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Calculator - Snowflake vs BigQuery vs Databricks
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Best Data Warehouse for Your Business (2025)
Use our free Data Warehouse Fit Finder to compare Snowflake, Databricks, BigQuery, Azure Synapse, Redshift, Clic and Oracle and discover which cloud platform is best for your size, stack, and growth goals.
Cloud Data Warehouse AI Finder
Answer 7 quick questions to get your personalized match between Snowflake, BigQuery, Databricks, or Redshift and a roadmap for your data foundation.
What's your company size?
This helps us understand your scale and complexity needs
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Help & Support
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Frequently Asked Questions
If you don’t see your question here, we’re always available to help. Get in touch to discuss your needs, explore opportunities, or clarify how we work.
What is a cloud data warehouse, and why does the choice matter?
A cloud data warehouse is a managed platform that stores and processes structured or semi-structured data for analytics, reporting, and AI. Choosing the right one matters because each platform (Snowflake, BigQuery, Databricks, Redshift, etc.) is optimized for different workloads, team sizes, and architectures. Snowflake is known for governance and multi-tool integration. BigQuery excels in elastic cost control and simplicity. Databricks focuses on advanced analytics and machine learning. Selecting the wrong warehouse can lead to 12–18 months of rebuilds, high costs, and poor adoption.
Which data warehouse is best for my business — Snowflake, BigQuery, or Databricks?
The best data warehouse depends on your data maturity, workload type, and business goals: Scenario Recommended Platform You need governance, security, and multiple BI tools Snowflake You want scalability and minimal maintenance BigQuery You’re building AI or ML workloads Databricks You’re heavily on Microsoft stack Azure Synapse You’re AWS-native and cost-conscious Redshift Use the Data Warehouse Fit Finder to answer 8 questions and get a personalized recommendation based on your size, tech stack, and architecture readiness.
What factors should I consider before migrating to a cloud data warehouse?
Before moving to Snowflake, BigQuery, or Databricks, consider three things: Data maturity – Do you have defined metrics, governance, and ownership? Workload type – Are your needs mostly BI and reporting, or ML and data science? Team bandwidth – Do you have the skills to manage orchestration, modeling, and cost? Most migrations fail because teams move too fast without aligning on architecture and trust. The Fit Finder reveals whether you’re ready to migrate — or if you need to fix your foundation first.
How accurate is the Data Warehouse Fit Finder’s recommendation?
The Data Warehouse Fit Finder is not a sales gimmick. It’s built from a logic model based on hundreds of data stack assessments across mid-market and enterprise companies. Your responses — company size, data goals, technical environment — are weighted against the core strengths of each platform. The output is a data-driven recommendation, not a generic result. Most users find it aligns with what an expert consultant would say after a short discovery call — but you get it in minutes, for free.
What happens after I complete the Fit Finder?
Right after completing the assessment, you’ll see your recommended data warehouse and a readiness score showing how prepared your team is to scale analytics or AI. You can then download a personalized Data Stack Readiness Report, which outlines: Your current data maturity level Top risks to address before scaling The ideal architecture roadmap for your goals
What is a cloud data warehouse, and why does the choice matter?
A cloud data warehouse is a managed platform that stores and processes structured or semi-structured data for analytics, reporting, and AI. Choosing the right one matters because each platform (Snowflake, BigQuery, Databricks, Redshift, etc.) is optimized for different workloads, team sizes, and architectures. Snowflake is known for governance and multi-tool integration. BigQuery excels in elastic cost control and simplicity. Databricks focuses on advanced analytics and machine learning. Selecting the wrong warehouse can lead to 12–18 months of rebuilds, high costs, and poor adoption.
Which data warehouse is best for my business — Snowflake, BigQuery, or Databricks?
The best data warehouse depends on your data maturity, workload type, and business goals: Scenario Recommended Platform You need governance, security, and multiple BI tools Snowflake You want scalability and minimal maintenance BigQuery You’re building AI or ML workloads Databricks You’re heavily on Microsoft stack Azure Synapse You’re AWS-native and cost-conscious Redshift Use the Data Warehouse Fit Finder to answer 8 questions and get a personalized recommendation based on your size, tech stack, and architecture readiness.
What factors should I consider before migrating to a cloud data warehouse?
