• Post category:StudyBullet-16
  • Reading time:14 mins read

by World-Class Snowflake Expert, former Data Superhero and SnowPro Certification SME

What you will learn

Create and run cost-effective queries

Consolidate underutilized warehouses

Monitor and improve query performance

How using a bigger warehouse could cost you less

Watch over longest running and frequently executed queries

Serverless features in detail: cost and how they work

Avoid cost traps by setting different parameter values

How to combine different Snowflake editions

Save on costs with parallel data transfer and processing

Design and architect applications with cost impact in mind


What You Will Learn About Spending

  • How to use even bigger virtual warehouses for less expensive queries.
  • How to avoid huge cost traps in Snowflake by changing different parameter values.
  • How to optimize queries, compute, storage and overall costs in Snowflake.
  • How to properly consolidate underutilized warehouses.
  • How each serverless feature in Snowflake works and how to estimate their cost.
  • How to use parallel data transfer and processing everywhere you can.
  • How to combine different Snowflake editions in your organization.
  • How to use better data visualizations to estimate spending.
  • How to reduce your spending on all your Snowflake accounts.
  • I also offer over 300 high-quality presentation slides with the summary on each tip or technique!

Where You May Use This Knowledge

  • Drastically reduce costs on your own Snowflake accounts.
  • Create more Snowflake accounts – with different editions – and use them efficiently.
  • Help your clients reduce spending on their Snowflake accounts.
  • Help your employer reduce costs on Snowflake in your own organization.
  • Learn to recognize the traps most people fall into when using Snowflake.
  • Create not just highly performant, but also cost-effective SQL queries.
  • Learn how to get cheaper queries with huge warehouses, using hundreds of nodes.
  • I also offer an open-source GitHub repository with dozens of hands-on exercises and experiments!

What You Will Learn About Snowflake

  • Learn all about the virtual warehouses.
  • Learn how to monitor and optimize queries.
  • Learn about resource monitors and budgets.
  • Learn query acceleration and search optimization
  • Estimate efficient automatic clustering and maintenance of materialized views.
  • Learn all that matters about time travel and zero-copy cloning.
  • Learn how to efficiently deploy and combine Snowflake with other applications.
  • I have many brief but focused hands-on presentations of different Snowflake features!

Who I Am

Get Instant Notification of New Courses on our Telegram channel.

  • The only world-class expert from Canada selected for the Snowflake Data Superhero program in 2021.
  • SnowPro Certification SME (Subject Matter Expert) – many SnowPro exam questions have been created by me.
  • Passed four SnowPro certification exams to date (with no retakes): Core, Architect, Data Engineer, Data Analyst.
  • Specialized in Snowflake for the past few years: I worked for Snowflake Partner companies, I served dozens of clients in this capacity or as an independent consultant, today I share my knowledge with highly specialize courses on Snowflake.

This is truly “the” Bible on Snowflake spending!

No other course, book or documentation around will offer as much insights, hands-on experiments and knowledge transfer on optimizing the cost on Snowflake as my course here, guaranteed!

Enroll today, and keep this course forever!




Course Structure and Content
Best Ways to Benefit from this Course
Create a Free Trial Snowflake Account
Free Hands-On Project Setup

Virtual Warehouses

Introduction to Virtual Warehouses
Tip #1: Larger Virtual Warehouses May Actually Cost You Less
Tip #2: Auto-Suspend Any Warehouse After One Minute
Tip #3: Any Resumed Warehouse Will Cost You at Least One Minute
Tip #4: Never Auto-Suspend Any Warehouse After Less Than One Minute
Tip #5: X-Small Warehouses Could Be Powerful Enough
Tip #6: Resized Warehouses are for More Complex Queries
Tip #7: Multi-Cluster Warehouses are for Multiple Users and Concurrency
Tip #8: Multi-Cluster Warehouses Should Always Have Min Clusters 1
Tip #9: Use Economy Scaling Policy To Save Money
Tip #10: When to Use Snowpark-Optimized Warehouses
Test Your Knowledge

Compute Workloads

Introduction to Compute Workloads
Tip #11: Use Resource Monitors
Tip #12: Use Account-Level Budgets
Tip #13: Prevent Never-Ending Queries
Tip #14: Manually Kill Running Queries
Tip #15: Reduce Warehouse Sizes
Tip #16: Consolidate All Warehouses
Tip #17: Use Parallel Jobs for Batch Transformations
Tip #18: Avoid Checking Too Much on Metadata
Tip #19: Charts for Warehouse Monitoring
Tip #20: Revisit the Main Traps with Warehouses
Test Your Knowledge

