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• date: 29 June 2025
• posted by: AD-TEAM

Prepare for Google Cloud Data Practitioner | BigQuery, Dataproc, Dataform, Cloud Composer, Looker Studio, Dataflow

What you'll learn

Understand the core services and tools used in Google Cloud for data management, analytics, and orchestration

Design and implement data pipelines using BigQuery, Cloud Composer, Dataflow, Dataform, and Dataproc

Perform data preparation, transformation, and ingestion using Cloud Data Fusion and BigQuery

Analyze and visualize data using BigQuery, Looker Studio, and BigQuery ML

Understand the differences and use cases of data storage options like BigQuery, Cloud Storage, Firestore, Cloud SQL, Bigtable, and Spanner

Apply ETL, ELT, and ETLT concepts in real-world cloud data workflows

Build, schedule, and monitor data workflows using Cloud Composer (Apache Airflow)

Gain hands-on experience through labs aligned with the official certification exam guide

Prepare effectively for the Google Cloud Associate Data Practitioner certification exam

Requirements

No prior Google Cloud experience is required

A basic understanding of data concepts (such as tables, rows, queries) is helpful

Willingness to explore cloud tools and perform hands-on practice

A Google Cloud free-tier account for running labs and exercises

Description

This course is a comprehensive, hands-on learning path designed to help you prepare for the Google Cloud Associate Data Practitioner Certification, following the structure and objectives outlined in the official exam guide.The certification targets individuals working with data in the cloud, requiring foundational skills in managing, processing, analyzing, and visualizing data using Google Cloud technologies.In this course, you'll learn to confidently work across various GCP services and develop a clear understanding of their practical use in end-to-end data workflows.Key Focus Areas:Data Preparation and Ingestion: Learn to differentiate between ETL, ELT, and ETLT, clean and transform datasets, and work with tools like Cloud Data Fusion and BigQuery.Data Analysis and Visualization: Use BigQuery to explore datasets, interpret analytical results, and build impactful dashboards with Looker Studio. Learn to utilize BigQuery ML and AutoML for predictive insights.Data Pipeline Orchestration: Implement and schedule data pipelines using Cloud Composer (Apache Airflow), Dataflow (Apache Beam), Dataform, and Dataproc.Data Management: Understand when to use Cloud Storage, BigQuery, Cloud SQL, Firestore, Bigtable, Spanner, and AlloyDB, including considerations around cost, scale, and performance.This course blends theory with practical labs, real-world scenarios, and project-based exercises to help you internalize concepts and gain confidence.Whether you're aiming to clear the exam or build a strong data foundation in GCP, this course provides everything you need to succeed.

Overview

Section 1: Introduction

Lecture 1 Course Introduction

Section 2: –––––– Part 1: Data Preparation and Ingestion ––––––

Lecture 2 Part Introduction

Lecture 3 Data Manipulation Methods

Lecture 4 Choose Appropriate Data Transfer Tool

Lecture 5 Different Data File Formats

Lecture 6 Choose Appropriate Extraction Tool

Lecture 7 Select Appropriate Storage Solution

Lecture 8 Choose Appropriate Data Storage Location Type

Lecture 9 Structured, Unstructured, and Semi-Structured Data

Lecture 10 Hands-On] gcloud Storage CLI Utility Part 1 - Transfer Data from Local to GCP

Lecture 11 Hands-On] gcloud Storage CLI Utility Part 2 - Transfer Data from Local to GCP

Lecture 12 [Hands-On] Database Migration Part - 1

Lecture 13 [Hands-On] Database Migration Part - 2

Lecture 14 [Hands-On] Database Migration Part - 3

Lecture 15 [Hands-On] Transfer Objects from One GCP Bucket to Another

Lecture 16 [Hands-On] Transfer Objects from Azure Cloud Storage to GCP Bucket

Lecture 17 [Hands-On] Transfer Objects from AWS S3 to GCP Bucket

Lecture 18 [Hands-On] Data Ingestion into BigQuery Using bq CLI

Lecture 19 [Hands-On] Using Python SDK to Interact with Google Cloud Services Part 1

Lecture 20 [Hands-On] Using Python SDK to Interact with Google Cloud Services Part 2

Section 3: –––––– Part 2: Data Analysis and Presentation –––––-

Lecture 21 Part Introduction

Lecture 22 [Hands-On] Data Insight using BigQuery Part 1

Lecture 23 [Hands-On] Data Insight using BigQuery Part 2

Lecture 24 [Hands-On] Data Insight using BigQuery Part 3

Lecture 25 Data visualization using Python Notebook

Lecture 26 BigQuery Data Transfer Service: Dataset Copy

Lecture 27 BigQuery Data Transfer Service: Google Cloud Storage

Lecture 28 ML Use Cases using BigQuery ML and AutoML

Lecture 29 Plan a Machine Learning Project

Lecture 30 Analyse and Visualize Data with Looker

Lecture 31 Complete ML Project with BigQuery

Section 4: –––––– Part 3: Data Pipeline Orchestration –––––––

Lecture 32 Part Introduction

Lecture 33 Selecting a Data Transformation Tools

Lecture 34 Use Cases for ELT and ETL

Section 5: [Hands-on] Google Cloud Composer

Lecture 35 Create Cloud Composer Environment

Lecture 36 Create and Run Basic DAG Pipeline

Lecture 37 ETL DAG - GCS to BigQuery Pipeline

Section 6: [Hands-on] Google Cloud Dataproc

Lecture 38 Create Dataproc Cluster

Lecture 39 Explore Hadoop Distributed File System (HDFS)

Lecture 40 Interact with Hive

Lecture 41 PySpark Jobs on Dataproc

Lecture 42 Run PySpark Job on Dataproc using User Interface (UI)

Lecture 43 Run PySpark Job on Dataproc via Jupyter Notebook

Section 7: [Hands-on] Google Cloud Dataflow

Lecture 44 Using Dataflow Templates to Load Data from GCS to BigQuery

Lecture 45 Create an ETL Pipeline with Dataflow Job Builder

Section 8: –––––– Part 4: Data management –––––––

Lecture 46 Part Introduction

Lecture 47 Principles of Least Privileged Access using IAM

Lecture 48 Different Types of Roles: BigQuery and Storage

Lecture 49 Access Control for Google Cloud Storage Part 1

Lecture 50 Access Control for Google Cloud Storage Part 2

Lecture 51 Google Cloud Storage Classes

Lecture 52 Configure Rules to Delete Objects in BigQuery & Cloud Storage

Lecture 53 High Availability & Disaster Recovery in Cloud Storage & Cloud SQL

Lecture 54 Introduction to Cloud Key Management Service (Cloud KMS)

Section 9: Thank You

Lecture 55 Congratulations

Beginners who want to start a career in cloud data and analytics,Students and professionals preparing for the Google Cloud Associate Data Practitioner Certification,Data analysts, engineers, and business intelligence professionals interested in learning GCP,Anyone who wants to build practical skills in managing and analyzing data on Google Cloud




   
   
   

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