Description
Course Content
- Module 1: Introduction to Data Engineering
- Module 2: Building a Data Lake
- Module 3: Building a Data Warehouse
- Module 4: Introduction to Building Batch Data Pipelines,
- Module 5: Executing Spark on Cloud Dataproc
- Module 6: Serverless Data Processing with Cloud Dataflow
- Module 7: Manage Data Pipelines with Cloud Data Fusion and Cloud Composer
- Module 8: Introduction to Processing Streaming Data
- Module 9: Serverless Messaging with Cloud Pub/Sub
- Module 10: Cloud Dataflow Streaming Features
- Module 11: High-Throughput BigQuery and Bigtable Streaming Features
- Module 12: Advanced BigQuery Functionality and Performance
- Module 13: Introduction to Analytics and AI
- Module 14: Prebuilt ML model APIs for Unstructured Data
- Module 15: Big Data Analytics with Cloud AI Platform Notebooks
- Module 16: Production ML Pipelines with Kubeflow
- Module 17: Custom Model building with SQL in BigQuery ML
- Module 18: Custom Model building with Cloud AutoML
Who should attend
This class is intended for experienced developers who are responsible for managing big data transformations including:
- Extracting, loading, transforming, cleaning, and validating data.
- Designing pipelines and architectures for data processing.
- Creating and maintaining machine learning and statistical models.
- Querying datasets, visualizing query results and creating reports
Certifications
This course is part of the following Certifications:
Google Cloud Certified Professional Data Engineer
Prerequisites
To get the most of out of this course, participants should have:
Completed Google Cloud Fundamentals: Big Data and Machine Learning (GCF-BDM) course OR have equivalent experience
Basic proficiency with common query language such as SQL Experience with data modeling, extract, transform, load activities.
Developing applications using a common programming language such as Python Familiarity with basic statistics
- Completed Google Cloud Fundamentals: Big Data and Machine Learning (GCF-BDM) course OR have equivalent experience
- Basic proficiency with common query language such as SQL Experience with data modeling, extract, transform, load activities.
- Developing applications using a common programming language such as Python Familiarity with basic statistics