Before moving to Snowflake, BigQuery, or Databricks, consider three things: Data maturity – Do you have defined metrics, governance, and ownership? Workload type – Are your needs mostly BI and reporting, or ML and data science? Team bandwidth – Do you have the skills to manage orchestration, modeling, and cost? Most migrations fail because teams move too fast without aligning on architecture and trust. The Fit Finder reveals whether you’re ready to migrate — or if you need to fix your foundation first.
How accurate is the Data Warehouse Fit Finder’s recommendation?
The Data Warehouse Fit Finder is not a sales gimmick. It’s built from a logic model based on hundreds of data stack assessments across mid-market and enterprise companies. Your responses — company size, data goals, technical environment — are weighted against the core strengths of each platform. The output is a data-driven recommendation, not a generic result. Most users find it aligns with what an expert consultant would say after a short discovery call — but you get it in minutes, for free.
What happens after I complete the Fit Finder?
Right after completing the assessment, you’ll see your recommended data warehouse and a readiness score showing how prepared your team is to scale analytics or AI. You can then download a personalized Data Stack Readiness Report, which outlines: Your current data maturity level Top risks to address before scaling The ideal architecture roadmap for your goals
[
Help & Support
]
Frequently Asked Questions
If you don’t see your question here, we’re always available to help. Get in touch to discuss your needs, explore opportunities, or clarify how we work.
What is a cloud data warehouse, and why does the choice matter?
A cloud data warehouse is a managed platform that stores and processes structured or semi-structured data for analytics, reporting, and AI. Choosing the right one matters because each platform (Snowflake, BigQuery, Databricks, Redshift, etc.) is optimized for different workloads, team sizes, and architectures. Snowflake is known for governance and multi-tool integration. BigQuery excels in elastic cost control and simplicity. Databricks focuses on advanced analytics and machine learning. Selecting the wrong warehouse can lead to 12–18 months of rebuilds, high costs, and poor adoption.
Which data warehouse is best for my business — Snowflake, BigQuery, or Databricks?
The best data warehouse depends on your data maturity, workload type, and business goals: Scenario Recommended Platform You need governance, security, and multiple BI tools Snowflake You want scalability and minimal maintenance BigQuery You’re building AI or ML workloads Databricks You’re heavily on Microsoft stack Azure Synapse You’re AWS-native and cost-conscious Redshift Use the Data Warehouse Fit Finder to answer 8 questions and get a personalized recommendation based on your size, tech stack, and architecture readiness.
What factors should I consider before migrating to a cloud data warehouse?
Before moving to Snowflake, BigQuery, or Databricks, consider three things: Data maturity – Do you have defined metrics, governance, and ownership? Workload type – Are your needs mostly BI and reporting, or ML and data science? Team bandwidth – Do you have the skills to manage orchestration, modeling, and cost? Most migrations fail because teams move too fast without aligning on architecture and trust. The Fit Finder reveals whether you’re ready to migrate — or if you need to fix your foundation first.
How accurate is the Data Warehouse Fit Finder’s recommendation?
The Data Warehouse Fit Finder is not a sales gimmick. It’s built from a logic model based on hundreds of data stack assessments across mid-market and enterprise companies. Your responses — company size, data goals, technical environment — are weighted against the core strengths of each platform. The output is a data-driven recommendation, not a generic result. Most users find it aligns with what an expert consultant would say after a short discovery call — but you get it in minutes, for free.
What happens after I complete the Fit Finder?
Right after completing the assessment, you’ll see your recommended data warehouse and a readiness score showing how prepared your team is to scale analytics or AI. You can then download a personalized Data Stack Readiness Report, which outlines: Your current data maturity level Top risks to address before scaling The ideal architecture roadmap for your goals
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start with aztela
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Is Data Blocking Your Growth?
Get Free Data Roadmap Right Now
In 30 minutes, we’ll map out your biggest data challenges and show you how to unlock clarity, ROI, and confident decision-making.
[
start with aztela
]
Is Data Blocking Your Growth?
Get Free Data Roadmap Right Now
In 30 minutes, we’ll map out your biggest data challenges and show you how to unlock clarity, ROI, and confident decision-making.
[
start with aztela
]
Is Data Blocking Your Growth?
Get Free Data Roadmap Right Now
In 30 minutes, we’ll map out your biggest data challenges and show you how to unlock clarity, ROI, and confident decision-making.