Google Cloud Dataflow is a data processing service that does not require a server. Cloud Dataflow helps organizations in multiple ways, including:
- Fast streaming pipeline for better data analytics speed
- Less need for resource management and overhead given that it is serverless
- Cost-efficient options
- AI capabilities
One main alternative for Cloud Dataflow is Apache Spark, but Cloud Dataflow is fully managed and has autoscaling capabilities.
The Apache Beam programming model, according to Google, “simplifies the mechanics of large-scale data processing. Using one of the Apache Beam SDKs, you build a program that defines the pipeline. Then, one of Apache Beam’s supported distributed processing backends, such as Dataflow, executes the pipeline.” At Qlogic, we are experts in integration with services like this as well as Cloud Dataflow.
Unity and Cloud Dataflow
Unity, a company that provides tools for game developers as well as relevant in-game ads, was looking for a way to have a single place to gather, organize, and transform data into a useful state that would provide insights and product ideas to contribute to scaling and avoid having data in silos. Additionally, their data was unstructured and depended on daily ETLs, with several different technology solutions that had to be maintained due to multiple acquisitions.
In the end, Unity was able to use Cloud Dataflow along with other GCP products to structure data from multiple places into one and create consistency, latency, and compliance with their data.
Let us Help
- Contributing to development, documentation, and code samples for Cloud Dataflow
- Clean up and optimize your legacy data platforms and structure