BigQuery for Big data engineers - Master Big Query Internals

A Complete & deep knowledge BigQuery guide for Data engineers & Analysts; Hands-On Bigquery via Console, CLI, Python lib

4.47 (199 reviews)


8.5 hours


May 2021

Last Update
Regular Price

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What you will learn

Learn Full In & Out of Google Cloud BigQuery with proper HANDS-ON examples from scratch.

Get an Overview of Google Cloud Platform and a brief introduction to the set of services it provides.

Start with Bigquery core concepts like understanding its Architecture, Dataset, Table, View, Materialized View, Schedule queries, Limitations & Quotas.

ADVANCE Big query topics like Query Execution plan, Efficient schema design, Optimization techniques, Partitioning, Clustering, etc.

Build Big data pipelines using various Google Cloud Platform services - Dataflow, Pub/Sub, BigQuery, Cloud storage, Beam, Data Studio, Cloud Composer/Airflow etc.

Learn to interact with Bigquery using Web Console, Command Line, Python Client Library etc.

Learn Best practices to follow in Real-Time Projects for Performance and Cost saving for every component of Big query.

Bigquery Pricing models for Storage, Querying, API requests, DMLs and free operations.

Data-sets and Queries used in lectures are available in resources tab. This will save your typing efforts.


Note : This Bigquery course is NOT intended to teach SQL or PostgreSQL. The focus of the course is kept to give you In-depth knowledge of Google Bigquery concepts/Internals.

"BigQuery is server-less, highly scalable, and cost-effective Data warehouse designed for Google cloud Platform (GCP) to store and query petabytes of data."

What's included in the course ?

  • Brief introduction to the set of services Google Cloud provides.

  • Complete In-depth knowledge of Google BigQuery concepts explained from Scratch to ADVANCE to Real-Time implementation.

  • Each and every BigQuery concept is explained with HANDS-ON examples.

  • Includes each and every, even thin detail of Big Query.

  • Learn to interact with BigQuery using its Web Console, Bq CLI and Python Client Library.

  • Create, Load, Modify and Manage BigQuery Datasets, Tables, Views, Materialized Views etc.

  • *Exclusive* - Query Execution Plan, Efficient schema design, Optimization techniques, Partitioning, Clustering.

  • Build and deploy end-to-end data pipelines (Batch & Stream) of Real-Time case studies in GCP.

  • Services used in the pipelines- Dataflow, Apache Beam, Pub/Sub, Bigquery, Cloud storage, Data Studio, Cloud Composer/Airflow etc.

  • Learn Best practices and Optimization techniques to follow in Real-Time Google Cloud BigQuery Projects.

After completing this course, you can start working on any BigQuery project with full confidence.


  • Questions and Queries will be answered very quickly.

  • Queries and datasets used in lectures are attached in the course for your convenience.

  • I am going to update it frequently, every time adding new components of Bigquery.


BigQuery for Big data engineers - Master Big Query Internals
BigQuery for Big data engineers - Master Big Query Internals
BigQuery for Big data engineers - Master Big Query Internals
BigQuery for Big data engineers - Master Big Query Internals


Introduction to GCP & its services

Introduction to Google Cloud Platform

GCP vs AWS vs Azure - Why choose GCP

Compute Services in GCP

Storage Services in GCP

Big data Services in GCP

AI & ML Services in GCP

Big data ecosystem in GCP

Quiz 1

Introduction to BigQuery

Conventional Datawarehouse Problems

What is BigQuery

BigQuery Out-of-the Box Features

Architecture of BigQuery

Dataset & Table creation

Setup a GCP account

Create a Project

BigQuery UI Tour

Region Vs Multi-region

Create a Dataset

Create a Table

Using BigQuery Dashboard options

Running query with various Query Settings

Caching features & limitations

Querying Wildcard Tables

Wildcard Table Limitations

Schedule, Save, Share a Query

Schema Auto detection

Efficient Schema Design in BigQuery

Design an Efficient schema for BigQuery Tables

Nested & Repeated Columns

Quiz 2

Operations on Datasets & Tables

Copying Datasets

Transfer Service for scheduling Copy Jobs

Native operations on Table for Schema change

Manual operations on Table

Execution Plan of BigQuery

How BigQuery creates Execution Plan of a Query

Understanding Execution Plan in UI Dashboard

Partitioned Tables in BigQuery

What is Partitioning & its benefits

Ingestion time Partitioned Tables

Date column Partitioned Tables

Integer based Partitioned Tables

ALTER, COPY operations on Partitioned Tables

DML operations on Partitioned Tables

Best Practices for Partitioning

Clustered Tables in BigQuery

What is Clustering

When to use Clustering OR Partitioning OR Both

Create Clustered Table

Dos & Don'ts for Clustering

Loading & Querying External Data Sources

Introduction and Create Cloud Storage Bucket

Create & Query Permanent Table on Cloud Storage bucket

External data source Limitations

Views in Bigquery

Introduction to Views & its Advantages

Create Views in BigQuery

Restrict rows at User level in Views

Limitations of Views

Materialized Views in BigQuery

What are Materialized Views

Create a Materialized View

ALTER Materialized View

Design an optimized query for Materialized View

Auto & Manual Refreshes of Materialized Views

Limitations & Quotas of Materialized Views

Best Practices in Materialized Views

Quiz 3

BQ Command Line


Cloud SDK Setup

BQ Basic commands

BQ - Querying Commands

BQ- Dataset creation command

BQ - Create all types of Tables

BQ - Load data into Table

BQ - Exclusive operations

Python Client Library of BigQuery


Python code to create dataset

Python code to create table

Python code to query tables

Build end-to-end Data Pipelines

Case Study Requirements

Apache Beam Pipeline creation

Write Transformations in Beam

Write to BigQuery

Create View for Daily data

Run the Beam Pipeline

Create Reports in Cloud DataStudio

Create monthly reports in DataStudio

BigQuery Pricing

Storage Pricing

Query Pricing

API, DML pricing

Free operations in BigQuery

Google Cloud Pricing Calculator

Best Practices / Optimization Techniques


Methods to restrict data scan

Ways to reduce CPU time

Which SQL anti-patterns to avoid

BONUS - Different File Formats & BEAM

What do we need from a File

Text, Sequence, Avro Files

RC, ORC, Parquet Files

Performance Test results of Various Files

Which File Format to choose

Introduction to Apache Beam

Google Pub/Sub Architecture



Anna26 May 2021

The UI for bigquery has been updated since this course was published- so it takes a little longer to follow

Abdurrahman30 March 2021

the content is great and practical. 1 suggestions is that to increase the assignment for each section. right now only 2 assignments are there in the whole course which is not sufficient for self paratice

Rajnil29 March 2021

The course is a very detailed course on Big Query and explains the various features of Big Query in-depth.

Bill1 March 2021

So far it's a good match. But I was hoping to get a better and deeper understanding about the other gcp services that is explained in the sub-section 3, section 1. But I can understand if the explanation isn't too deep since this course mainly covers the GCP's BigQuery service.

Santosh18 January 2021

As with all his previous courses, J Garg, kept the course simple and engaging, while covering almost all the aspects of the course. The course will give enough confidence to deal with real world problems for Data Engineers. Few things I would like to be added though , are - Publish the course presentations. Add Big Query ML capabilities

Olivier4 January 2021

Good, but if you already have basic knowledge of SQL and data modelisation, some lessons can be skipped.


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