The global data warehousing market is expected to hit $7.69 billion by 2028. That shows how central these platforms have become for analytics. Yet most businesses find data across separate databases, SaaS apps, and reporting tools.

A modern data warehouse breaks those silos by centralizing data for analytics, BI, and AI. But the real challenge is choosing the right fit for your workloads, budget, and ecosystem.

Forget how vendors position their platforms as (serverless, lakehouse, or autonomous). What really matters for decision-makers is:

• Which platform delivers predictable performance and cost at scale?
• Which integrates with my existing stack and governance model?
• Which avoids long-term lock-in and provides flexibility across clouds?
  
  

This guide answers those questions directly. We’ll cover the top data warehouse tools of 2025, highlight what each does best, compare them head-to-head, and give you a checklist to decide.

Data warehouse vs database vs data lake

Data warehouses are often confused with databases and data lakes, so let’s draw a quick line.

In practice, most companies run databases for operations and a warehouse for analytics. Data lakes add value when organizations need to retain raw data or support advanced modeling.

With those distinctions clear, let’s look at the leading data warehouse tools in 2025.

Comparing 8 top data warehouse tools

Here’s a quick overview of the top 8 data warehouse tools we’ll cover

Snowflake

Snowflake is a leading cloud-based data warehouse on AWS, Azure, and Google Cloud that removes the need for manual setup, hardware, or configuration.

The multi-cluster shared architecture accelerates query performance while supporting structured, semi-structured, and unstructured data. With built-in support for AI and real-time collaboration, Snowflake enables teams to ingest, process, analyze, and model data in a unified environment.

Standout features

• Independent scaling: Storage and compute resources scale separately for flexible cost control.
• Secure data sharing: Share live data across platforms and organizations without moving or copying.
• AI + Cortex integration: Use pre-built models, LLM functions, and conversational SQL for unstructured data.
• Advanced analytics: Get native support for forecasting, anomaly detection, and spatial-temporal analysis at scale.
• Governance and catalog: Horizon Catalog and Open Catalog enable unified discovery, compliance, and secure collaboration.

Fivetran’s fully-managed Snowflake connector automates ELT by extracting detailed data from source systems, replicating it into Snowflake, and organizing it in an easy-to-navigate schema. Analysts can then query this data alongside other business-critical information. The connector also supports efficient real-time replication from Snowflake to different destinations.

It may be less practical for small teams with low concurrency or simpler database needs.

Pricing

Snowflake uses a consumption-based model. Compute is billed per credit: Standard at $2, Enterprise at $3, and Business Critical at $4. Virtual Private Snowflake requires custom pricing. Storage is charged separately at $20 per TB per month on demand, with discounts for pre-purchased capacity.

30-day free trial.

Google BigQuery

BigQuery is Google Cloud’s fully-managed data warehouse built for speed, scale, and zero maintenance. It eliminates cluster management, so teams don’t spend time on infrastructure and can move straight to analysis.

The platform handles structured and unstructured data, supports open formats like Iceberg and Delta, and scales seamlessly from gigabytes to petabytes.

With built-in governance through Dataplex, teams can catalog, secure, and share data across projects while maintaining compliance.

Standout features

• Multimodal AI and ML integration: Run ML models directly using SQL, use pre‑trained Vertex AI models, and perform multimodal analysis.
• Serverless by design: Google manages provisioning, scaling, and infrastructure automatically; you’ll never have to manage clusters.
• Enterprise-grade durability: Data is automatically replicated across zones, encrypted by default, and encoded.
• Query external data: You can query data via external tables or federated queries (Cloud Storage, other databases) without ingesting the data.
• Built-in cost controls: Use dry-run estimates, daily limits, or byte-scanned caps to keep workloads within budget.

Fivetran supports BigQuery as both a source connector and a destination, syncing data as frequently as every 5 minutes. When the destination is hosted on Google Cloud, connections stay private through Google’s own Private Access network. The setup process is guided end to end, and once connected, data types are automatically mapped into BigQuery’s native schema.

Pricing

BigQuery splits costs into storage and compute. Each month, storage is billed per GB, with automatic replication and durability included. Compute can be billed.

• On-demand, where you pay only for the bytes scanned per query,
• Via capacity reservations, where you pre-purchase “slots” of compute for steady workloads.
• Free tier covers 10 GiB of storage and 1 TiB of queries per month, allowing teams to test workloads before committing.

90-day free trial on Google Cloud

ClickHouse Cloud

ClickHouse Cloud delivers open‑source ClickHouse as a serverless, analytics‑first warehouse available on AWS, GCP, and Azure. It removes operations overhead (no manual sizing, scaling, or sharding), letting teams focus on querying.

The platform pairs decoupled compute and storage with built‑in high availability, automated backups, and multi‑AZ replication. With Terraform and API support, teams can consistently automate deployments.

