Google Cloud BigQuery - A Complete Guide

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Vignesh Sekar Sujatha

4:37:38

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1. Introduction to Google Cloud Platform & Services

1. Learning Objectives.html

2. Introduction On Cloud Computing.mp4

06:18

3. Benefits of Cloud Computing.mp4

09:18

4. Why Google Cloud Platform.mp4

05:24

5. Google Cloud - Regions and Zones.mp4

08:19

6. Google Cloud - Resource Hierarchy.mp4

04:29

7. Google Cloud - Cloud Shell & Cloud Editor.mp4

03:20

8. Google Cloud - Cloud Shell & Cloud Editor[Contd].mp4

06:37

9. Google Cloud - Identity & Access Management (IAM).mp4

10:24

10. Google Cloud - Service Account & Types.mp4

05:44

2. Overview on Data Warehouse

1. Learning Objectives.html

2. Overview on Data Warehouse.mp4

05:24

3. Architecture of Data Warehouse.mp4

01:54

4. Features of Data Warehouse.mp4

02:26

5. Benefits of Data Warehouse.mp4

02:29

6. Challenges of Data Warehouse.mp4

04:15

3. Overview on Cloud BigQuery

1. Learning Objectives.html

2. Introduction on Cloud BigQuery.mp4

07:43

3. Features Of Cloud BigQuery.mp4

04:38

4. Cloud BigQuery in GCP Console.mp4

15:41

4. Overview of Analytics in BigQuery

1. Learning Objectives.html

2. Lab on BigQuery Console.mp4

09:03

3. Lab on BigQuery Console[Contd].mp4

07:55

4. Other Services in BigQuery.mp4

07:26

5. bq - Command line in BigQuery

1. Learning Objectives.html

2. Introduction on bq - command line tool.mp4

15:13

6. Overview on Partitioned Tables in BigQuery

1. Learning Objectives.html

2. Introduction to Partitioned Tables.mp4

03:53

3. Lab on Ingestion time partitioning.mp4

06:36

4. Lab on Time-unit column partitioning.mp4

05:55

5. Lab on Integer range partitioning.mp4

04:38

7. Overview on Clustered Tables in BigQuery

1. Learning Objectives.html

2. Introduction to clustered tables.mp4

04:13

3. Lab on Clustered tables in BigQuery.mp4

04:13

4. When to use Partitioned and Clustered Tables.mp4

05:55

8. Overview on Views and Materialized Views in BigQuery

1. Learning Objectives.html

2. Introduction to views.mp4

05:34

3. When to use clustering and partitioned tables.mp4

05:55

4. Introduction to materialized views.mp4

05:50

9. Client library in BigQuery - Python

1. Learning Objectives.html

2. Introduction on Client library.mp4

11:58

10. Overview on BigQuery Pricing

1. Learning Objectives.html

2. BigQuery Pricing.mp4

06:05

11. Best Practices and Optimization Techniques in BigQuery

1. Learning Objectives.html

2. Best Practices.mp4

02:11

12. External Sources in BigQuery

1. Learning Objectives.html

2. External Data Sources In BigQuery.mp4

05:29

3. Lab on External Data Sources in BigQuery.mp4

04:22

13. Real Time UseCases in BigQuery

1. Learning Objectives.html

2. UseCases of Cloud BigQuery.mp4

03:11

3. Streaming Data to Cloud Bigtable in GCP.mp4

08:17

4. Export BigQuery Table to Cloud Storage Bucket.mp4

06:10

14. Overview on BigLake in BigQuery

1. Learning Objectives.html

2. Overview on Cloud BigLake.mp4

03:23

3. Features of Cloud BigLake.mp4

02:08

4. Lab on Creating your First Cloud BigLake in GCP.mp4

07:29

5. Lab on GCPs BigLake with setting up control policies.mp4

09:17

6. Lab on Upgrading external table to Biglake in GCP.mp4

07:51

7. Pricing of Cloud BigLake.mp4

01:37

8. Limitations of Cloud BigLake.mp4

01:28

Description

A complete E2E knowledge on GCP's BigQuery exploring on various aspects like Administrative, Storage and Analytics.

