Build Big data pipelines with Apache Beam in any language and run it via Spark, Flink, GCP (Google Cloud Dataflow).
InstructorJ Garg - Real Time LearningEditorial course preview
These 3 complementary views highlight concrete, legible examples from the course presentation.
This screenshot displays Python code in an IDE implementing an Apache Beam data pipeline, featuring custom functions for profit calculation and timestamping within a PubSub workflow.
This slide introduces Apache Beam as a unified and portable programming model for building Big Data processing pipelines, highlighting its compatibility with engines like Spark and Flink.
This slide outlines additional course benefits, noting that queries are answered quickly and that Beam codes and datasets used in lectures are available in the Resources tab.
The curriculum provides depth in advanced streaming concepts, specifically covering Tumbling, Sliding, Session, and Global windows alongside Watermarks.
Instruction includes deploying pipelines on Google Cloud Dataflow and loading processed data into BigQuery tables.
Some signals suggest the teaching style can feel rushed, with a lack of line-by-line code explanations for complex transformations. This signal predates the displayed course update; the update may have addressed it, but the update label does not prove that it was corrected.
A lack of easy, highly detailed examples may make it difficult to grasp a complete end-to-end workflow.
The curriculum aligns with the stated goals of learning Apache Beam and GCP integration for data engineers.
