Data Engineering and Analytics on GCP (Google Cloud Platform)

Description:

This training provides an in-depth introduction to data engineering and analytics on Google Cloud Platform (GCP). Participants will explore key GCP services such as BigQuery, Dataflow, and Cloud Storage while learning to build scalable data pipelines and analyze datasets effectively. The training focuses on hands-on application of GCP tools to address real-world data challenges. By the end of the day, attendees will be equipped to design and implement efficient data workflows and analytics solutions on GCP.

Duration: 1 Day

Course Code: BDT34

Learning Objectives:

By the end of this training, participants will be able to:

• Identify the core data engineering and analytics tools on GCP.
• Build data pipelines using Cloud Storage, Dataflow, and Pub/Sub.
• Analyze large datasets with BigQuery.
• Design workflows to integrate real-time and batch processing.
• Optimize data solutions for cost and performance on GCP.

• Basic familiarity with data concepts and cloud computing is recommended. Knowledge of SQL is helpful but not required.

• Data engineers and analysts exploring GCP for data solutions.
• IT professionals interested in building scalable data workflows on GCP.
• Business leaders seeking to understand GCP analytics capabilities.

Course Outline:

Module 1: Introduction to GCP for Data Engineering and Analytics

• Overview of GCP’s Data Ecosystem
• Key Services: BigQuery, Dataflow, Cloud Storage, and Pub/Sub

Module 2: Data Storage and ETL Pipelines on GCP

• Storing and Managing Data with Cloud Storage
• Creating ETL Pipelines with Dataflow
• Hands-On: Building a Data Pipeline

Module 3: Analytics with BigQuery

• Introduction to BigQuery: Architecture and Features
• Querying and Analyzing Datasets
• Hands-On: Writing and Executing BigQuery SQL Queries

Module 4: Real-Time Data Processing with Pub/Sub

• Introduction to Pub/Sub for Streaming Data
• Designing Real-Time Data Workflows
• Hands-On: Processing Streaming Data

Module 5: Use Cases, Best Practices, and Wrap-Up

• Real-World Applications of GCP in Data Engineering
• Best Practices for Performance and Cost Optimization
• Q&A and Additional Resources

Training material provided: Yes (Digital format)