What is Apache Beam and how is it used with Dataflow?

 

 Quality Thoughts – Best GCP Cloud Engineering Training Institute in Hyderabad

If you're aspiring to become a certified the Best GCP Cloud Engineer, training in Hyderabad look no further than Quality Thoughts, Hyderabad’s premier institute for Google Cloud Platform (GCP) training. Our course is expertly designed to help graduates, postgraduates, and even working professionals from non-technical backgrounds, education gaps, or those looking to switch job domains build a strong foundation in cloud computing using GCP.

At Quality Thoughts, we focus on hands-on, real-time learning. Our training is not just theory-heavy – it’s practical and deeply focused on industry use cases. We offer a live intensive internship program guided by industry experts and certified cloud architects. This ensures every candidate gains real-world experience with tools such as BigQuery, Cloud Storage, Dataflow, Pub/Sub, Dataproc, Cloud Functions, and IAM.

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GCP Fundamentals + Advanced Concepts

Real-time Projects with Cloud Data Pipelines

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Placement-focused Curriculum

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Hands-on Labs using GCP Console and SDK

Apache Beam and how is it used with dataflow?

 is an open-source, unified programming model designed for both batch and streaming data processing. It allows developers to define data processing workflows (called pipelines) in a language-agnostic way and then execute them on various supported runners such as Google Cloud Dataflow, Apache Flink, or Apache Spark.

In GCP, Apache Beam is the underlying SDK for writing Dataflow pipelines. Developers use Beam SDKs (commonly in Java or Python) to construct pipelines composed of PCollections (parallel data sets) and transforms (operations on data). These pipelines are then executed using the Cloud Dataflow runner, a fully managed service that handles provisioning, scaling, and monitoring.

With Beam, you can apply complex logic like windowing, triggering, stateful processing, and late data handling—all essential for real-time use cases. Beam enables a write-once, run-anywhere model, promoting portability and reuse, while Dataflow handles the distributed execution at scale in the cloud.

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