Course Introduction
Google Cloud Data Analytics Fundamentals Training Course provides a practical foundation for professionals who want to understand, build, and operate modern analytics solutions across Google Cloud. The course introduces key services and concepts including BigQuery, Dataflow, Cloud Storage, Pub/Sub, Data Studio, Looker, data modeling, batch and streaming analytics, governance, and cloud-based data workflows. Google Cloud positions BigQuery, Dataflow, Looker, and Data Studio among its core data analytics capabilities.
Participants learn how data moves through a modern cloud analytics architecture, from ingestion and storage through processing, warehousing, visualization, and insight generation. The course examines how Cloud Storage can support data lake patterns, Pub/Sub can provide event ingestion, Dataflow can process batch and streaming data, and BigQuery can provide scalable analytical storage and querying. Dataflow supports unified batch and stream processing and can write transformed data into BigQuery for analytics.
A major focus is BigQuery analytics and GoogleSQL. Learners develop practical skills for querying, filtering, joining, aggregating, transforming, and analyzing large datasets. They also explore analytical data structures, partitioning, clustering, query performance, and cost-aware querying. BigQuery is a fully managed, highly scalable analytics data warehouse designed for large-scale analytical workloads without requiring users to manage traditional warehouse infrastructure.
The course also introduces data processing with Dataflow and Apache Beam. Participants learn the difference between batch and streaming pipelines, understand common transformation patterns, and examine how data can be ingested from services such as Pub/Sub and delivered to analytical destinations such as BigQuery. Dataflow uses the Apache Beam programming model and provides both managed processing and templates for common pipeline scenarios.
Analytics visualization and business intelligence form another important part of the training. Participants explore Data Studio and Looker for building reports, dashboards, governed analytical models, and interactive data experiences. Current Google Cloud documentation describes Data Studio as a visualization and reporting platform with configurable charts, tables, filters, and sharing capabilities, while Looker provides governed business intelligence, data exploration, visualization, and embedded analytics.
By the end of the course, participants will understand how the major components of a Google Cloud analytics ecosystem work together to support data-driven decision-making. Through practical exercises and an end-to-end project, they will develop foundational capabilities in data ingestion, transformation, BigQuery analytics, data visualization, performance optimization, governance, and modern cloud analytics architecture.
Duration
5 days
Who Should Attend
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Data analysts seeking a practical introduction to Google Cloud data analytics.
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Business intelligence professionals developing cloud-based reporting and analytical solutions.
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Data engineers beginning to work with Google Cloud analytics services.
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SQL developers transitioning from traditional databases to BigQuery and Google Cloud.
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BI developers creating dashboards and reports using Google Cloud data platforms.
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Database professionals expanding their skills into cloud-native analytics environments.
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Data scientists requiring scalable cloud data exploration and preparation skills.
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Cloud professionals seeking foundational knowledge of data analytics architectures.
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Reporting specialists responsible for transforming data into business insights.
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ETL developers transitioning traditional data processing workflows to Google Cloud.
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Analytics managers overseeing cloud data, BI, and reporting initiatives.
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Technical consultants implementing Google Cloud analytics solutions for organizations.
Course Objectives
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Understand the fundamental architecture of Google Cloud data analytics and how services such as BigQuery, Dataflow, Cloud Storage, and Pub/Sub work together.
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Develop practical GoogleSQL skills for querying, filtering, joining, aggregating, transforming, and analyzing structured and semi-structured datasets in BigQuery.
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Understand modern data ingestion patterns and select appropriate approaches for batch files, application events, streaming data, and operational data sources.
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Build foundational Dataflow concepts covering batch processing, stream processing, transformations, pipeline execution, and integration with analytical destinations.
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Design basic analytical data models using datasets, tables, views, dimensions, measures, and appropriate structures for business intelligence workloads.
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Apply BigQuery performance and cost optimization practices including effective filtering, partitioning, clustering, query analysis, and responsible data processing.
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Develop interactive reports and dashboards using Google Cloud visualization and BI capabilities while presenting analytical findings clearly to different audiences.
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Understand foundational data governance concepts including access control, data discovery, data quality, security, and responsible management of analytical assets.
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Explore modern Google Cloud analytics capabilities including BigQuery ML, conversational analytics, AI-assisted data analysis, and programmatic analytical workflows.
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Complete an end-to-end cloud analytics project that combines data ingestion, processing, BigQuery analysis, visualization, optimization, and presentation of business insights.
Comprehensive Course Outline
Module 1: Introduction to Google Cloud Data Analytics
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Understanding the role of Google Cloud in modern data analytics and the major services used for data ingestion, processing, storage, analysis, and visualization.
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Exploring the relationship between Cloud Storage, Pub/Sub, Dataflow, BigQuery, Data Studio, Looker, and other analytics components.
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Reviewing common cloud analytics architectures and understanding how batch, streaming, warehouse, lake, and BI workloads fit together.
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Mapping the complete data lifecycle from source systems and ingestion through transformation, analytical storage, visualization, governance, and decision support.
Module 2: Google Cloud Data Storage and Ingestion
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Understanding Cloud Storage concepts and how object storage can provide scalable foundations for data lake and analytics architectures.
