Our primary use case for the solution is running batch jobs. It is mainly used for running computations on large batches of data. So in a case where you have big data, you need to know the analytics on the data, process the data, and present it. Google Cloud Dataflow gives you the scale and processing engine to run expensive computations on your data, quite similar to big data processing engines.
What is Streaming Analytics? Streaming analytics, also known as event stream processing (ESP), refers to the analyzing and processing of large volumes of data through the use of continuous queries. Traditionally, data is moved in batches. While batch processing may be an efficient method for handling huge pools of data, it is not suitable for time-sensitive, “in-motion” data that could otherwise be streamed, since that data can expire by the time it is processed. By using streaming...
I use the solution in my company for data transmission and data storage.
We use Google Cloud Dataflow mainly for batch pipelines, like migrating workload from on-premise data movement to BigQuery or Storage Bucket.
I primarily work with Google Cloud Dataflow on data analytics use cases, and my experience has been good.
We use the solution for data streaming analytics.
We use Google Cloud Dataflow for data pipeline and connecting data.
We use the solution as distributed data pipelines.
We use Google Cloud Dataflow for building data pipelines using Python.
Our primary use case for the solution is running batch jobs. It is mainly used for running computations on large batches of data. So in a case where you have big data, you need to know the analytics on the data, process the data, and present it. Google Cloud Dataflow gives you the scale and processing engine to run expensive computations on your data, quite similar to big data processing engines.
we are using Google Cloud Dataflow for retailers and eCommerce.