Live Online BigQuery Course for Data Engineering

Live Course Module: BigQuery Course for Data Engineering

Total Duration: 40 Hours (4 Weeks)

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WEEK 1: Introduction to BigQuery & Cloud Data Warehousing

Duration: 8 Hours (4 Sessions × 2 Hrs)**

Topics:

1. Introduction to Google Cloud & BigQuery (2 hrs)

   • Overview of Google Cloud Platform (GCP)

   • What is BigQuery and where it fits in the data engineering ecosystem

   • BigQuery architecture: Storage, Compute, and Control plane

   • BigQuery vs Traditional Data Warehouses

2. Setting Up BigQuery Environment (2 hrs)

   • Creating GCP and BigQuery accounts

   • Understanding projects, datasets, and tables

   • BigQuery Web UI, CLI, and Python SDK

3. Loading & Querying Data (2 hrs)

   • Loading data from CSV, JSON, and Parquet

   • Querying datasets using BigQuery SQL

   • Using the public datasets

4. Mini Project + Q&A (2 hrs)

   • Load sample data from GCS → Query & visualize results in BigQuery

Learning Outcome:

✅ Understand BigQuery architecture & setup
✅ Load, query, and manage datasets
✅ Use BigQuery web UI and APIs for basic operations

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WEEK 2: Mastering BigQuery SQL & Data Modeling

Duration: 10 Hours (5 Sessions × 2 Hrs)**

Topics:

1. BigQuery SQL Essentials (2 hrs)

   • SELECT, WHERE, GROUP BY, HAVING, and ORDER BY

   • Joins, Subqueries, and Common Table Expressions (CTEs)

2. Advanced SQL Functions in BigQuery (2 hrs)

   • Window functions, aggregation, and analytical functions

   • Array and Struct data types

   • JSON extraction using BigQuery functions

3. Data Modeling in BigQuery (2 hrs)

   • Star vs Snowflake Schema

   • Designing efficient tables for analytical workloads

   • Partitioned and Clustered tables

4. Query Optimization & Performance Tuning (2 hrs)

   • Understanding execution plans

   • Cost-based optimization, caching, and pricing control

   • Query tuning and best practices

5. Mini Project + Q&A (2 hrs)

   • Build and query a data model for analytical insights

Learning Outcome:

✅ Write efficient analytical SQL queries
✅ Implement optimized data models in BigQuery
✅ Reduce query cost through partitioning and clustering

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WEEK 3: Data Integration, ETL/ELT, and Automation

Duration: 10 Hours (5 Sessions × 2 Hrs)**

Topics:

1. ETL/ELT Workflows with BigQuery (2 hrs)

   • Difference between ETL and ELT

   • Using SQL-based transformations within BigQuery

2. BigQuery with Cloud Storage and Dataflow (2 hrs)

   • Ingesting data from GCS (Google Cloud Storage)

   • Streaming data via Pub/Sub + Dataflow into BigQuery

3. BigQuery Integration with Data Engineering Tools (2 hrs)

   • Orchestrating pipelines using Apache Airflow (Cloud Composer)

   • Integrating with dbt for transformation workflows

4. BigQuery and BI Tools (2 hrs)

   • Connecting BigQuery with Looker Studio, Tableau, Power BI

   • Real-time dashboard creation

5. Mini Project + Q&A (2 hrs)

   • Build an automated data pipeline from GCS → BigQuery → BI dashboard

Learning Outcome:

✅ Build ETL and ELT pipelines with BigQuery
✅ Integrate BigQuery with Airflow, dbt, and BI tools
✅ Automate ingestion and transformation workflows

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WEEK 4: Advanced Features, Security, and Capstone Project

Duration: 12 Hours (6 Sessions × 2 Hrs)**

Topics:

1. BigQuery Scripting & Stored Procedures (2 hrs)

   • Using scripting for automation

   • Control flow statements and variable handling

2. Data Governance & Security (2 hrs)

   • Access controls: IAM roles, datasets, and column-level security

   • Data masking and encryption

   • Logging and auditing with Cloud Logging

3. BigQuery ML (2 hrs)

   • Building ML models directly in BigQuery using SQL

   • Model evaluation and prediction queries

4. Monitoring, Cost Control, and Optimization (2 hrs)

   • Query cost estimation

   • Quotas and limits

   • Managing and monitoring usage in production

5. Capstone Project Development (2 hrs)

   • Design and implement an end-to-end cloud data warehouse

   • Include ingestion, transformation, and reporting

6. Capstone Presentation & Feedback (2 hrs)

   • Project presentation and instructor review

   • Best practices and industry tips

Learning Outcome:

✅ Implement governance and security in BigQuery
✅ Use BigQuery ML for analytics and forecasting
✅ Deploy production-ready data pipelines with cost efficiency

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🧩 CAPSTONE PROJECT EXAMPLE

Project Title: Cloud Data Warehouse for E-Commerce Analytics
Goal: Build an end-to-end pipeline that ingests e-commerce data from GCS, transforms and aggregates it in BigQuery, and visualizes sales analytics using Looker Studio.
Stack: GCP, BigQuery, GCS, Dataflow, Airflow (Composer), Looker Studio

FINAL COURSE OUTCOMES

By the end of this 4-week (40-hour) live training, learners will be able to:

✅ Set up and manage BigQuery environments on GCP
✅ Design optimized schemas and write analytical SQL
✅ Build automated ETL/ELT pipelines with Airflow, Dataflow, and dbt
✅ Integrate BigQuery with BI tools for analytics dashboards
✅ Implement data governance, cost control, and security
✅ Deploy a real-world BigQuery Data Engineering project end-to-end