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Exploring data ingestion patterns for files, databases, applications, event streams, and other enterprise data sources.
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Understanding Pub/Sub fundamentals and how event-driven messaging can support scalable ingestion of continuously arriving data.
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Designing basic ingestion workflows that organize raw data appropriately before processing and analytical transformation.
Module 3: BigQuery Fundamentals
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Understanding BigQuery architecture, projects, datasets, tables, views, jobs, schemas, and the role of serverless analytical data warehousing.
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Navigating BigQuery Studio and working with SQL editors, query results, job history, data exploration, and analytical development workflows.
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Understanding BigQuery storage and compute concepts and how the platform supports large-scale analytical workloads without traditional infrastructure management.
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Exploring common BigQuery use cases including ad hoc analysis, enterprise reporting, data warehousing, business intelligence, and advanced analytics.
Module 4: GoogleSQL for Data Analytics
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Writing GoogleSQL queries using SELECT, WHERE, GROUP BY, HAVING, ORDER BY, conditional expressions, and common analytical functions.
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Working with joins, subqueries, common table expressions, set operations, and multi-stage queries to analyze information from multiple datasets.
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Applying date, timestamp, string, numeric, mathematical, and null-handling functions to transform and prepare analytical data.
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Using window functions, ranking, running totals, comparisons, and other advanced SQL techniques for practical business analysis.
Module 5: Data Processing with Dataflow
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Understanding Dataflow as a managed service for unified batch and streaming data processing using the Apache Beam programming model.
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Exploring pipeline components including sources, transforms, sinks, windows, triggers, and processing stages for scalable data workflows.
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Building conceptual ETL and streaming architectures that move data from sources such as Pub/Sub into destinations such as BigQuery.
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Comparing templates, Job Builder, and Apache Beam development approaches according to pipeline complexity and engineering requirements.
Module 6: Analytical Data Modeling and Data Warehousing
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Understanding analytical modeling concepts including fact tables, dimension tables, measures, attributes, keys, relationships, and business metrics.
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Designing star-schema and other analytical structures that support reporting, dashboards, business intelligence, and reusable analysis.
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Creating logical layers that separate raw, processed, curated, and presentation-ready data to improve analytical usability and maintainability.
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Applying appropriate data modeling practices to support scalable queries, consistent metrics, reliable reporting, and changing business requirements.
Module 7: BigQuery Performance and Cost Optimization
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Understanding query processing, data scanning, query stages, slots, and other workload characteristics that influence BigQuery performance.
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Applying partitioning and clustering techniques to organize large tables and reduce unnecessary data processing for appropriate query patterns.
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Optimizing SQL by selecting required columns, filtering effectively, reducing unnecessary joins, and designing efficient analytical transformations.
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Using query history, job information, and workload analysis to identify expensive queries and opportunities to improve performance and cost efficiency.
Module 8: Business Intelligence, Visualization, and Reporting
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Creating analytical reports and dashboards using Data Studio with charts, tables, filters, date controls, and interactive reporting components.
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Understanding how Looker provides governed data exploration, visualization, business intelligence, and reusable analytical models.
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Connecting BigQuery with BI platforms and understanding how analytical datasets can support interactive dashboards and recurring business reporting.
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Applying data storytelling principles to present trends, comparisons, key performance indicators, exceptions, and other business findings clearly.
Module 9: Governance, Security, and Modern Analytics
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Understanding foundational data governance concepts including data discovery, access control, data quality, security, lifecycle management, and responsible analytics.
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Exploring analytical workflows that use notebooks, Python, BigQuery DataFrames, and other programmatic tools alongside traditional SQL analysis.
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Introducing BigQuery ML and conversational analytics as capabilities that extend traditional SQL-based analysis into machine learning and natural-language data exploration.
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Understanding how governed semantic definitions, trusted datasets, and consistent business metrics support reliable self-service analytics.
Module 10: End-to-End Google Cloud Analytics Project
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Defining a realistic business analytics requirement and identifying appropriate sources, ingestion methods, processing requirements, warehouse structures, and analytical questions.
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Building a practical workflow that ingests data, processes it with appropriate cloud services, stores analytical results in BigQuery, and prepares reusable datasets.
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Developing GoogleSQL analysis and interactive dashboards that communicate trends, metrics, comparisons, and actionable business findings.
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Reviewing the completed solution for performance, cost efficiency, data quality, governance, scalability, maintainability, and suitability for ongoing analytical use.
Training Approach
This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.
Tailor-Made Course
This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808
Training Venue
The training will be held at our Upskill Training Centre. We also offer training for a group (at a discount of 10% to 50%) at requested location all over the world. The Onsite course fee covers the course tuition, training materials, two break refreshments, buffet lunch, airport transfers, Upskill gift package, and guided tour.
Visa application, travel expenses, dinners, accommodation, insurance, and other personal expenses are catered by the participant
Certification
Participants will be issued with Upskill certificate upon completion of this course.
Airport Pickup and Accommodation
Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808
Terms of Payment:
Unless otherwise agreed between the two parties’ payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.
