Cloud Data Warehouse Comparison - Snowflake vs BigQuery vs Redshift vs Databricks

Introduction: The 2026 Data Warehouse Landscape

The cloud data warehouse market is projected to reach $183B by 2035, growing at a 20.71% CAGR. The competitive landscape has shifted significantly from even a year ago, driven by two forces: AI/agent integration and the convergence of data warehouses with data lakes through open table formats.

Every major vendor now supports Apache Iceberg read and write. Every vendor has embedded AI capabilities directly into the query layer. The result is that platform selection in 2026 depends less on raw SQL performance and more on ecosystem fit, AI strategy, and how well a platform supports your data architecture going forward.

This guide compares the five leading platforms -- Snowflake, BigQuery, Redshift, Databricks, and ClickHouse Cloud -- across architecture, pricing, AI capabilities, and organizational fit.

For data migration support, start here: ETL and data migration services.

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Platform Overview

Snowflake

Snowflake posted FY2025 revenue of $3.626B, up 29% year-over-year. It remains the multi-cloud leader across AWS, Azure, and GCP with a strong emphasis on data sharing and its marketplace ecosystem.

The biggest news in 2026 is a $200M partnership with OpenAI, making GPT-5.2 available directly through Cortex AI. Snowflake also launched Cortex Code, an AI coding agent purpose-built for enterprise data workflows.

Key characteristics:

• Multi-cloud deployment (AWS, Azure, GCP)
• Independent compute and storage scaling
• Cortex AI with GPT-5.2 integration and Cortex Code agent
• Apache Iceberg write support GA (Oct 2025) and Apache Polaris Catalog open-sourced
• Snowpipe simplified pricing: 0.0037 credits/GB (Dec 2025)
• Strong data sharing and marketplace ecosystem

Google BigQuery

BigQuery has doubled down on AI-native analytics. Gemini is now embedded across all BigQuery editions at no additional cost. The Data Engineering Agent, currently in preview, automates pipeline creation from natural language descriptions.

BigQuery Data Canvas provides a visual, natural-language interface for exploring and transforming data. Committed Use Discounts (CUDs) launched at Google Cloud Next '25, giving teams a way to reduce costs on predictable workloads.

Key characteristics:

• Fully serverless with Gemini AI bundled at no extra cost
• Data Engineering Agent for natural language pipeline creation (preview)
• BigQuery Data Canvas for visual data exploration
• Committed Use Discounts for cost predictability
• Multi-region data transfer fees begin Feb 2026
• Native ML/AI integration and streaming capabilities

Amazon Redshift

Redshift has made major strides in reducing the operational burden that historically held it back. Multidimensional Data Layouts deliver up to 10x better price performance on qualifying workloads. Zero-ETL integrations now support 23 sources, including PostgreSQL, Salesforce, and DynamoDB.

The Redshift MCP Server enables natural language queries through Amazon Bedrock, bringing agentic AI capabilities to existing Redshift deployments without data movement.

Key characteristics:

• Deep AWS integration with 23 zero-ETL sources
• Multidimensional Data Layouts for 10x price-performance improvement
• Apache Iceberg write support (Nov 2025)
• Redshift MCP Server for natural language queries via Bedrock
• Cluster-based and serverless deployment options
• Mature ecosystem with broad third-party tool support

Databricks

Databricks is the fastest-growing platform in this comparison, reaching a $4.8B revenue run-rate (up 55% YoY) with a $134B valuation. Its lakehouse architecture -- combining data lake storage with warehouse-grade SQL performance -- has evolved from a differentiator to the direction the entire industry is moving.

Unity Catalog now provides full Iceberg REST Catalog API support, making Databricks a strong multi-engine hub. Automatic liquid clustering, now GA, eliminates the need for manual partitioning and sort key management.

Key characteristics:

• $4.8B revenue run-rate, fastest growth in the market (+55% YoY)
• Unity Catalog with full Iceberg REST Catalog API support
• Automatic liquid clustering GA (no manual partitioning)
• Standard tier retiring (Oct 2025 AWS/GCP, Oct 2026 Azure)
• Unified platform for data engineering, ML, and SQL analytics
• Strong open-source ecosystem (Delta Lake, MLflow, Spark)

ClickHouse Cloud

ClickHouse Cloud raised a $400M Series D at a $15B valuation in early 2026, signaling serious enterprise ambitions. The platform acquired Langfuse for LLM observability and launched a native Postgres service, broadening its appeal beyond pure analytics workloads.

