Course Introduction
Google BigQuery Analytics and Data Warehousing Training Course provides a practical foundation for professionals who want to analyze large datasets and build modern cloud data warehouse solutions using BigQuery. BigQuery is a fully managed, petabyte-scale analytics data warehouse that supports GoogleSQL and Python-based workflows without requiring organizations to manage traditional warehouse infrastructure.
The course develops essential BigQuery skills for querying, transforming, modeling, and analyzing enterprise data. Participants learn how to work with projects, datasets, tables, views, schemas, nested and repeated data, and GoogleSQL. They also explore analytical functions, aggregations, joins, subqueries, common table expressions, and other techniques required to turn large datasets into meaningful business insights.
A major focus is data warehousing design and performance. Learners examine dimensional modeling, fact and dimension tables, partitioned and clustered tables, materialized views, and strategies for organizing analytical data efficiently. BigQuery partitioning can reduce the amount of data scanned by queries through partition pruning, which can improve performance and help control query costs.
The training also covers business intelligence and interactive analytics. Participants learn how BigQuery integrates with BI Engine and visualization tools, enabling dashboards and reports to query analytical data efficiently. BI Engine uses in-memory caching to accelerate supported BigQuery SQL workloads and can integrate with tools such as Looker, Tableau, Power BI, and other applications through supported interfaces.
Participants are introduced to BigQuery workload management and cost considerations, including slots, reservations, on-demand processing, capacity-based models, and workload isolation. They learn how reservations can separate production, development, BI, and departmental workloads, while monitoring and slot-estimation capabilities can help organizations evaluate resource requirements and optimize capacity.
By the end of the course, participants will be able to develop practical BigQuery analytics solutions, design analytical warehouse structures, write advanced GoogleSQL queries, optimize tables and workloads, connect BI tools, and interpret business data effectively. The course also introduces BigQuery ML, Gemini-assisted analytics, and modern BigQuery data exploration capabilities to provide context for extending traditional data warehousing into AI-enabled analytics.
Duration
5 days
Who Should Attend
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Data analysts seeking practical skills in BigQuery analytics and cloud data warehousing.
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Business intelligence professionals developing modern reporting and analytical solutions.
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SQL developers transitioning from traditional databases to Google Cloud BigQuery.
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Data engineers who need stronger analytical warehouse design and optimization skills.
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Database professionals expanding their expertise into serverless cloud data warehousing.
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BI developers connecting dashboards and reporting tools to BigQuery datasets.
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Data scientists requiring scalable SQL-based data exploration and preparation capabilities.
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Analytics managers overseeing BigQuery reporting, warehousing, and business intelligence initiatives.
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Cloud architects designing scalable Google Cloud data and analytics platforms.
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ETL developers moving traditional data transformation workloads into BigQuery.
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Technical consultants implementing Google Cloud analytics and data warehouse solutions.
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Professionals preparing for BigQuery-focused analytics, engineering, or cloud data projects.
Course Objectives
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Understand BigQuery architecture and its role as a fully managed, scalable cloud data warehouse for large-scale analytics and business intelligence workloads.
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Develop advanced GoogleSQL skills for querying, filtering, joining, aggregating, transforming, and analyzing structured and semi-structured datasets.
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Design effective analytical data warehouse structures using datasets, tables, views, fact tables, dimension tables, and appropriate modeling techniques.
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Create partitioned and clustered tables that support efficient data organization, query performance, and reduced data scanning for appropriate analytical workloads.
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Work with nested and repeated data structures and apply GoogleSQL techniques for querying complex and semi-structured BigQuery datasets effectively.
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Build reusable analytical views, materialized views, and SQL transformations that provide reliable foundations for dashboards, reporting, and downstream analysis.
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Analyze query execution and workload behavior while applying practical techniques for improving performance, controlling resource consumption, and managing analytical costs.
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Understand BigQuery slots, reservations, capacity management, and workload isolation and apply appropriate approaches to different organizational analytics requirements.
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Connect BigQuery with BI and visualization solutions while understanding how BI Engine can accelerate suitable interactive analytics and dashboard workloads.
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Complete an end-to-end BigQuery data warehousing and analytics project that combines data modeling, SQL analysis, optimization, visualization, and business insight development.
Comprehensive Course Outline
Module 1: Introduction to BigQuery and Cloud Data Warehousing
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Understanding BigQuery architecture, serverless data warehousing, projects, datasets, tables, views, jobs, and core Google Cloud concepts.
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Exploring the differences between traditional data warehouses and BigQuery's managed architecture for large-scale analytical workloads.
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Navigating BigQuery Studio and understanding the interfaces available for SQL development, data exploration, notebooks, query history, and analytical workflows.
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Reviewing the end-to-end cloud analytics lifecycle from data ingestion and warehouse modeling through querying, visualization, monitoring, and reporting.
