BigQuery for Big data engineers - Master Big Query Internals

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

What you'll 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.
• 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.

**[Updated 2024]** - This course is updated as per latest BigQuery UIand features.

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 andManage 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.

Add-Ons

• 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.