In this article we are going to discuss the next stage of the big data pipeline which involves processing your raw data stored in cloud storage. We are going to take a look at Google Cloud’s data processing service Dataflow and the AWS, as well as Azure equivalents. We will also look at some of the benefits to using Dataflow and it’s capabilities in a big data pipeline.
So, what exactly is Dataflow and how can it be used to create the most value for your organization? Dataflow is Google Cloud’s serverless data processing service that is both cost-effective and fast. Dataflow supports both batch processing and streaming data which is used extensively in big data pipelines.
Let’s take a look at how Dataflow actually works. Dataflow uses the Apache Beam library, an open-source library that allows you to process batch and streaming data using processing jobs. These jobs are in essence clusters of virtual machines that Dataflow spins up. A key feature of Dataflow is that it automatically provisions and manages these clusters, so that you can focus on how to create value from your processed data as opposed to managing the infrastructure. Dataflow dynamically scales depending on the computational complexity of the job ensuring that performance is always maintained. It intelligently distributes tasks to different workers for a specific job, and can change the order of operations in your pipeline to ensure the best and optimal performance.
There are many reasons as to why Dataflow is a great choice to perform the data processing stage of our big data pipeline. Firstly, Dataflow minimizes the operational overhead and management of the infrastructure required for data processing, since it automatically scales having appropriate security configurations and high availability. Dataflow offers integrations with Stackdriver which is Google Cloud’s service offering for monitoring data pipelines – ensuring that your pipeline is always compliant and allowing you to troubleshoot any data processing issues as quickly as possible. The Apache Beam library used by Dataflow also allows for quick and effective development and integration into your big data pipeline. Since Apache Beam is open-source, the library is supported by a community and frequently updated to ensure that it is working as effectively as possible. Another benefit of Dataflow is that it allows for integrations with Tensorflow allowing for your data to be easily used in various AI and machine learning projects. Overall, Dataflow allows for easy management, streamlining and simplification of your big data pipeline, supporting popular services such as Google Cloud’s Pub/Sub and BigQuery which are often used in data pipelines.
Dataflow is a popular Google Cloud service to include and use in your big data pipeline, making the ingestion and processing of your data as easy and high performing as possible. AWS offers two services, Data Pipeline and Glue, that can both be used for data processing and ETL similar to Dataflow, and Azure has Data Factory providing similar functionality. In the next article, we are going to look at some of the Google Cloud services that can be used to derive insights from your data and create value for your business, some of these services include, BigTable, Data Studio, Datalab and BigQuery.