ClickHouse's reimagined execution model claims 75x faster query performance for specific workload patterns. It remains the strongest option for real-time analytics and high-throughput insert scenarios.

Key characteristics:

• $400M Series D at $15B valuation (early 2026)
• Acquired Langfuse for LLM observability
• Native Postgres service for broader workload support
• 75x faster queries via reimagined execution model (specific workloads)
• Open-source core with managed cloud offering
• Best-in-class for real-time analytics and high-frequency inserts

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Architecture Comparison

Understanding architecture helps explain each platform's strengths and trade-offs.

Snowflake Architecture

Snowflake uses a hybrid shared-data architecture:

Storage Layer

• Centralized storage managed by Snowflake
• Compressed, encrypted, columnar format
• Apache Iceberg read/write support (GA Oct 2025)
• Apache Polaris Catalog for open catalog interoperability

Compute Layer (Virtual Warehouses)

• Independent compute clusters
• Scale up (larger) or out (more clusters)
• Pay only when running
• Instant suspend and resume

AI/Services Layer

• Cortex AI with GPT-5.2 (via $200M OpenAI partnership)
• Cortex Code: AI coding agent for enterprise data
• Authentication, metadata, query optimization
• Always on, included in costs

Implications:

• True separation of storage and compute
• Multiple workloads don't compete for resources
• AI capabilities embedded directly in the platform
• Iceberg support reduces lock-in on the storage layer

BigQuery Architecture

BigQuery uses a fully serverless, slot-based architecture:

Dremel Engine

• Executes queries across thousands of workers
• Automatic parallelization
• No clusters to manage

Slots and Editions

• Unit of compute capacity
• On-demand: allocated per query ($6.25/TB)
• Committed Use Discounts (CUDs): reserved capacity at lower rates
• Editions: Standard, Enterprise, Enterprise Plus

Colossus Storage

• Distributed file system
• Automatic replication and optimization
• Seamless scaling

AI Layer

• Gemini embedded at no additional cost across all editions
• Data Engineering Agent for automated pipeline creation
• BigQuery Data Canvas for visual natural-language interaction

Implications:

• No infrastructure management
• Capacity automatically scales to query needs
• AI is a built-in feature, not an add-on
• CUDs provide cost predictability for steady workloads

Redshift Architecture

Redshift uses a cluster-based architecture with a growing serverless option:

Clusters

• Collection of nodes running Redshift
• Leader node coordinates queries
• Compute nodes store data and execute

Node Types

• Dense compute (DC2): Fast SSD, lower storage
• RA3: Managed storage with compute separation (recommended)
• Multidimensional Data Layouts for up to 10x price-performance gains

Redshift Serverless

• Automatic scaling via RPUs (Redshift Processing Units)
• Pay per compute used
• No cluster management

AI/Integration Layer

• Redshift MCP Server for natural language queries via Bedrock
• 23 zero-ETL sources (PostgreSQL, Salesforce, DynamoDB, and more)
• Apache Iceberg write support (Nov 2025)

Implications:

• More control over resources with provisioned clusters
• Serverless reduces management burden significantly
• Zero-ETL eliminates data movement for common AWS sources
• MCP Server brings agentic AI to existing deployments

Databricks Architecture

Databricks uses a lakehouse architecture that unifies data lake storage with warehouse performance:

Unity Catalog

• Centralized governance across all data assets
• Full Iceberg REST Catalog API support
• Cross-engine interoperability via open standards

SQL Warehouses

• SQL-native compute for BI and analytics queries
• Serverless option with automatic scaling
• Automatic liquid clustering (GA) eliminates manual partitioning

Compute Layer

• Spark-based clusters for data engineering and ML
• Serverless compute for SQL, notebooks, and workflows
• Photon engine for accelerated SQL performance

Implications:

• Single platform for engineering, analytics, and ML
• Open formats (Delta Lake, Iceberg) reduce vendor lock-in
• Liquid clustering simplifies performance tuning
• Standard tier retirement means Enterprise is the new baseline

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Pricing Comparison

Pricing models differ significantly. Understanding the model matters more than sticker prices because usage patterns determine actual cost.