What You'll Learn?

Learn In & Out of Google Cloud BigQuery with proper Labs from scratch.
Bigquery core concepts like understanding its Architecture, Dataset, Table, View, Materialized View, Schedule queries, Limitations & Quotas.
Data-sets and Queries used in lectures are available in resources tab. This will save your typing efforts.
Knowledge on all the essential concepts related to data engineers like Data Warehouse and Data modeling.
Understand the best practices for building available, scalable, resilient and secure BQ data pipelines on the Google Cloud Platform

Who is this for?

You want to learn BigQuery in Google Cloud Platform

You want to learn Deep Internals of BigQuery components in Google Cloud.

Beginner who's curious to learn about Cloud BigQuery - An Enterprise DataWarehouse solution

What You Need to Know?

Google Cloud account Free trial - (valid debit or credit card)

You have an attitude to learn while having fun

Basic knowledge of SQL

More details

Description
ONLY COURSE YOU NEED TO GET for learning Google Cloud (GCP) BigQuery.

Cloud is the future and GCP is Fastest growing Public cloud. 87 percentage of Google Cloud certified individuals are more confident about their cloud skills.

Google Cloud Platform is one of the fastest-growing cloud service platforms offered today that lets you run your applications and data workflows at a 'Google-sized' scale.

Google Cloud BigQuery is one of the most highly desired Services used in GCPÂ today. Includes each and every, even thin detail of Big Query. Complete In-depth knowledge of Google BigQuery concepts explained from Scratch to ADVANCE to Real-Time implementation.

Have a look at course curriculum, to see depth of Course coverage.
This course also comes with:

Lifetime access to all course material & updates
Q&A Section
Udemy Certificate of Completion

After completing this course, you can start working on any BigQuery project with full confidence.
So, What are you waiting for, Enroll NOW and I will see you inside course.

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.

NOTE
On July 17 2024, an important and Special day and now its even more special by launching my Second course on Udemy.
This is the first release of this course. You will find more contents and updates in upcoming releases(3-4 releases).

Best Wishes,
Vignesh Sekar KF.

Who this course is for:
You want to learn BigQuery in Google Cloud Platform
You want to learn Deep Internals of BigQuery components in Google Cloud.
Beginner who's curious to learn about Cloud BigQuery - An Enterprise DataWarehouse solution

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Google BigQuery

Vignesh Sekar Sujatha

Instructor's Courses

Overall 6 years of IT experience in designing and implementing various machine learning models, ETL data pipelines, Data Analysis, Statistical analysis, Development, Testing and Productizing ml models and data pipelines.● Strong in problem solving and solutioning business problems by breaking down into structured deliverables.● Experience in collaborating with multiple stakeholder teams (Business, product, tech, finance, delivery teams) to prioritize and align on cross-functional processes.● Experience working in a fast-paced environment and make pragmatic engineering decisions in a short amount of time.● Experience in Cloud platforms like Google Cloud Platform and Microsoft Azure Services.● Experience in crafting ML Models implementing feature engineering, inferencing pipelines and real time model predictions for high performance and scalability.● Experience in ML Ops to measure and track model performance● Experience in writing complex SQL queries based on business requirement.● Experience in Handling Big Data using different Hadoop eco system components such as HDFS, YARN, MapReduce, Spark, Pig, Sqoop, Hive, Hbase and Kafka.● Proficiency in programming languages like Python, PySpark and SQL. ● Hands on experience in working with Continuous Integration and Deployment (CI/CD) using Jenkins.● Experience with creating scripts for data modeling and data import and export and automating various activities.● Experienced in Agile Methodologies, Scrum stories and sprints experience in a Python based environment, along with data analytics, data wrangling. ● Team Player with good interpersonal skills, strong understanding of fundamental business processes ● Excellent communication and problem-solving skills. ● Capable of rapidly learning new technologies and processes and successfully applying them to projects and operations

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