Cloud data warehouse comparison: finding the right platform for 2026

Choosing a cloud data platform used to mean picking between a handful of traditional warehouses. Today, the landscape spans fully managed warehouses, open lakehouses, and real-time analytics engines — each built around different tradeoffs between simplicity, cost, openness, and control.

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Quick answer: If you want fully managed simplicity across clouds, Snowflake is the safest default. If your team is code-first and ML-focused, Databricks' lakehouse model fits better. Teams standardized on Microsoft or AWS often get the most value staying inside Microsoft Fabric or Amazon Redshift, respectively. ClickHouse wins on raw real-time query speed, and BigQuery suits teams that want a fully serverless platform on Google Cloud. Whichever you choose, Fivetran connects to all of them — so your data movement layer doesn't have to change even if your platform does. The rest of this guide breaks down why.

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This guide compares the top cloud data platforms side by side so you can match your workload, existing stack, and budget to the right fit — rather than assuming any single platform is the default.

What to know before you compare platforms

Modern data platforms generally fall into a few categories:

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• Fully managed cloud data warehouses (Snowflake, Amazon Redshift, Google BigQuery): SaaS platforms that separate storage from compute so teams can scale without managing infrastructure.
• Lakehouses (Databricks, and increasingly Microsoft Fabric): platforms that combine data lake flexibility with data warehouse structure, often built on open table formats.
• Real-time analytics engines (ClickHouse): databases optimized for sub-second query performance on high-volume event data.

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None of these categories is objectively "better" — the right choice depends on your workload, your existing cloud commitments, and how much control your team wants over infrastructure versus how much you'd rather hand off to a managed service.

Key factors that should drive your decision

• Pricing model: Consumption-based, reserved capacity, and serverless pricing all shift cost and predictability differently. Match the model to how spiky or steady your workloads are.
• Performance requirements: Interactive dashboards, large batch jobs, and real-time analytics all favor different architectures. A platform that excels at one doesn't necessarily excel at all three.
• Open architecture and interoperability: Support for open table formats like Apache Iceberg reduces vendor lock-in and lets multiple engines query the same data without duplication — increasingly important as organizations adopt Open Data Infrastructure (ODI).
• Deployment flexibility: Some teams — especially in regulated industries or with strict latency needs — require on-premises or hybrid deployment options that not every platform supports.
• Governance and security: Compliance requirements vary by industry; confirm a platform meets both your mandatory and voluntary standards before committing.

Quick comparison: the top 6 data platforms

Feature-by-feature comparison

The platforms in detail

Snowflake

Snowflake is a cloud-based data platform that runs entirely on public cloud infrastructure (AWS, Azure, and Google Cloud), separating storage from compute so teams can scale each independently.

Top features:

• Near-instant elasticity: Scale resources up or down automatically to handle any volume of users or datasets.
• Zero-copy cloning: Create instant copies of tables or databases for development and testing without additional physical storage.
• Data sharing: Secure, governed data access across accounts and organizations without copying or moving data.
• Semi-structured data support: Native support for formats like Avro, JSON, and Parquet.
• Cortex AI capabilities: Built-in AI and machine learning features close to the data.
• Integrated dbt support: Embedded transformation workflows directly within the platform.

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Best for: Teams that want fully managed simplicity with elasticity across multiple clouds, without managing infrastructure themselves.

Pricing: Consumption-based, billed via Snowflake credits tied to compute and storage usage.

Consider carefully if: You need on-premises deployment, or you need multi-engine access to the same data without staying inside one platform's ecosystem.

Databricks

Databricks is a lakehouse platform built by the original developers of Apache Spark, combining data lake flexibility with data warehouse structure.

Top features:

• Lakehouse architecture: Uses the open Delta Lake format for reliability and speed across structured and unstructured data.
• Collaborative notebooks: Real-time collaboration in Python, R, SQL, or Scala.
• Unity Catalog: Unified governance for AI and data assets across cloud infrastructure.
• Photon engine: Vectorized processing that accelerates large batch tasks.
• GenAI capabilities: Built-in tools for model development, synthetic data, and AI-driven insights.

Best for: Code-first teams focused on machine learning and data engineering who want an open, lakehouse-native architecture.