Module 2: GoogleSQL Fundamentals
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Writing GoogleSQL queries using SELECT, WHERE, ORDER BY, GROUP BY, HAVING, aliases, expressions, and conditional logic.
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Working with string, numeric, date, timestamp, mathematical, conditional, and null-handling functions for practical data transformation.
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Applying joins, subqueries, common table expressions, set operations, and multi-stage queries to combine and analyze business datasets.
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Developing readable, maintainable SQL queries that can be reused as foundations for views, reports, dashboards, and analytical workflows.
Module 3: Advanced BigQuery Analytics
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Applying window functions for ranking, running totals, moving averages, partition-based calculations, and comparative business analysis.
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Performing time-series analysis using dates, timestamps, periods, trends, seasonality, growth calculations, and historical comparisons.
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Analyzing customer, sales, financial, operational, marketing, and product datasets using advanced analytical SQL techniques.
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Identifying trends, anomalies, outliers, relationships, and performance changes while connecting analytical results to defined business questions.
Module 4: Data Warehousing and Dimensional Modeling
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Understanding data warehouse architecture and the role of fact tables, dimension tables, measures, attributes, keys, and relationships.
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Designing star and related dimensional models for reporting, analytics, business intelligence, and scalable analytical workloads.
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Applying appropriate grain, surrogate keys, slowly changing dimension concepts, and business definitions when developing analytical warehouse structures.
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Creating logical warehouse layers that separate raw, transformed, curated, and presentation-ready datasets for improved usability and maintainability.
Module 5: BigQuery Tables, Partitioning, and Clustering
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Understanding BigQuery storage organization and how partitioning divides large tables into manageable segments for analytical queries.
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Designing time-unit, ingestion-time, and integer-range partitioning strategies according to workload access patterns and business requirements.
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Applying clustering to organize data based on selected columns and improve query performance for appropriate filtering and analytical workloads.
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Using partition pruning and effective query filters to reduce unnecessary data scanning while improving performance and supporting cost-conscious analytics.
Module 6: Data Loading, Transformation, and Integration
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Loading structured and semi-structured data into BigQuery from cloud storage and other supported sources while considering schema and format requirements.
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Building SQL-based transformation workflows that clean, standardize, enrich, aggregate, and prepare data for analytical consumption.
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Working with nested and repeated fields, JSON-oriented structures, and other complex data types commonly encountered in modern analytical environments.
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Exploring external tables, federated queries, and other integration approaches for analyzing data without always requiring it to be physically stored in BigQuery.
Module 7: Query Performance and Cost Optimization
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Understanding BigQuery query execution, data scanning, slots, query stages, and workload characteristics that influence performance.
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Applying SQL optimization techniques such as effective filtering, column selection, partition pruning, appropriate joins, and pre-aggregation.
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Using query history, job information, and performance insights to identify expensive queries, resource bottlenecks, and opportunities for optimization.
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Evaluating materialized views, partitioning, clustering, BI Engine, and other optimization capabilities according to actual workload requirements.
Module 8: BigQuery Workload Management and Capacity
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Understanding BigQuery slots as virtual compute units and how slot availability affects the execution of SQL queries and other workloads.
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Exploring reservations, assignments, baseline capacity, autoscaling, and workload isolation for organizations using capacity-based BigQuery processing.
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Separating production, development, data science, ELT, and BI workloads to reduce resource competition and align capacity with organizational requirements.
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Using workload monitoring and slot-estimation approaches to evaluate capacity needs and make informed resource-management decisions.
Module 9: BI Engine, Dashboards, and Advanced Analytics
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Understanding BI Engine's in-memory architecture and how it can accelerate supported BigQuery SQL queries used by interactive dashboards and BI applications.
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Connecting BigQuery with visualization and business intelligence platforms such as Looker, Tableau, Power BI, Google Sheets, and other supported tools.
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Designing analytical datasets and dashboard-oriented models that support responsive reporting, recurring business analysis, and executive decision support.
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Exploring BigQuery ML, Gemini-assisted analytics, Data Canvas, data insights, and conversational analytics as extensions to traditional SQL-based data analysis.
Module 10: End-to-End BigQuery Analytics and Data Warehousing Project
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Defining a realistic business analytics requirement and identifying appropriate source datasets, warehouse structures, dimensions, measures, and analytical questions.
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Building a BigQuery data warehouse solution with appropriately modeled tables, transformations, partitioning, clustering, views, and reusable analytical datasets.
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Developing advanced GoogleSQL queries and dashboards that communicate important trends, performance indicators, comparisons, and business insights.
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Reviewing the completed solution for performance, scalability, cost management, workload organization, data quality, maintainability, and suitability for production analytics.
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.
