Course Introduction
Snowflake Data Analytics Fundamentals Training Course provides a practical foundation for professionals who want to use Snowflake to explore, query, transform, visualize, and interpret data for business decision-making. The course introduces the Snowflake platform, Snowsight, SQL-based analytics, databases, schemas, tables, views, virtual warehouses, and analytical workflows, helping participants build the skills required to work confidently with modern cloud data platforms.
Participants learn how to use SQL to investigate datasets, identify patterns, calculate business metrics, combine information from multiple tables, and produce meaningful analytical results. The training covers filtering, aggregation, joins, subqueries, common table expressions, date analysis, window functions, and other techniques that enable analysts to move from basic data exploration to more sophisticated business and operational analysis within Snowflake.
The course provides hands-on experience with Snowsight, Snowflake's web interface for working with data through SQL and Python, monitoring query performance and history, visualizing worksheet results, and creating dashboards. Participants learn how to navigate Snowflake objects, work with worksheets, inspect data, execute analytical queries, and organize outputs into reusable assets for reporting and decision support.
A strong emphasis is placed on analytical data modeling and the development of reliable business metrics. Participants explore tables, views, dimensional structures, relationships, facts, dimensions, and metrics, while learning how semantic views can provide a business-friendly layer over physical data. Snowflake documentation identifies semantic views as schema-level objects that define business concepts and support consistent data analysis.
The training also introduces modern AI-assisted analytics capabilities in Snowflake. Participants gain foundational awareness of Cortex Analyst and semantic views, including how natural-language questions can be translated into SQL against governed structured data. Current Snowflake documentation recommends semantic views for new implementations and describes their use in defining business entities, dimensions, facts, metrics, and relationships.
By the end of the course, participants will be able to perform practical analytics in Snowflake, develop effective SQL queries, build reusable analytical views, create dashboards and visualizations, interpret query results, and apply foundational performance and governance practices. The course culminates in an end-to-end analytics exercise that brings together data exploration, SQL analysis, business metrics, visualization, and modern semantic analytics concepts.
Duration
5 days
Who Should Attend
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Data analysts seeking practical Snowflake analytics skills.
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Business intelligence professionals working with cloud-based analytical platforms.
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SQL developers transitioning to Snowflake for modern data analytics.
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Reporting specialists responsible for preparing business and operational reports.
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Business users who want to develop stronger self-service analytics capabilities.
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Database professionals expanding their knowledge of cloud data warehousing.
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Data engineers who need a stronger understanding of the analytics layer in Snowflake.
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Data scientists requiring SQL skills for data exploration and analytical preparation.
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BI developers creating reports, dashboards, and analytical data models.
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Analytics managers and technical leads overseeing Snowflake analytics initiatives.
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Consultants implementing Snowflake-based analytics solutions for organizations.
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Professionals preparing for Snowflake-focused data analytics projects or certification learning.
Course Objectives
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Understand the fundamental architecture of Snowflake and how databases, schemas, tables, views, warehouses, and Snowsight support modern data analytics.
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Develop practical SQL skills for querying, filtering, joining, aggregating, transforming, and analyzing structured business data stored in Snowflake.
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Explore datasets systematically using Snowsight worksheets, data previews, query results, and analytical techniques to identify useful business patterns.
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Create reusable views and analytical datasets that provide consistent foundations for reporting, dashboards, metrics, and downstream analytical activities.
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Apply advanced SQL techniques such as window functions, common table expressions, date calculations, conditional logic, and multi-table analytical queries.
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Develop meaningful business metrics and analytical calculations while understanding how dimensions, facts, relationships, and semantic definitions support consistent analysis.
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Create effective visualizations and dashboards in Snowsight that communicate trends, comparisons, performance indicators, and other important analytical findings.
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Understand foundational query performance practices, including warehouse considerations, query history, execution behavior, and techniques for improving analytical workloads.
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Explore Snowflake semantic views and Cortex Analyst concepts to understand how governed business semantics can support natural-language data exploration.
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Complete an end-to-end Snowflake analytics project that combines SQL querying, data modeling, metrics, visualization, interpretation, and presentation of business insights.
Comprehensive Course Outline
Module 1: Introduction to Snowflake Data Analytics
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Understanding Snowflake architecture and the role of cloud data warehousing in modern analytical environments.
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Exploring databases, schemas, tables, views, stages, warehouses, roles, and other fundamental Snowflake objects.
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Navigating Snowsight and understanding worksheets, data exploration, query execution, visualizations, dashboards, and monitoring capabilities.
