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Snowflake SQL for Data Analysts

Query, Transform and Analyse Cloud Data with Snowflake SQL

Regular Price: £480
Batch Price: From £420   📅  View Dates & Prices

Group Booking Discount: From £300 per person  🧮 Calculate & Book

1-to-1 Training: £600  📋 Booking Request Form

Course Overview

Snowflake SQL for Data Analysts is a practical 12-hour instructor-led course designed to help learners query, transform and analyse data directly within the Snowflake cloud data platform. Starting with essential SQL querying skills, the course progresses through filtering, aggregation, joins, subqueries, Common Table Expressions (CTEs), analytical functions and other techniques commonly used by Data Analysts.

Participants will work in Snowflake Snowsight using realistic business datasets and learn how to write clear and efficient SQL, analyse large datasets, work with semi-structured JSON data and understand Snowflake-specific concepts such as databases, schemas, virtual warehouses and query history. Through practical exercises and an end-to-end analytics project, learners will develop the confidence to work with Snowflake data directly rather than relying solely on BI tools to generate queries.


Requirements

No previous experience with Snowflake is required.

Participants should have basic experience working with data in spreadsheets, databases or Business Intelligence tools. Previous SQL experience is helpful but not essential, as the course reviews the core SQL concepts before progressing to more advanced analytical techniques.

Learners who are completely new to databases and SQL may benefit from completing our SQL for Beginners course first, particularly if they would like more time to practise fundamental SQL concepts.

You may also complete the following course(s) before attending the Snowflake SQL for Data Analysts course but they are not mandatory:


Course Content

  1. Introduction to Snowflake for Data Analysts
    • What Snowflake is and where it fits in the modern data environment
    • Understanding cloud data platforms
    • Snowflake databases, schemas, tables and views
    • Understanding virtual warehouses
    • Introduction to roles and access
    • Navigating Snowflake Snowsight
    • Working with Snowflake Workspaces
    • Creating and running SQL files
    • Selecting the correct role, warehouse, database and schema
  2. SQL Fundamentals in Snowflake
    • Writing queries with SELECT
    • Selecting individual columns
    • Using column aliases
    • Removing duplicates with DISTINCT
    • Filtering with WHERE
    • Comparison operators
    • Using AND, OR and NOT
    • Working with IN, BETWEEN and LIKE
    • Handling NULL values
    • Sorting results with ORDER BY
    • Limiting query results
  3. Functions, Calculations & Conditional Logic
    • Creating calculated columns
    • Working with arithmetic expressions
    • String functions
    • Numeric functions
    • Date and time functions
    • Converting data types
    • Working with COALESCE()
    • Creating conditional logic with CASE
    • Cleaning and standardising values using SQL
  4. Aggregation & Business Analysis
    • Using COUNT(), SUM(), AVG(), MIN() and MAX()
    • Grouping data with GROUP BY
    • Filtering grouped results with HAVING
    • Creating business KPIs
    • Analysing sales, customers, products and other business dimensions
    • Combining filtering and aggregation
    • Creating multi-level summaries
  5. Joining & Combining Data
    • Understanding relationships between tables
    • Using INNER JOIN
    • Using LEFT JOIN
    • Understanding other join types
    • Joining multiple tables
    • Using aliases effectively in complex queries
    • Identifying duplicate rows caused by joins
    • Combining result sets with UNION and UNION ALL
  6. Subqueries & Common Table Expressions
    • Understanding subqueries
    • Using subqueries in filtering
    • Using derived results in analysis
    • Introduction to Common Table Expressions (CTEs)
    • Creating CTEs with WITH
    • Using multiple CTEs
    • Breaking complex analytical queries into manageable stages
    • Choosing between subqueries and CTEs
  7. Window Functions for Data Analysis
    • Understanding analytical window functions
    • Using the OVER() clause
    • Using PARTITION BY
    • Using ORDER BY within a window
    • Creating row numbers with ROW_NUMBER()
    • Ranking data with RANK() and DENSE_RANK()
    • Comparing previous and next values with LAG() and LEAD()
    • Creating running totals
    • Calculating values within groups
    • Filtering window-function results with QUALIFY
  8. Transforming & Managing Analytical Data
    • Creating tables and views
    • Creating analytical views
    • Using INSERT, UPDATE and DELETE
    • Introduction to MERGE
    • Cleaning data with SQL
    • Handling duplicate and missing data
    • Converting and standardising data types
    • Preparing analysis-ready datasets
    • Understanding temporary and reusable analytical objects
  9. Working with Semi-Structured & JSON Data
    • Understanding structured vs semi-structured data
    • Introduction to Snowflake's VARIANT data type
    • Querying JSON attributes
    • Working with nested objects
    • Working with arrays
    • Extracting values from JSON
    • Converting VARIANT values into standard SQL data types
    • Introduction to FLATTEN()
    • Turning nested data into analytical rows
  10. Loading Data into Snowflake
    • Understanding the Snowflake data-loading process
    • Loading CSV data
    • Introduction to stages
    • Understanding file formats
    • Introduction to COPY INTO
    • Checking loaded data
    • Understanding how business data reaches Snowflake tables
  11. Query Performance & Cost Awareness
    • Understanding compute in Snowflake
    • How virtual warehouses execute SQL queries
    • Understanding warehouse size at a practical level
    • Using Query History
    • Introduction to Query Profile
    • Identifying unnecessarily expensive queries
    • Selecting only the data required
    • Writing clear and efficient SQL
    • Understanding auto-suspend and auto-resume
    • Basic cost awareness for Data Analysts
  12. Practical Snowflake SQL Analytics Project
    • Explore a realistic business dataset in Snowflake
    • Understand the available tables and relationships
    • Clean and transform data using SQL
    • Join multiple datasets
    • Create analytical CTEs
    • Use window functions for advanced analysis
    • Create business KPIs
    • Analyse trends and performance
    • Create reusable analytical views
    • Review query performance
    • Present findings derived directly from Snowflake SQL


