Learn how to optimize cloud analytics costs and build efficient data warehousing solutions in this comprehensive webinar that compares pricing models and architectures of major analytics platforms. Dive deep into the mechanics of Snowflake's Virtual Data Warehouse, BigQuery's serverless approach, and ClickHouse's database architecture while discovering strategies to reduce expenses without sacrificing performance. Master the intricacies of building custom analytic stacks using open-source technologies, from selecting Kubernetes distributions to implementing GitOps practices. Explore best practices for constructing cost-effective, high-performance analytics infrastructure, complete with practical guidance on choosing operators, observability platforms, and database solutions that align with specific business needs.
Comparing Snowflake, BigQuery, and ClickHouse - Building Cost-Efficient Analytics Solutions
Overview
Syllabus
Introduction
How Cloud Businesses Work
Snowflake's Virtual Data Warehouse Model
BigQuery's Serverless Query Model
ClickHouse's Database Architecture
Better Comparison: Modernized 'Buy-the-Box'
Effect Of Storage & Compute on ClickHouse Prices
Quick Comparison of Models
What Snowflake Does Well
What Snowflake Does Not Do
How Can You Get a Better Price on Cloud Analytics?
When Is Cloud Analytic Database Pricing a Good Deal?
Pick A Specific Problem
Reality Check Against Snowflake
Modern Analytic Stacks Are Custom Data Platforms
Choose a Kubernetes Distribution
Pick an Open-Source Analytic Database
Pick an Operator to Run the Database
Choose the Observability Platform
Pick a Kubernetes GitOps Implementation
Best Practices for Do-It-Yourself Modern Analytic Stacks
Taught by
Altinity
