BigQuery for Big data engineers - Master Big Query Internals
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 etc.
- Learn to interact with Bigquery using Web UI, 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.
Requirements
- Basic knowledge of SQL
Description
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 UI dashboard, 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.
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.
Who this course is for:
- Students who want to learn Deep Internals of BigQuery components
- Data engineers, intending to build end-to-end Data pipelines in GCP (Google Cloud Platform)
- Anyone planning to give Google Cloud Data engineer certification.
Course content
- Preview02:13
- 06:03GCP vs AWS vs Azure - Why choose GCP
- 05:35Compute Services in GCP
- 05:32Storage Services in GCP
- 03:56Big data Services in GCP
- 08:27AI & ML Services in GCP
- Preview04:47
- 5 questionsQuiz 1
Instructor
![J Garg - Hadoop Real Time Learning](data:image/svg+xml,%3Csvg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 64 64"%3E%3C/svg%3E){kind=avatar}
- 4.4 Instructor Rating
- 1,882 Reviews
- 12,649 Students
- 4 Courses
At Hadoop Real time Learning, the courses are made keeping in mind the Real-time implementation of Big data technologies in Live Projects. We make courses which majorly consist of Hands-On & Practicals. All our courses contain a detailed knowledge of a technology from Scratch to Advance level. Course's lectures explain the codes in such a way that even a Non-technical person can understand.