Data Engineering on GCP
Gain hands-on introduction to designing and building data processing systems that are capable of handling large sets of structured, unstructured and streaming data on Google Cloud Platform.
Through this course, participants will dive into Google Cloud’s data services by utilizing the power of Big Data to build data processing systems, design end-to-end data pipelines, analyze data and carry out machine learning tasks.
This course is intended for experienced developers who are responsible for managing Big Data transformations. To get the best outcome of this course, participants should have: Completed Google Cloud Fundamentals: Big Data & Machine Learning course OR have equivalent experience.
- Introduction to Data Engineering
- Building a Data Lake & Data Warehouse
- Introduction to Building Batch Data Pipelines
- Executing Spark on Cloud Dataproc
- Serverless Data Processing with Cloud Dataflow
- Manage Data Pipelines with Cloud Data Fusion and Cloud Composer
- Introduction to Processing Streaming Data
- Serverless Messaging with Cloud Pub/Sub
- Cloud Dataflow Streaming Features
- High-Throughput BigQuery and Bigtable Streaming Features
- Advanced BigQuery Functionality and Performance
- Introduction to Analytics and AI
- Prebuilt ML model APIs for Unstructured Data
- Big Data Analytics with Cloud AI Platform Notebooks
- Production ML Pipelines with Kubeflow
- Custom Model building with SQL in BigQuery ML & Cloud AutoML