Snowflake Pricing

Compute (Credits)

• Standard: $2/credit (on-demand)
• Enterprise: $3/credit (on-demand)
• Business Critical: $4/credit (on-demand)
• Credits consumed per second of compute

Storage

• ~$23/TB/month (compressed, often 3-4x compression)
• Includes Time Travel retention

Data Ingestion

• Snowpipe simplified pricing: 0.0037 credits/GB (Dec 2025)

Cost example (approximate):

• Small warehouse, 8 hours/day: $500-1,500/month
• Medium analytics workload: $3,000-12,000/month
• Enterprise usage: $20,000+/month

Cost control:

• Suspend warehouses when idle
• Right-size virtual warehouses
• Monitor credit consumption
• Use Snowpipe simplified pricing for streaming ingestion

BigQuery Pricing

On-Demand

• $6.25 per TB queried
• Free tier: 1TB queried + 10GB storage per month
• No compute costs separately

Committed Use Discounts (CUDs)

• 1-year or 3-year commitments for reserved slots
• Launched at Google Cloud Next '25
• Better for consistent, high-volume usage

Storage

• Active storage: ~$0.02/GB/month
• Long-term (90+ days): ~$0.01/GB/month

Note: Multi-region data transfer fees begin February 2026. Factor these into cross-region deployment costs.

Cost example (approximate):

• Light usage: $0-500/month (free tier covers many small workloads)
• Moderate analytics: $2,000-8,000/month
• Heavy usage: CUDs typically deliver better unit economics

Cost control:

• Partition and cluster tables
• Preview query costs before running
• Use CUDs for predictable workloads
• Watch for new multi-region transfer fees

Redshift Pricing

Provisioned Clusters

• Hourly node pricing (RA3 recommended)
• Reserved instances for savings (1-3 year commitments)
• Multidimensional Data Layouts improve price-performance by up to 10x

Serverless

• $0.375/RPU-hour
• Minimum 8 RPU base
• Automatic scaling based on workload

Storage (RA3)

• Managed storage: ~$0.024/GB/month
• Independent of compute

Cost example (approximate):

• Serverless minimum (8 RPU, 8hr/day): ~$660/month
• Production cluster (RA3): $2,000-15,000/month
• Enterprise: $20,000+/month

Cost control:

• Use reserved instances for provisioned clusters
• Right-size clusters or use serverless
• Leverage zero-ETL to avoid separate pipeline costs
• Multidimensional Data Layouts reduce compute for sorted queries

Databricks Pricing

SQL Serverless

• $0.70/DBU-hour
• Automatic scaling, no cluster management
• Photon engine included

SQL Pro

• $0.55/DBU-hour
• Cluster-based, more control

Note: Standard tier is retiring (Oct 2025 on AWS/GCP, Oct 2026 on Azure). Enterprise tier becomes the baseline, which affects minimum pricing.

Storage

• Uses your cloud provider's object storage (S3, ADLS, GCS)
• You pay cloud storage rates directly
• No Databricks markup on storage

Cost example (approximate):

• Light SQL analytics: $500-2,000/month
• Moderate analytics + engineering: $5,000-20,000/month
• Full platform (engineering + ML + BI): $20,000+/month

Cost control:

• Use serverless SQL for variable workloads
• Liquid clustering reduces over-provisioning
• Monitor DBU consumption by workspace
• Leverage Unity Catalog to avoid duplicate data

ClickHouse Cloud Pricing

Serverless

• Pay per compute and storage consumed
• Auto-scaling based on query load
• Idle timeout reduces costs during quiet periods

Dedicated

• Reserved compute for predictable performance
• Fixed monthly pricing

Cost example (approximate):

• Development/testing: $200-500/month
• Production analytics: $1,000-10,000/month
• High-throughput real-time: $10,000+/month

Cost control:

• Use idle timeouts aggressively
• Optimize table engines and materialized views
• Leverage async inserts for high-throughput ingestion

Pricing Summary Table

Platform	Entry Point	Unit	On-Demand Rate	Free Tier
Snowflake	Standard credits	Credit	$2/credit	Trial credits
BigQuery	On-demand query	TB queried	$6.25/TB	1TB queried + 10GB storage/month
Redshift Serverless	RPU-hours	RPU-hour	$0.375/RPU-hour	2-month trial
Databricks SQL	DBU-hours	DBU-hour	$0.70/DBU-hour	14-day trial
ClickHouse Cloud	Compute + storage	Various	Usage-based	Trial credits

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AI and Agent Capabilities

AI integration is the defining battleground for 2025-2026. Every platform has moved beyond "run ML models on your data" to embedding AI agents directly into the analytics workflow.