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Reviewing the end-to-end analytics lifecycle from data discovery and SQL development through visualization, interpretation, and business reporting.
Module 2: SQL Fundamentals for Snowflake Analytics
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Writing SELECT statements and applying filtering, sorting, aliases, expressions, and conditional logic to analytical datasets.
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Using aggregation functions, GROUP BY, HAVING, DISTINCT, and other SQL techniques to summarize business and operational information.
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Working with string, numeric, date, timestamp, and null-handling functions to prepare data for meaningful analysis.
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Developing readable and maintainable SQL queries that can be reused for analytical datasets, views, reports, and dashboards.
Module 3: Multi-Table Data Analysis
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Using INNER, LEFT, RIGHT, and FULL joins to combine information from customers, products, transactions, operations, and other related datasets.
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Applying common table expressions and subqueries to structure complex analytical logic into manageable and reusable query components.
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Understanding primary business relationships, join keys, cardinality, and duplicate-record risks when combining multiple analytical tables.
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Designing multi-table queries that produce accurate results while avoiding unintended row multiplication and inconsistent analytical calculations.
Module 4: Advanced SQL Analytics
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Applying window functions for ranking, running totals, moving calculations, comparisons, and partition-based analytical analysis.
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Performing time-series analysis using dates, timestamps, periods, trends, comparisons, and business reporting intervals.
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Building analytical calculations for percentages, variances, contributions, rankings, growth rates, and key performance indicators.
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Using advanced SQL patterns to investigate trends, anomalies, customer behavior, operational performance, and other business questions.
Module 5: Analytical Data Modeling and Views
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Understanding analytical modeling concepts including facts, dimensions, business entities, relationships, and dimensional structures used for reporting.
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Creating and using standard views to simplify complex SQL logic and provide consistent analytical interfaces for business users.
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Designing reusable datasets and views that separate business logic from individual reports and reduce duplicated analytical calculations.
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Introducing Snowflake semantic views and their role in defining business concepts, metrics, dimensions, facts, and relationships for governed analytics.
Module 6: Data Visualization and Snowsight Dashboards
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Creating charts from SQL query results and selecting visualization types that appropriately communicate trends, comparisons, distributions, and performance.
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Building dashboards from analytical queries and organizing charts into useful collections for operational, management, and executive reporting.
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Applying filters, formatting, labels, titles, and layout techniques to improve dashboard clarity and analytical usability.
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Designing dashboards around defined business questions so users can quickly understand important metrics, trends, exceptions, and performance indicators.
Module 7: Business Analytics and Insight Development
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Translating business questions into analytical requirements, identifying appropriate datasets, dimensions, measures, and calculations for investigation.
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Performing customer, sales, financial, operational, product, and performance analysis using realistic Snowflake datasets and SQL techniques.
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Identifying trends, relationships, outliers, changes, and other meaningful patterns while distinguishing observations from unsupported assumptions.
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Presenting analytical findings clearly by connecting calculated results and visualizations to specific business questions and measurable outcomes.
Module 8: Query Performance and Analytical Workloads
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Understanding the role of virtual warehouses in executing analytical queries and supporting different workloads and user requirements.
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Using query history and available monitoring information to investigate execution behavior and identify opportunities for query improvement.
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Applying practical SQL optimization techniques such as reducing unnecessary data processing, selecting appropriate joins, and simplifying analytical logic.
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Developing responsible workload practices that balance analytical responsiveness, scalability, resource utilization, and operational requirements.
Module 9: Semantic Analytics and Snowflake Cortex
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Understanding how semantic views create a business-friendly analytical layer by defining logical tables, dimensions, facts, metrics, and relationships.
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Exploring Cortex Analyst concepts and how natural-language questions can be translated into SQL against structured Snowflake data.
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Understanding the importance of business definitions, verified examples, semantic metadata, and governed access when building AI-assisted analytics.
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Reviewing current Snowflake approaches to semantic analytics, including Semantic View Autopilot and the transition toward Cortex Agents for conversational analytics.
Module 10: End-to-End Snowflake Analytics Project
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Defining a realistic business analytics requirement and identifying the relevant datasets, dimensions, metrics, relationships, and analytical questions.
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Developing SQL queries and reusable views to transform Snowflake data into accurate and meaningful analytical datasets.
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Creating visualizations and an interactive dashboard that communicates key trends, comparisons, performance measures, and business findings.
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Presenting the completed analytics solution while reviewing query quality, semantic consistency, visualization effectiveness, performance considerations, and future enhancement opportunities.
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.
