Every leader in data engineering will eventually reach this critical point of decision-making. Your data has grown significantly, your pipelines have become increasingly complex, and the business teams you report to need faster and more reliable insights from their data.
At some point, you will need a migration to a cloud based (public or private) data warehouse. However, the true challenge lies in the fact that your choice of cloud data warehouse isn’t simply a choice of database; your choice of platform represents the future foundation for all of your organization’s information systems as they relate to analytics, artificial intelligence, and data-driven decisions.
Leading Platforms
Currently, the three leading platforms in the discussion surrounding cloud data warehousing are as follows:
- Snowflake
- Databricks
- Google BigQuery
Although each of these platforms is designed to meet different types of requirements, is based on differing development philosophies and approaches, they can all provide powerful solutions for some aspect of data warehousing.
Evaluation Differnitator Framework
Why great CTOs don’t just build they evaluate. Use this framework to spot bottlenecks and benchmark performance.
This guide will help you better understand each of the platforms by covering the following topics:
- What is a Cloud Data Warehouse?
- What are Some Key Features and Benefits of Cloud Data Warehouses?
- A Detailed Comparison of the 3 Leading Cloud Data Warehouse Platforms (Snowflake vs. Databricks vs. Google BigQuery)
- What are the Different Pricing Models for Each Platform?
- How to Determine Which Cloud Data Warehouse is Best for your Organization
What is a Cloud Data Warehouse?
A cloud data warehouse is an extended data storage, processing, and analysis system that runs on a cloud infrastructure and is capable of scaling to accommodate the increased volume and complexity of both structured and semi
A cloud data warehouse Solution
A Cloud Data Warehouse Solution is a single place to store your information from many places. A cloud data warehouse combines your data into one large place for all types of analysis.
Key Characteristics of Cloud Data Warehouses
Modern cloud data warehouses:
- Are elastic and grow with your data volume
- Are able to run queries on distributed data across multiple systems quickly
- Integrate with existing analysis tools
Why Are Organizations Moving to Cloud Data Warehouses?
Traditional on-premise data warehouses:
- Have limitations on their ability to grow
- Require maintenance
- Are slow to deploy
Cloud Data Warehouse Solutions:
- Provide on-demand compute services
- Offer per use pricing
- Are fast to deploy
BigQuery is an example of a Serverless Cloud Data Warehouse that follows a more traditional method to handle the needs of customers with large volumes of data for reporting and analysis.
BigQuery
Core Architecture
- A Hybrid Data Warehouse
- A Multi-Cloud Solution
- Serverless Technology
Strengths
- Low cost analytics
- User-friendly
- Fast performance
- Real-time analysis
Limitations
- Not optimized for complex ML
- Limited tools
Use Cases
- Data visualization
- Reporting
- Enterprise analytics
Summary
If you need real-time analysis of large amounts of data at minimal cost, then Google BigQuery is a viable cloud warehouse solution.
Core Features
- Serverless
- Managed infrastructure
- Query engine
Benefits
- Fast queries
- No infrastructure
- Google integration
Drawbacks
- Expensive queries
- Limited control
What is BigQuery
BigQuery is a fully managed data warehouse in the cloud (serverless) where you can run SQL queries against huge datasets while not having to manage any server or other infrastructure.
Key takeaway: BigQuery is designed for speed and ease of use at scale.
Snowflake vs Databricks vs BigQuery Detailed comparison
Architecture comparison
Snowflake
- Warehouse
- Separate compute
- Cloud storage
Databricks
- Lakehouse
- Unified compute
- Data lake
BigQuery
- Serverless
- Managed compute
- Managed storage
Performance
- Snowflake High concurrency
- Databricks Complex workloads
- BigQuery Large queries
Pricing structure
- Snowflake Compute + Storage
- Databricks Usage based
- BigQuery Pay per query
Integration capabilities
- Snowflake Broad ecosystem
- Databricks ML ecosystem
- BigQuery Google ecosystem
Which cloud data warehouse is best for you?
It depends on your use case.
- Analytics → Snowflake
- AI/Data engineering → Databricks
- Serverless → BigQuery
Key takeaway: Each platform serves different workloads.
Benefits of Implementing a Cloud Data Warehouse
- Scalability
- Cost effective
- Quicker time to insight
- Improved collaboration
- Less
Migration to Cloud Data Warehouses
Organizations have increased agility, scalability, and ability to quickly make decisions.
Key Point: Usually the motivation to migrate is based upon the need for scalability and speed.
Selecting the Best Cloud Data Warehouse
Step 1: Define your workload
- Analytics
- ML
- Real-Time
Step 2: Evaluate Your Ecosystem
- Cloud provider
- Tool compatibility
Step 3: Determine Your Team’s Proficiency
- SQL → Snowflake/BigQuery
- Engineering → Databricks
Step 4: Analyze Cost
- Predictability
- Flexibility
Step 5: Run Performance Tests
Mid-size Organization
Simplicity, cost control, scalability.
Key Point: The right solution depends on workload and team.
Comparative Pricing Models
Snowflake
- Compute billing
- Storage separate
Databricks
- DBU billing
- Extra cloud charges
BigQuery
- Per query
- Fixed rate
Key point: Understanding pricing is critical.
Key Issues to consider when adopting a Cloud Data Warehouse
- Do Not Base Your Decision on the Hype
- Ignoring Cost Optimization
- No Governance
- Poor Data Modeling
- Underestimating Migration Complexity
The big takeaway: Success depends on execution.
Conclusion: Choosing an Appropriate Foundation for Your Data and AI
A Cloud Data Warehouse is no longer simply an Analytics Tool.
It now serves as the backbone for:
- Data-Driven decisions
- Real-Time insights
- Ai and Machine Learning
As Data Engineering Leads, choosing the most popular platform to deliver data is not the final objective; instead, Appropriateness to workload, team capabilities, and long term strategies should be the real Influencers.
Logiciel Solutions works with organizations to design and build cloud data platforms where the technical architecture of the Cloud Data Warehouse is designed appropriately for the company's business goals, AI, or other machine learning initiatives. From Migration to Optimization, Logiciel Solutions ensures a Data Infrastructure delivers a measurable Business Impact.
Contact Logiciel Solutions to start building a scaleable, prepare for the future Data Platform.
AI Velocity Blueprint
Measure and multiply engineering velocity using AI-powered diagnostics and sprint-aligned teams.
Frequently Asked Questions
What is a Cloud Data Warehouse, and why would I want to use one?
Cloud data warehouses are scalable systems created specifically to store and analyze huge amounts of data within the cloud. Many organizations depend on cloud data warehouses to manage large amounts of data, allow for higher performance when querying data, and provide real-time analytics without having to manage the infrastructure. Compared to traditional data warehouse systems, cloud data warehouses offer more flexibility, are less expensive, and allow for quicker access to data and analysis.
Who are the leading providers of Cloud Data Warehousing?
Three of the top cloud data warehouse providers are Snowflake, Databricks, and Google BigQuery.
How should I select the appropriate Cloud Data Warehouse for a mid-sized business?
Select based on ease of use, cost control, and scalability.
Which Cloud Data Warehouse has the best integration with BI tools?
Snowflake and Google BigQuery.
How do I migrate my Traditional Data Warehouse to a Cloud-Based Solution?
Evaluate architecture, define goals, migrate step-by-step.
