Snowflake Data Analytics and SQL Performance Tuning Training Course

Course overview

Snowflake teams often have abundant data but inconsistent query performance, unclear warehouse costs, duplicated transformation logic, and dashboards that cannot be trusted at peak usage. Analysts and engineers need more than SQL syntax: they must understand how Snowflake stores micro-partitions, prunes data, allocates virtual warehouses, caches results, and exposes query behaviour through Query Profile. This course addresses the practical challenge of delivering responsive analytics while controlling consumption and maintaining reliable, governed data products.

Participants build advanced SQL patterns for analytical reporting, including window functions, common table expressions, semi-structured JSON querying, time-series analysis, and reusable views. They learn to diagnose slow or expensive workloads using Query History, Query Profile, warehouse load metrics, and execution details. The course covers clustering strategy, search optimization, materialized views, result caching, warehouse sizing, concurrency management, and query rewrite techniques. Participants also apply secure data-access patterns using roles, secure views, row access policies, and masking policies.

Instruction combines instructor-led demonstrations with guided Snowflake labs based on a realistic retail analytics workload. Participants investigate poorly performing dashboards, transform raw event data, tune reporting queries, and compare cost and elapsed-time results before and after optimisation. They leave with a documented Snowflake analytics optimisation playbook containing query-tuning findings, warehouse recommendations, SQL patterns, and a prioritised improvement plan they can adapt to their own environment.

The course is designed for professionals who already write SQL and work with Snowflake data, and who need to improve analytical workload performance, reliability, governance, or cost control.

Course objectives

By the end of this course, participants will be able to:

• Analyse Snowflake Query Profile output to identify partition scans, spills, skew, queueing, and inefficient operators
• Rewrite analytical SQL using CTEs, window functions, QUALIFY, joins, and predicate placement to reduce execution cost
• Configure virtual warehouse sizing, auto-suspend, auto-resume, multi-cluster scaling, and resource monitors for workload demand
• Apply micro-partition pruning, clustering keys, Search Optimization Service, and materialized views to targeted workloads
• Query and flatten semi-structured JSON data using VARIANT, FLATTEN, lateral joins, and schema-on-read patterns
• Use Query History and warehouse telemetry to build a repeatable SQL performance-investigation workflow
• Implement role-based access controls, secure views, masking policies, and row access policies for governed analytics
• Produce a Snowflake analytics optimisation playbook with baseline metrics, tuning recommendations, and implementation priorities

Benefits of attending

Target competencies

Who should attend

• Data Analysts — who need faster, more reliable SQL queries for reporting and self-service analysis
• Analytics Engineers — who build Snowflake transformation models and must improve their efficiency and maintainability
• Data Engineers — who manage ingestion, data structures, and compute resources supporting analytical workloads
• Business Intelligence Developers — who need to diagnose dashboard latency and optimise Snowflake-backed semantic queries
• Snowflake Administrators — who govern warehouses, monitor consumption, and investigate platform performance issues
• Data Platform Leads — who must establish measurable standards for cost-efficient, secure analytics delivery

Requirements and prerequisites

Participants should be comfortable writing multi-table SQL queries, including SELECT statements, joins, aggregations, GROUP BY, CASE expressions, and basic subqueries. Practical experience using Snowflake worksheets, Snowsight, or another SQL client is expected, along with familiarity with relational tables and business-reporting datasets. Experience creating views or loading data is useful but not essential. Participants should understand the purpose of cloud data warehousing and have access to a Snowflake training account or permitted sandbox environment. Prior experience with Python, dbt, Tableau, machine learning, or database administration is not required.

Training methodology

Each day combines focused instructor-led explanation with live Snowflake demonstrations and hands-on SQL labs. Participants work from query baselines and Query Profile evidence rather than abstract tuning rules, testing changes to predicates, joins, warehouse settings, clustering, and caching behaviour. A retail analytics case study provides shared data, workload patterns, and dashboard requirements for group discussion. In the final workshop, participants consolidate lab evidence into a practical optimisation playbook, including target metrics, recommended changes, ownership, and validation steps for a production Snowflake environment.

Course outline

Day 1: Snowflake architecture and analytical SQL foundations

• Snowflake storage, compute, and cloud services architecture
• Databases, schemas, tables, views, and transient object choices
• Virtual warehouses and independent compute scaling
• Micro-partitions, metadata, and automatic partition pruning
• Advanced SELECT patterns with CTEs and QUALIFY
• Window functions for ranking, running totals, and cohort analysis
• Snowsight Worksheets, Query History, and query tagging

Workshop: Participants baseline a set of retail reporting queries in Snowsight and produce an initial query inventory with elapsed time, bytes scanned, and warehouse usage.

Day 2: Query profiling and SQL performance diagnosis

• Reading Snowflake Query Profile operator trees
• Identifying full scans, excessive partitions, and poor pruning
• Join order, join explosion, and cardinality investigation
• Detecting local and remote disk spilling
• Recognising queueing, compilation time, and execution bottlenecks
• Predicate pushdown and filter placement techniques
• Query rewrite patterns for aggregations, EXISTS, and DISTINCT

Workshop: Participants investigate three deliberately slow SQL queries, annotate Query Profile findings, and deliver rewritten versions with measured performance comparisons.

Day 3: Compute management and cost-efficient workload design

• Warehouse sizing using workload characteristics and query elapsed time
• Auto-suspend, auto-resume, and idle-credit control
• Multi-cluster warehouses for concurrency scaling
• Statement timeouts, queued-query controls, and workload isolation
• Result cache, local disk cache, and metadata cache behaviour
• Resource monitors, budgets, and consumption alert thresholds
• Query tags and ACCOUNT_USAGE views for cost attribution

Workshop: Participants configure a warehouse strategy for competing dashboard, ELT, and ad hoc workloads and produce a cost-control recommendation matrix.

Day 4: Physical optimisation and semi-structured analytics

• Clustering depth and clustering-key selection criteria
• Automatic clustering cost and maintenance trade-offs
• Search Optimization Service use cases and limitations
• Materialized views for repeated aggregate workloads
• Dynamic tables and refresh-lag considerations for analytics
• VARIANT columns, JSON path extraction, and FLATTEN
• Lateral joins and performance patterns for nested data

Workshop: Participants tune an event-stream analytics dataset by testing clustering, materialized-view, and JSON FLATTEN approaches, then document the preferred design.

Day 5: Governed analytics and optimisation planning

• Snowflake RBAC hierarchy and least-privilege role design
• Secure views for controlled analytical data sharing
• Dynamic data masking policies and protected columns
• Row access policies for regional and business-unit filtering
• Data quality checks and reconciliation queries for reporting outputs
• Performance regression testing with baseline query metrics
• Optimisation backlog prioritisation by impact, risk, and effort

Workshop: Participants complete an end-to-end analytics optimisation workshop and produce a Snowflake playbook covering SQL fixes, warehouse actions, governance controls, and validation metrics.

Tools & standards covered

Snowflake, Snowsight, dbt Core, ANSI SQL

A typical training day

Live online deliveries follow the same structure in the East Africa Time zone, with shorter screen blocks and longer breaks.

What the fee includes

How you can take this course

Certification

Participants who complete the full five days receive the Skillset Development Certificate of Completion, stating the course title, course code, dates and delivery format — suitable for professional-development records and employer reimbursement.

Frequently asked questions