Snowflake: Cortex AI + OpenAI Partnership

Snowflake's $200M partnership with OpenAI (Feb 2026) makes GPT-5.2 available natively through Cortex AI. This means enterprise teams can run LLM-powered queries, summaries, and classifications without data leaving Snowflake's security perimeter.

Cortex Code is Snowflake's AI coding agent, designed for enterprise data developers. It assists with writing SQL, building pipelines, and debugging data quality issues within the Snowflake environment.

Strengths: Enterprise-grade AI with data governance, multi-model support (OpenAI + others in Cortex), coding agent for developer productivity.

BigQuery: Gemini Embedded

Google embedded Gemini across all BigQuery editions at no additional cost. This is a strong value play -- you get AI capabilities without a separate line item.

The Data Engineering Agent (preview) automates pipeline creation. Describe what you need in natural language, and the agent generates the pipeline. BigQuery Data Canvas provides a visual interface for data exploration that accepts natural language inputs.

Strengths: AI included in base pricing, natural-language pipeline creation, visual data exploration, tight integration with Vertex AI for custom models.

Redshift: Bedrock via MCP Server

Amazon's approach connects Redshift to Bedrock's model catalog through the Redshift MCP Server. This enables natural language queries against your Redshift data using whatever foundation model you choose through Bedrock.

The 23 zero-ETL integrations complement this by eliminating data movement -- you can query PostgreSQL, Salesforce, or DynamoDB data directly in Redshift, then layer AI on top through Bedrock.

Strengths: Model choice via Bedrock, zero-ETL eliminates pipeline complexity, MCP Server provides a standard integration pattern for AI agents.

Databricks: Open Ecosystem

Databricks takes an open-ecosystem approach, integrating with open-source LLMs and commercial models alike. Unity Catalog governs AI model artifacts alongside data assets, providing a unified governance layer.

The platform's roots in Spark and ML give it an advantage for teams that need to both build and serve models alongside analytical queries.

Strengths: Unified governance for data and models, strong ML/training capabilities, model-agnostic approach, open-source alignment.

ClickHouse Cloud: LLM Observability

ClickHouse's acquisition of Langfuse positions it uniquely for LLM observability -- monitoring and analyzing LLM application performance at scale. If you're building AI applications that need real-time analytics on model behavior, ClickHouse Cloud is worth evaluating.

Strengths: Real-time analytics on AI/LLM workloads, Langfuse integration for observability, high-throughput insert performance for telemetry data.

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The Lakehouse Convergence

The biggest structural shift in the data warehouse market is the convergence of warehouses and data lakes into a unified "lakehouse" architecture. Apache Iceberg has emerged as the open table format that makes this possible.

Apache Iceberg: The Universal Standard

As of early 2026, every major vendor supports Apache Iceberg read and write:

Platform	Iceberg Read	Iceberg Write	Catalog Support
Snowflake	GA	GA (Oct 2025)	Apache Polaris (open-sourced)
BigQuery	GA	GA	BigLake Metastore
Redshift	GA	GA (Nov 2025)	AWS Glue Catalog
Databricks	GA	GA	Unity Catalog (Iceberg REST API)
ClickHouse	GA	Limited	External catalogs

This means you can store data in Iceberg format on your own object storage and query it from multiple engines without moving it. A Databricks engineering team can write data that a Snowflake analytics team queries. A Redshift dashboard can read from the same tables that a BigQuery ML pipeline trains on.

The Catalog Layer

The real battle has moved from storage formats to catalog layers. Catalogs manage metadata -- table definitions, schema evolution, access controls, and lineage.

Three catalogs are competing for dominance:

• Apache Polaris (open-sourced by Snowflake): Vendor-neutral Iceberg catalog
• Unity Catalog (Databricks): Full Iceberg REST Catalog API, governs data and AI models
• AWS Glue Catalog: Default for AWS-native architectures, supports Iceberg

The catalog you choose determines how easily you can run multiple query engines against the same data. Organizations investing in multi-engine strategies should evaluate catalog interoperability as a primary decision factor.

