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    Gcp - Google Cloud Associate Data Practitioner Certification

    Posted By: ELK1nG
    Gcp - Google Cloud Associate Data Practitioner Certification

    Gcp - Google Cloud Associate Data Practitioner Certification
    Published 5/2025
    MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
    Language: English | Size: 4.00 GB | Duration: 8h 30m

    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