Dataflow

1. Dataflow

Let's learn a little bit about Dataflow. Dataflow is a managed service for executing a wide variety of data processing patterns. It's essentially a fully managed service for transforming and enriching data in stream and batch modes with equal reliability and expressiveness. With Dataflow, a lot of the complexity of infrastructure setup and maintenance is handled for you. It's built on Google Cloud infrastructure and autoscales to meet the demands of your data pipelines, allowing it to intelligently scale to millions of queries per second. Dataflow supports fast, simplified pipeline development via expressive SQL, Java, and Python APIs in the Apache Beam SDK, which provides a rich set of windowing and session analysis primitives, as well as an ecosystem of source and sync connectors. Dataflow is also tightly coupled with other Google Cloud services, like Google Cloud Observability, so you can set up priority alerts and notifications to monitor your pipeline and the quality of data coming in and out. This diagram shows some example use cases of Dataflow. As I just mentioned, Dataflow processes stream and batch data. This data could come from other Google Cloud services like Datastore or Pub/Sub, which is Google's messaging and publishing service. The data could also be ingested from third-party services like Apache Avro and Apache Kafka. After you transform the data with Dataflow, you can analyze it in BigQuery, Vertex AI, or even Bigtable. Using Looker Studio, you can even build real time dashboards for IoT devices.

2. Let's practice!

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