What This Means for Platform Selection

Lakehouse convergence reduces the risk of vendor lock-in on the storage layer. You can store data in Iceberg on S3, ADLS, or GCS and query it from any compliant engine. This shifts the decision from "where do I store my data" to "which query engine and AI capabilities best serve each workload."

Practically, this means:

• You can start with one platform and add others without migrating data
• Multi-engine architectures are now viable, not just theoretical
• The catalog layer becomes the control plane for your data estate
• Platform differentiation shifts to AI capabilities, governance, and developer experience

For migration strategies, see: Zero-Downtime Cloud Data Migration.

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Performance Characteristics

Performance depends heavily on workload characteristics. The 2026 improvements are mostly around automated optimization and AI-assisted tuning.

Snowflake Performance

Strengths:

• Consistent performance through resource isolation
• Fast scaling (seconds to add compute)
• Good for concurrent users
• Automatic query optimization

2026 improvements:

• Cortex Code assists with query optimization
• Iceberg table performance approaching native Snowflake tables
• Snowpipe simplified pricing reduces ingestion overhead

Considerations:

• Warehouse sizing requires experimentation
• Very large queries may need larger warehouses
• Zero-copy cloning enables fast testing

BigQuery Performance

Strengths:

• Excellent for ad-hoc queries on large datasets
• Automatic parallelization
• No tuning required
• BI Engine for sub-second responses

2026 improvements:

• Gemini-assisted query optimization at no extra cost
• Data Canvas simplifies data exploration workflows
• CUDs provide guaranteed capacity for critical workloads

Considerations:

• Slot contention possible with on-demand
• New multi-region transfer fees may affect cross-region performance strategy
• Streaming has different performance characteristics

Redshift Performance

Strengths:

• Multidimensional Data Layouts deliver up to 10x price-performance improvement
• Strong for traditional BI workloads
• Good compression and encoding
• Zero-ETL eliminates pipeline latency for 23 source types

2026 improvements:

• Multidimensional Data Layouts (biggest single performance gain)
• Iceberg write support enables lakehouse queries
• MCP Server enables AI-assisted query building

Considerations:

• Provisioned clusters still require capacity planning
• Serverless minimum of 8 RPUs sets a cost floor
• Distribution key and sort key tuning still matters for provisioned

Databricks Performance

Strengths:

• Photon engine delivers strong SQL performance
• Automatic liquid clustering eliminates manual partitioning
• Unified runtime for SQL, Python, and Scala workloads

2026 improvements:

• Automatic liquid clustering GA reduces tuning effort significantly
• Unity Catalog with Iceberg REST API enables multi-engine queries
• Standard tier retirement means all users get Enterprise optimizations

Considerations:

• SQL Serverless cold start times can be noticeable
• Complex Spark workloads still benefit from manual cluster tuning
• DBU pricing can be hard to predict for mixed workloads

ClickHouse Cloud Performance

Strengths:

• 75x faster queries for specific workload patterns (reimagined execution model)
• Best-in-class for real-time analytics and high-throughput inserts
• Columnar storage with excellent compression ratios

Considerations:

• Less suited for complex joins and ad-hoc exploration
• Requires understanding of table engines and materialized views
• Smaller ecosystem of BI tool integrations compared to the big four

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Use Case Fit

Choose Snowflake When:

• You need multi-cloud deployment across AWS, Azure, and GCP
• Data sharing with partners or customers is important
• You want embedded AI (GPT-5.2 via Cortex) without managing models
• You have variable workloads with predictable patterns
• You value ecosystem breadth and marketplace

Best for: Companies with multi-cloud strategies, organizations prioritizing ease of use, data monetization use cases, and teams wanting enterprise AI without infrastructure overhead.

Choose BigQuery When:

• You're invested in Google Cloud
• You want zero infrastructure management with AI included
• The Data Engineering Agent fits your pipeline automation goals
• You have unpredictable query patterns (on-demand pricing)
• Real-time streaming and ML integration are requirements

Best for: GCP-first organizations, companies wanting AI bundled at no extra cost, teams doing ML and advanced analytics, and organizations that value serverless simplicity.

