Associate Data Practitioner Training

Live Online & Classroom Enterprise Training

Over a series of courses and labs, participants will gain hands-on experience with Google Cloud’s data tools. They will learn to query and manage datasets with BigQuery, prepare data for dashboards in Looker, understand AI/ML product basics, manage infrastructure aspects (storage, monitoring), optimize cloud storage costs, and apply cloud security fundamentals. On completion, learners are better positioned to pursue the Associate Data Practitioner certification.

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What is Associate Data Practitioner Training about?

This learning path builds foundational data skills on Google Cloud. Learners explore data engineering, SQL in BigQuery, dashboard/report preparation with Looker, AI/ML basics, cloud infrastructure, cost optimization, and security fundamentals. The path aims to equip data practitioners to analyze data, build insights, and support data-driven decision-making using Google Cloud tools.

What are the objectives of Associate Data Practitioner Training ?

Build a foundational understanding of data engineering roles, responsibilities, and challenges on Google Cloud.
Write, load, and troubleshoot SQL queries in BigQuery to derive insights from data.
Prepare, filter, merge, and format data to create dashboards and reports in Looker.
Gain an introductory understanding of AI/ML lifecycle, products, and use cases.
Acquire basics of cloud infrastructure, cost optimization strategies, and security best practices on Google Cloud.

Who is Associate Data Practitioner Training for?

Aspiring data practitioners or business analysts looking to work with cloud-based data tools.
Professionals in data/analytics roles aiming to upskill in Google Cloud.
Analysts or BI users wanting to use Looker and BigQuery in their workflow.
IT or operations staff supporting data infrastructure in cloud environments.
Individuals preparing to pursue the Associate Data Practitioner certification or transitioning into data roles.

What are the prerequisites for Associate Data Practitioner Training?

Basic familiarity with data and tabular concepts (e.g. rows, columns, tables).
Some exposure to or willingness to learn SQL.
General awareness of cloud computing or willingness to grasp cloud fundamentals.
Access to a Google Cloud account to perform hands-on labs and exercises.
Willingness to practice, experiment, and engage in lab-based learning.

Available Training Modes

Live Online Training

5 Days

Self-Paced Training

30 Hours

Course Outline Expand All

Expand All

Module 1: Introduction to Data Engineering on Google Cloud

Roles and responsibilities of data engineers and how they map to business needs

Google Cloud’s data engineering offerings and service options

Typical challenges in data pipelines, workflows, and scaling

Mapping data engineering tasks to Google Cloud services and tools

Module 2: Derive Insights from BigQuery Data

Writing SQL queries in BigQuery (SELECT, JOIN, aggregation)

Querying public datasets and exploring existing tables

Loading sample data into BigQuery and managing datasets

Troubleshooting syntax errors via query validator and query debugging

Module 3: Prepare Data for Looker Dashboards and Reports

Filtering, sorting, and pivoting data for reporting needs

Merging results from different Looker Explores or data sources

Using functions and operators in Looker to transform data

Building dashboard visuals, reports, and embedding insights

Module 4: Introduction to AI and Machine Learning on Google Cloud

Overview of AI/ML lifecycle: training, evaluation, deployment

Explore Google Cloud’s AI/ML product offerings (Vertex AI, AI Platform)

Compare predictive vs generative AI models and use cases

Investigate AI/ML solution design considerations and constraints

Module 5: Baseline: Infrastructure

Introduction to Cloud Storage and storage options on GCP

Understanding monitoring, logging, and observability basics

Working with application services in Google Cloud environment

Hands-on lab experiences with storage and monitoring tasks

Module 6: Optimizing Cost with Google Cloud Storage

Configure monitoring for bucket object access and usage

Automate migration of objects across storage classes (e.g., Regional to Nearline)

Analyze storage usage patterns and cost implications

Deploy Cloud Functions or scripts to assist cost-based storage management

Module 7: Implement Cloud Security Fundamentals on Google Cloud

Identity and Access Management (IAM): roles, permissions, policies

Service accounts, identities, and resource access control

Enabling private connectivity, VPC, subnets, and networking security

Secure access best practices, audit logging, and defense in depth

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Who is the instructor for this training?

The trainer for this Associate Data Practitioner Training has extensive experience in this domain, including years of experience training & mentoring professionals.

Associate Data Practitioner Training - Certification & Exam

After completing this course, you can take the below certifications: Google Cloud Associate Data Practitioner Certification

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