These Apache Beam course modules are designed by industry experts to reflect real-world use cases, best practices, and the latest developments in modern data engineering for professional analytics and cloud-based data platforms. Learners will gain hands-on experience in building and managing scalable data pipelines, integrating third-party data sources, and applying modern Apache Beam best practices for clean, maintainable, and efficient data processing.
Prerequisites
- Programming knowledge – Python or Java
- Understanding of data processing concepts – batch vs. stream processing
- Basic knowledge of ETL (Extract, Transform, Load) workflows
- Familiarity with distributed systems (e.g., Spark, Flink, or Hadoop)
- Knowledge of data formats – JSON, Avro, or Parquet
- Basic cloud computing concepts (especially Google Cloud Dataflow if using GCP)
- Understanding of windowing and event time (for streaming pipelines)
What You Will Learn
- Build and run data pipelines for batch and streaming data
- Use Apache Beam SDKs (Python or Java) to write transformations
- Apply windowing, triggers, and aggregations for stream processing
- Work with multiple runners (e.g., Dataflow, Spark, Flink)
- Read and write data from various sources (BigQuery, Pub/Sub, files, etc.)
- Monitor and optimize pipeline performance