Pricing: Consumption-based, billed in Databricks Units (DBUs) depending on workload type and duration.

Consider carefully if: Your team prefers a more turnkey, less code-intensive experience.

Microsoft Fabric

Microsoft Fabric is a unified data platform that combines data integration, analytics, and enterprise-grade data management in a single environment, built on OneLake as its underlying unified data lake.

Top features:

• Unified workspace: A single interface for managing, interpreting, and delivering data.
• Deep Microsoft integrations: Seamless connections to Power BI, Azure Machine Learning, and other Microsoft tools.
• Flexible query options: Choose between dedicated and serverless queries to balance cost and performance.
• Apache Spark support: Built-in Spark integration for large-scale data lakes and ML workloads.

Best for: Organizations heavily invested in the Microsoft ecosystem that want fully integrated analytics and data management.

Pricing: Storage and compute-based, with predictable dedicated-pool rates and pay-per-query serverless options.

Consider carefully if: Your stack isn't primarily built around Microsoft or Azure tools.

Amazon Redshift

Amazon Redshift is a cloud data warehouse built for deep integration with the AWS ecosystem.

Top features:

• Redshift Spectrum: Query data directly from Amazon S3 without loading it into the warehouse first.
• AQUA (Advanced Query Accelerator): Improves query speeds by up to 10 times on large scans, aggregations, and filtering, applied automatically to supported clusters.
• Streaming ingestion: Natively ingest real-time data from Amazon Kinesis Data Streams and Amazon MSK without staging in S3 first.
• Apache Iceberg support: Query and now read/write (including UPDATE, DELETE, and MERGE) Iceberg tables directly from Redshift.
• Concurrency scaling: Automatically handles spikes in user demand without performance degradation.
• AWS affinity: Integrates with S3, Athena, and SageMaker for a seamless AWS-native workflow.

Best for: Organizations already operating in the AWS ecosystem that want deep integration with existing cloud infrastructure.

Pricing: Provisioned (hourly, on-demand) and serverless options.

Consider carefully if: You're not already committed to AWS, or you need multi-cloud flexibility.

ClickHouse

ClickHouse is an open-source, cloud-native database built for speed and efficiency at scale.

Top features:

• Seamless integrations: Compatible with most modern tech stacks, including a "bring your own cloud" model for some customers.
• Interactive SQL console: Load, query, and visualize data with minimal setup.
• Easy scaling: Scale up or down without paying for unused storage or compute.
• Sub-second query latency: Optimized for online analytical processing on event data and high-volume analytics.
• Built-in ML functions: Native statistical and machine learning functions (including regression and vector search) for in-database analysis, though without a full managed ML platform.

Best for: Teams that need sub-second query speeds for real-time analytics, logs, and massive datasets.

Pricing: Based on storage (per terabyte) and compute (per hour), with plans ranging from basic to enterprise.

Consider carefully if: Your priority is broad managed-service simplicity over raw query performance, or you don't have the engineering resources to manage a more hands-on deployment.

Google BigQuery

Google BigQuery is a fully serverless, highly scalable data platform built on Google Cloud.

Top features:

• Managed infrastructure: Google Cloud handles all backend infrastructure — no servers or virtual warehouses to manage.
• BigQuery ML: Build and run ML models directly within the warehouse using standard SQL.
• Real-time streaming: Ingest millions of rows per second for immediate analysis and reporting.
• BI Engine: In-memory analytics that accelerates dashboards to sub-second response times.

Best for: Teams already using Google Cloud or Google Workspace that want a truly serverless platform with zero infrastructure management.

Pricing: Compute and storage-based, with on-demand and capacity-based options. Core Gemini in BigQuery features are included at no added cost, though enterprise-scale usage may require a subscription or custom quote.

Consider carefully if: You're not already on Google Cloud, or you need on-premises or hybrid deployment.

How to choose the right platform for your team

There's no universal "best" data platform — only the best fit for your workload, team, and existing stack. As a starting point:

• Already committed to a specific cloud (AWS, Azure, or GCP)? Redshift, Fabric, and BigQuery respectively offer the deepest native integration.
• Need multi-cloud flexibility and fully managed simplicity? Snowflake is built for exactly that.
• Have a code-first team focused on ML and open architecture? Databricks' lakehouse model is designed around that workflow.
• Need real-time, sub-second analytics at scale? ClickHouse is purpose-built for that use case.

