers an AI platform to build and run pipelines in an inherent environment.