Snowflake Accounts

Introduction to Snowflake Accounts
Tip #21: What to Choose for a Free Trial Account
Tip #22: When to Use a Free Trial Account
Tip #23: Understand Price Tables for Virtual Warehouse Compute Services
Tip #24: Understand Price Tables for Cloud and Serverless Services
Tip #25: Understand Price Tables for Storage and Data Transfer
Tip #26: Use the Account Overview Interface in Snowsight
Tip #27: Use Organization Accounts
Tip #28: Limit Warehouse Changes with Access Control
Tip #29: Adjust Default Values of Account-Level Parameters
Tip #30: Careful with Reader Accounts
Test Your Knowledge

Snowflake Editions

Introduction to Snowflake Editions
Tip #31: When to Choose Enterprise over Standard Edition
Tip #32: How to Avoid Multi-Cluster Warehouses
Tip #33: When to Use Incremental Materializations
Tip #34: How to Emulate Materialized Views
Tip #35: The Case for Extended Time Travel
Tip #36: Use Standard Edition Account for Analytics
Tip #37: Use Separate Standard Edition Account for Common Queries
Tip #38: How to Reduce Costs to Zero for an Inactive Paid Account
Tip #39: When to Choose the Business Critical Edition
Tip #40: When to Choose the Virtual Private Snowflake (VPS) Edition
Test Your Knowledge

Query Monitoring

Introduction to Query Monitoring
Tip #41: Monitor Longest Running Queries
Tip #42: Interpret Query History
Tip #43: More Charts for Query Monitoring
Tip #44: Use Query Tags
Tip #45: Reduce Frequency of Simple Queries
Tip #46: Reduce Frequency of Metadata Queries
Tip #47: Reduce Frequency of SHOW Commands
Tip #48: Clone Less Frequently
Tip #49: Change Query Schedules
Tip #50: Parallel over Sequential Transfer and Processing
Test Your Knowledge

Query Optimization

Introduction to Query Optimization
Tip #51: Use the Query Profile
Tip #52: Use the Explain Statement
Tip #53: Use Data Caching
Tip #54: Queries on Data Lakes
Tip #55: Use Vectorized Python UDFs
Tip #56: Use Batch Commands to Prevent Transaction Locks
Tip #57: Reduce Query Complexity and Compilation Time
Tip #58: Check for Cross Joins and Exploding Joins
Tip #59: Process Only New or Updated Data
Tip #60: Remote Spillage Optimization
Test Your Knowledge

Serverless Features

Introduction to Serverless Features
Tip #61: Monitor the Cost of Automated Jobs
Tip #62: Estimate Cost of Scheduled Tasks
Tip #63: When to Use Serverless Tasks
Tip #64: Replace Snowpipe with Snowpipe Streaming
Tip #65: Estimate Cost of Automatic Clustering on Tables
Tip #66: Estimate Cost of the Query Acceleration Service (QAS)
Tip #67: Estimate Cost of the Search Optimization Service (SOS)
Tip #68: Reduce Materialized Views Maintenance Cost
Tip #69: Reduce Database Replication Cost
Tip #70: Estimate Cost of Hybrid Tables
Test Your Knowledge

Data Storage

Introduction to Data Storage
Tip #71: Use On-Demand Storage When You Don’t Know Your Spending Pattern
Tip #72: Copy and Keep Less Data
Tip #73: Lower Data Retention with No Time Travel
Tip #74: Estimate Storage Cost of the Fail-Safe
Tip #75: Use Transient or Temporary Tables
Tip #76: Use Zero-Copy Cloning
Tip #77: Clone Less Data
Tip #78: Ensure Tables Are Clustered Correctly
Tip #79: Drop Unused Tables and Other Objects
Tip #80: Remove Old Files from Stage Areas
Test Your Knowledge

Data Transfer

Introduction to Data Transfer
Tip #81: Data In is Free, Data Out is Expensive
Tip #82: Choose the same Provider and Region Where Your Data Is
Tip #83: External Access Integrations vs External Functions
Tip #84: Use Data Compression
Tip #85: Use Batch Transfer with Path Partitioning
Tip #86: Use Bulk Loads instead of Single-Row Inserts
Tip #87: Use Parallel Data Uploading
Tip #88: Design Cost-Effective Data Pipelines
Tip #89: Use External Tables in a Data Lake
Tip #90: Query Parquet Files instead of CSV
Test Your Knowledge

Snowflake Apps

Introduction to Snowflake Apps
Tip #91: Estimate Cost Impact of Data Sharing in Snowflake
Tip #92: Estimate Cost Impact of Client and Server (Snowpark) Applications
Tip #93: Estimate Cost Impact of Streamlit in Snowflake and Native Applications
Tip #94: Estimate Cost Impact of Data Science Applications
Tip #95: Check All Connected Applications
Tip #96: Third-Party Apps Saving Money Will Spend Money
Tip #97: Free Marketplace Native Apps Will Cost Money
Tip #98: Keep App Versions Updated
Tip #99: Cache Data in Third-Party Tools
Tip #100: Auto-Abort Running Queries from Disconnected Apps
Test Your Knowledge

Wrapping Up

Congratulations, You Made It!
Bonus Lecture