Whichever platform you land on, prioritize open table format support (like Apache Iceberg) if minimizing vendor lock-in and multi-engine flexibility matter to your long-term data strategy.

Interoperability: why Fivetran works with every platform on this list

Interoperability — the ability for multiple engines, warehouses, and lakehouses to access the same governed data without duplication — is a core tenet of Open Data Infrastructure (ODI). It's also what determines whether your stack can actually adapt as your needs, and the market, evolve. A platform-first decision can lock you in; an interoperability-first approach keeps your options open no matter which platform you land on today.

That's where Fivetran fits in. Regardless of which platform you choose from this guide — Snowflake, Databricks, Microsoft Fabric, Redshift, ClickHouse, or BigQuery — Fivetran plugs into all of them. Instead of betting your data movement strategy on a single destination, Fivetran gives you one automated, reliable pipeline layer that works across your entire stack, today and as it evolves.

By integrating validation tools and using Fivetran Transformations, you can automate data cleaning and modeling directly within whichever destination you choose — keeping your data governed, trusted, and ready for analysis no matter where it lives.

This matters even more as organizations adopt open table formats like Apache Iceberg: Fivetran provides the connective layer that makes open tables and multi-platform strategies work in practice, whether you're standardizing on one platform or moving across several as your needs change.

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FAQ

What's the difference between a data warehouse and a data lakehouse?

A data warehouse (like Snowflake, Redshift, or BigQuery) is optimized for structured data and fast SQL analytics. A data lakehouse (like Databricks, and increasingly Microsoft Fabric) combines data lake flexibility — storing structured and unstructured data cheaply in open formats — with data warehouse-style querying and governance on top.

Which data platform is best for real-time analytics?

ClickHouse is purpose-built for sub-second query latency on high-volume event data. Databricks, Microsoft Fabric, Amazon Redshift, and Google BigQuery also support real-time streaming ingestion, though with different latency and cost tradeoffs.

Which platform should I choose if I'm already committed to a specific cloud provider?

If you're on AWS, Amazon Redshift offers the deepest native integration. If you're on Azure, Microsoft Fabric is built around that ecosystem. If you're on Google Cloud, BigQuery is the natural fit. Snowflake and Databricks are multi-cloud and work well if you want to avoid being tied to one provider.

Which data platform is most cost-effective for a growing team?

It depends on workload predictability more than the platform itself. Consumption-based pricing (Snowflake, Databricks) scales with usage but can be less predictable; reserved-capacity or provisioned pricing (Redshift, Fabric) offers more predictable costs at steady volume. ClickHouse's storage-plus-compute model tends to be cost-efficient for high-volume, real-time workloads specifically.

Do these platforms support open table formats like Apache Iceberg?

Yes, to varying degrees. Databricks has native support through Delta Lake, and Snowflake, Microsoft Fabric, Amazon Redshift, ClickHouse, and BigQuery all support Iceberg as well — including growing read/write support on Redshift. If minimizing vendor lock-in is a priority, confirm the specific level of Iceberg support before committing, since capabilities continue to expand across all these platforms.

Is a data warehouse an ETL tool?

No. Data warehouses and lakehouses store and analyze data — most can handle the "transform" step of ETL/ELT, but none of them extract or load data from your source applications on their own. That's why teams typically pair a platform like Snowflake, Databricks, or BigQuery with a dedicated tool like Fivetran to move data in.

Are these platforms SQL-based?

Yes — Snowflake, Databricks, Microsoft Fabric, Amazon Redshift, ClickHouse, and BigQuery are all SQL-based platforms, though several (like Snowflake and BigQuery) also natively support querying semi-structured formats such as JSON, Avro, and Parquet.

Can I switch data platforms later without starting over?

It's easier if you've prioritized open table formats and kept your transformation logic outside the warehouse (for example, using dbt or a tool like Fivetran Transformations). This is also exactly the interoperability problem Fivetran is built to solve — see the section above.

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