Choose Redshift When:

• You're heavily invested in AWS
• Zero-ETL integrations with PostgreSQL, Salesforce, or DynamoDB eliminate pipeline needs
• You want deep integration with the AWS ecosystem (Bedrock, SageMaker, Glue)
• Cost optimization is critical and Multidimensional Data Layouts apply to your workload
• You have experienced data warehouse administrators

Best for: AWS-centric organizations, teams with existing PostgreSQL or DynamoDB data, traditional BI workloads, and cost-conscious enterprises leveraging reserved instances.

Choose Databricks When:

• You need a unified platform for engineering, analytics, and ML
• Data lake and lakehouse architecture is central to your strategy
• You want strong open-format support (Delta Lake, Iceberg via Unity Catalog)
• Spark expertise exists in your team
• You plan a multi-engine strategy with a single governance layer

Best for: Data-intensive organizations, ML-heavy workloads, teams standardizing on lakehouse architecture, and companies that want a single platform from ingestion to serving.

Choose ClickHouse Cloud When:

• Real-time analytics on high-volume event data is the primary use case
• You need sub-second query response on billions of rows
• LLM observability and AI application monitoring are priorities
• You have engineering resources to optimize table design
• Open-source alignment matters to your organization

Best for: Real-time dashboards, log/event analytics, LLM observability (via Langfuse), adtech and IoT workloads, and teams comfortable with a more hands-on approach.

For data architecture decisions, see: ETL vs. ELT in the Cloud.

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Decision Framework

Step 1: Assess Cloud Strategy

• Single cloud (AWS)? Redshift with zero-ETL integrations
• Single cloud (GCP)? BigQuery with Gemini AI bundled
• Single cloud (Azure)? Synapse or Databricks (both have strong Azure support)
• Multi-cloud? Snowflake provides the most flexibility
• Cloud-agnostic/lakehouse? Databricks with Unity Catalog and Iceberg

Step 2: Evaluate AI and Agent Requirements

This is new for 2026. AI capabilities now vary enough to influence platform choice:

• Need LLM integration with data governance? Snowflake Cortex AI (GPT-5.2)
• Want AI included in base pricing? BigQuery with Gemini
• Want model choice via a managed service? Redshift MCP Server with Bedrock
• Building and serving your own models? Databricks (strongest ML platform)
• Monitoring LLM applications? ClickHouse Cloud with Langfuse

Step 3: Evaluate Workload Patterns

• Predictable, consistent: Provisioned/reserved pricing (Snowflake, Redshift, BigQuery CUDs)
• Variable, spiky: Serverless options (BigQuery on-demand, Redshift Serverless, Databricks SQL Serverless)
• High concurrency: Snowflake or BigQuery handle well
• Real-time/streaming: ClickHouse Cloud or BigQuery
• Complex ETL: Consider zero-ETL (Redshift) or unified platform (Databricks)

Step 4: Consider Open Format Strategy

• Want open table formats? All vendors support Iceberg, but catalog choice matters
• Multi-engine queries? Evaluate Polaris, Unity Catalog, or Glue as your catalog layer
• Minimize lock-in? Store data in Iceberg on your own object storage

Step 5: Consider Organizational Factors

• Skills: What does your team know? SQL-heavy teams may prefer Snowflake or BigQuery. Spark/Python teams lean toward Databricks.
• Governance: What does your enterprise require? Unity Catalog and Snowflake both offer strong governance.
• Ecosystem: What tools do you already use? BI tools, orchestrators, and CI/CD pipelines all factor in.
• Budget: What's realistic and predictable? Free tiers and serverless options lower the barrier to entry.

Step 6: Proof of Concept

Before committing:

• Load representative data (including Iceberg tables if relevant)
• Run realistic queries and measure actual costs
• Test AI/agent features with your actual workflows
• Validate integration with your existing tools
• Benchmark against your current platform if migrating

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Migration Considerations

Moving data to or between warehouses requires planning. The good news: Iceberg convergence makes migration less painful than it was even a year ago.

