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IT & Software Other IT & Software Google BigQuery

BigQuery for Big data engineers - Master Big Query Internals

A Complete & deep knowledge BigQuery guide for Data engineers & Analysts; Hands-On Bigquery via UI, CLI, Python library
Bestseller
Rating: 4.6 out of 54.6 (60 ratings)
518 students
Created by J Garg - Hadoop Real Time Learning
Last updated 12/2020
English
English
30-Day Money-Back Guarantee

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.
Curated for the Udemy for Business collection

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

19 sections • 101 lectures • 8h 25m total length

  • Preview02:13
  • GCP vs AWS vs Azure - Why choose GCP
    06:03
  • Compute Services in GCP
    05:35
  • Storage Services in GCP
    05:32
  • Big data Services in GCP
    03:56
  • AI & ML Services in GCP
    08:27
  • Preview04:47
  • Quiz 1
    5 questions

  • Conventional Datawarehouse Problems
    03:41
  • What is BigQuery
    03:38
  • BigQuery Out-of-the Box Features
    04:01
  • Architecture of BigQuery
    05:43

  • Setup a GCP account
    00:42
  • Create a Project
    04:35
  • BigQuery UI Tour
    03:12
  • Preview06:33
  • Create a Dataset
    02:37
  • Create a Table
    08:11
  • Assignment 1
    2 questions

  • Preview08:03
  • Caching features & limitations
    03:41
  • Querying Wildcard Tables
    08:10
  • Wildcard Table Limitations
    04:01
  • Schedule, Save, Share a Query
    09:16
  • Schema Auto detection
    05:14

  • Design an Efficient schema for BigQuery Tables
    05:09
  • Nested & Repeated Columns
    06:13
  • Quiz 2
    5 questions
  • Assignment 2
    1 question

  • Copying Datasets
    05:17
  • Transfer Service for scheduling Copy Jobs
    04:03
  • Native operations on Table for Schema change
    07:32
  • Manual operations on Table
    07:32

  • How BigQuery creates Execution Plan of a Query
    08:10
  • Understanding Execution Plan in UI Dashboard
    05:50

  • Preview05:01
  • Ingestion time Partitioned Tables
    09:22
  • Date column Partitioned Tables
    06:49
  • Integer based Partitioned Tables
    05:05
  • ALTER, COPY operations on Partitioned Tables
    05:25
  • DML operations on Partitioned Tables
    04:17
  • Best Practices for Partitioning
    07:27

  • What is Clustering
    04:42
  • When to use Clustering OR Partitioning OR Both
    03:46
  • Create Clustered Table
    02:54
  • Preview04:37

  • Introduction and Create Cloud Storage Bucket
    06:50
  • Create & Query Permanent Table on Cloud Storage bucket
    04:48
  • External data source Limitations
    02:18

Instructor

J Garg - Hadoop Real Time Learning
Hadoop Trainer
J Garg - Hadoop Real Time Learning
  • 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.

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