Course Dates, Prices & Enrolment

Scroll right for more details
Online Training using Zoom
21 Dec 2026 - 23 Dec 2026
Mon, Tue & Wed
10:00 AM - 02:00 PM BT
£420 £480
Classroom Training
21 Dec 2026 - 23 Dec 2026
Mon, Tue & Wed
10:00 AM - 02:00 PM BT
£480
Online Training using Zoom
10 Feb 2027 - 12 Feb 2027
Wed, Thu & Fri
10:00 AM - 02:00 PM BT
£420 £480
Classroom Training
10 Feb 2027 - 12 Feb 2027
Wed, Thu & Fri
10:00 AM - 02:00 PM BT
£480

Price Calculator & Booking Request Form

Calculate prices for Corporate, 1-on-1 or group training and request a booking.

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Frequently Asked Questions

No. Previous Snowflake experience is not required. The course introduces the Snowflake environment, Snowsight, Workspaces, databases, schemas and virtual warehouses before progressing into practical SQL analysis.

Yes. The course is specifically designed for Data Analysts and other professionals who need to query and analyse data directly within Snowflake.

The emphasis is on practical SQL for analysis rather than Snowflake administration or platform engineering.

Yes. Practical exercises are performed within the Snowflake environment so that learners gain experience writing and running SQL directly against Snowflake data.

The course uses the current Snowsight Workspaces environment rather than teaching SQL only through generic database examples.

Yes. Learners will be introduced to Snowflake's VARIANT data type and learn how to query JSON and other nested data structures.

The course also introduces FLATTEN() for converting nested arrays and objects into a form that can be analysed using SQL.

The course covers query-performance awareness and good SQL practices rather than advanced Snowflake performance engineering.

Learners will use Query History and receive an introduction to Query Profile, while also learning how query design and virtual warehouse usage can affect performance and cost.

Yes. One of the course's main objectives is to help learners feel more confident writing SQL directly in Snowflake, rather than relying entirely on Business Intelligence tools to generate or manipulate queries.

This provides greater control over data preparation, transformation, and analysis before reporting tools use the results.

Previous SQL experience is helpful but not essential. The course reviews core SQL concepts before progressing to joins, subqueries, Common Table Expressions and window functions.

Learners who are completely new to databases and SQL and would prefer more time on the fundamentals may benefit from completing our SQL for Beginners course first.

Yes. The course begins with essential SQL querying, filtering, aggregation and joins before progressing to analytical techniques such as CTEs, window functions, QUALIFY, data transformation and semi-structured data.

Yes. Window functions are an important part of the course because they are widely used in analytical SQL.

Learners will work with functions such as ROW_NUMBER(), RANK(), DENSE_RANK(), LAG() and LEAD(), as well as running totals and Snowflake's QUALIFY clause.

Yes, at a practical introductory level. The course explains stages, file formats and the COPY INTO command and demonstrates how file-based data such as CSV can be loaded into Snowflake tables.

The focus remains on what a Data Analyst needs to understand rather than advanced data ingestion engineering.

No. This course is primarily designed for Data Analysts and focuses on querying and analysing data.

Roles, permissions and virtual warehouses are introduced so that learners understand the Snowflake environment, but advanced administration, security architecture and account management are outside the core syllabus.

Yes. The course concludes with a practical analytics project using a realistic business dataset in Snowflake.

Learners will combine filtering, joins, aggregations, CTEs, window functions and data transformation techniques to answer business questions and produce analysis directly with Snowflake SQL.

What we do?

At London Academy of IT, we provide instructor-led online and in-person IT training in Data Analytics, SQL, Python, Power BI, and more. Our cutting-edge courses are designed to boost performance and enhance employability, providing the competitive edge employers look for.

Our Contacts

London Academy of IT
64 Broadway
Stratford
London E15 1NT
United Kingdom

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