GCP – Google Cloud Associate Data Practitioner Certification

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
Homepage

https://www.udemy.com/course/gcp-google-cloud-gcp-associate-data-practitioner-certification/