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A Complete & deep knowledge BigQuery guide for Data engineers & 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.
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.
Overview