Common Migration Challenges

Schema differences

• Data types vary between platforms
• Function syntax differs
• Stored procedure support varies

Query translation

• SQL dialects have differences
• Window functions vary
• Date handling differs

Performance tuning

• Each platform optimizes differently
• Distribution strategies vary
• Indexing approaches differ

Migration Approach

1. Assess current state

   • Document schema, data types, volumes
   • Catalog queries and workloads
   • Identify dependencies

2. Evaluate Iceberg as a bridge

   • Convert source tables to Iceberg format on object storage
   • Query from the new platform without full migration
   • Validate query results match before cutting over

3. Design target state

   • Map data types
   • Plan schema organization
   • Define performance targets

4. Migrate incrementally

   • Start with less critical data
   • Validate thoroughly
   • Run parallel systems temporarily

5. Optimize post-migration

   • Tune for new platform
   • Adjust queries as needed
   • Monitor performance and costs

For migration strategies, see: Zero-Downtime Cloud Data Migration.

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Market Context

Understanding vendor momentum helps gauge long-term platform viability.

Vendor	Revenue / Run-Rate	YoY Growth	Est. Market Share
Snowflake	$3.626B (FY2025)	+29%	~20-35%
Redshift	Not separately reported	Moderate	~15%
BigQuery	Not separately reported	Strong	~12.5%
Databricks	$4.8B run-rate	+55%	Growing fast
ClickHouse Cloud	Private ($15B valuation)	Rapid	Niche but expanding

Databricks is the fastest-growing platform by revenue. Snowflake holds the largest standalone market share. Redshift and BigQuery benefit from being bundled into broader cloud platform spending, making their true market shares harder to isolate.

The overall cloud data warehouse market is projected to grow at a 20.71% CAGR, reaching $183B by 2035. There is room for all five platforms to grow, but differentiation is increasingly driven by AI capabilities and ecosystem breadth rather than raw query performance.

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Getting Help

Choosing and implementing a cloud data warehouse is a significant decision. The platform you select will shape your data strategy for years. With lakehouse convergence reducing lock-in risk, the focus should be on matching platform strengths to your organization's workloads, AI strategy, and cloud ecosystem.

If you need help evaluating options, migrating data, or optimizing your analytics infrastructure, we can guide you through the process.

Start here: ETL and data migration services

For broader strategy: Digital strategy consulting

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FAQs

1. Which cloud data warehouse is best in 2026?

It depends on your stack and priorities. Snowflake leads in multi-cloud flexibility with GPT-5.2 via Cortex AI. BigQuery bundles Gemini at no extra cost. Redshift has the deepest AWS integration with 23 zero-ETL sources. Databricks is the fastest-growing option for lakehouse workloads. All vendors now support Apache Iceberg, so lock-in is decreasing.

2. How much does a cloud data warehouse cost in 2026?

Entry costs start around $200-500/month. Snowflake charges $2-4/credit by edition. BigQuery on-demand is $6.25/TB with 1TB free monthly. Redshift Serverless runs $0.375/RPU-hour (8 RPU minimum). Databricks SQL Serverless costs $0.70/DBU-hour. Mid-size workloads typically run $3,000-15,000/month.

3. What's the difference between Snowflake and BigQuery in 2026?

Snowflake separates compute and storage with explicit virtual warehouses across AWS, Azure, and GCP. BigQuery is fully serverless but GCP-only. Snowflake integrates GPT-5.2 through Cortex AI. BigQuery embeds Gemini at no additional cost. Both support Apache Iceberg natively.

4. Is Redshift still a good choice in 2026?

Yes, especially for AWS-centric organizations. Multidimensional Data Layouts deliver up to 10x better price performance. Zero-ETL now supports 23 sources including PostgreSQL, Salesforce, and DynamoDB. The Redshift MCP Server enables natural language queries through Bedrock.

5. What is lakehouse convergence?

Traditional data warehouses and data lakes are merging into a single architecture through open table formats like Apache Iceberg. All major vendors now support Iceberg read and write, which means you can query the same data across multiple engines without moving it. This reduces lock-in and makes multi-engine strategies practical.

6. How do AI capabilities compare across platforms?

Snowflake offers GPT-5.2 via a $200M OpenAI partnership. BigQuery bundles Gemini at no extra cost. Redshift connects to Bedrock via its MCP Server. Databricks integrates with open-source and commercial models through Unity Catalog. ClickHouse acquired Langfuse for LLM observability. AI strategy should now factor into your platform decision.