Security is enterprise‑grade: always‑on encryption, activity logging, and SOC 2 Type II compliance. It’s built for real‑time analytics and machine‑scale SQL, without infrastructure drag.

Standout features

• Instant deployment: Launch a fully managed warehouse in seconds without requiring manual infrastructure planning.
• Serverless compute: Compute adjusts automatically to workload needs, preventing over-provisioning or idle resources.
• Shared object storage: Separates compute from storage; multiple compute nodes access the same data in object storage without replication.
• Extensive connector ecosystem (ClickPipes): Load and integrate data easily from multiple data sources with high throughput.
• Column-oriented database: ClickHouse is optimized for OLAP workloads and uses a columnar format that accelerates aggregation and filtering on large datasets.

Fivetran supports ClickHouse Cloud as a SaaS destination. Data types are automatically mapped to native formats, and tables use the SharedReplacingMergeTree engine for deduplication. Setup is guided, with retries on network errors to ensure reliable and consistent syncs.

Pricing

ClickHouse Cloud pricing is usage-based.

• Storage costs $25.30 per TB/month.
• Compute is billed hourly per unit:
  • Basic: $0.2181
  • Scale: $0.2985
  • Enterprise: $0.3903
• Data transfer costs $0.1152/GB for public egress.
• ClickPipes ingestion is $0.04/GB.

30-day free trial offer

Databricks

Databricks SQL Warehouse is the company’s cloud data warehouse layer, built on its Lakehouse Platform. It gives analysts a familiar SQL interface while letting them query directly using AI-powered dashboards. Because compute and storage are separate, performance scales without impacting costs.

Built for real-time analytics, it supports discovering, governing, and querying siloed systems through Lakehouse federation. It runs on lakehouse storage with ACID-backed Delta tables and supports direct connections from familiar business intelligence tools like Tableau and Power BI.

Standout features

• Photon-accelerated execution: Processes SQL queries with a compiled, vectorized engine for dramatic speed gains.
• Serverless SQL warehouses: Run queries instantly without capacity planning; Databricks handles scaling, patching, and tuning.
• Semantic metric views: Define reusable business metrics as consistent layers that maintain accuracy across dashboards.
• Built-in AI extensions: Use conversational query assistants and integrate RAG models with Mosaic AI vector search, all via SQL.
• Predictive I/O: Optimizes selective scanning by predicting data access patterns to reduce latency.

Fivetran connects with Databricks across AWS, Azure, and Google Cloud. It supports SaaS and Hybrid deployments, with Hybrid available on Enterprise or Business Critical plans. Here, data types are converted to Delta-native formats, and their weekly maintenance (VACUUM, OPTIMIZE) keeps tables efficient and reliable.

Pricing

Databricks charges by Databricks Units (DBUs), usage metered per second.

• Serverless SQL warehouses cost about $0.70 per DBU-hour in U.S. regions.
• Pro SQL compute tiers cost $0.55 per DBU-hour.
• Classic SQL compute tiers cost $0.22 per DBU-hour.
• Discounts on DBU pricing are available for committed usage.
• 14-day free trial

MotherDuck

MotherDuck is a serverless, cloud data warehouse built atop DuckDB’s analytic engine. It delivers isolated, per-user compute (Ducklings) layered over shared storage, so every analyst gets dedicated performance without infrastructure complexity. Its query planner decides whether queries run locally, in the cloud, or both to minimize data movement.

The platform adds enterprise-grade capabilities like zero-copy cloning, AI-assisted SQL editing, and integration with tools like Fivetran, dbt, and BI tools.

Analysts can query files in S3, Google Cloud Storage, Azure Blob, or Cloudflare R2 directly with SQL commands, while credentials are handled safely through environment variables or configurations.

Standout features

• Pulse compute tiers: Multiple compute sizes (Pulse, Standard, Jumbo, Mega) let teams scale from light queries to production-grade workloads.
• AI-powered SQL IDE: Inline autocomplete, AI fixes, and a modern UI help analysts write accurate queries quickly.
• Control with versioned data: Supports querying historical data through versioning and change tracking.
• Token-based authentication: Uses secure tokens to manage access, and users and organizations are granted either full database access or none.
• Analytical workload focus: MotherDuck is built for batch operations and analytical queries rather than OLTP, and streaming use cases are best supported by pairing it with external queues.

Fivetran integrates with MotherDuck by syncing data into its SaaS-based warehouse. The connector automatically applies Fivetran’s standard type transformations, covering numeric, date, and string formats. Because it is partner-built, support comes directly from MotherDuck.

Pricing

MotherDuck offers 3 plan tiers:

• Free: includes 10 GB of storage and limited compute (sufficient for prototyping or light workloads)
• Lite: $25 per org/month plus usage
• Business: $100 per org/month plus usage

All tiers include compute (per-second Duckling charges) and storage ($0.08/GB-month).

21-day free trial offer.

Azure Synapse Analytics

Azure Synapse is a unified analytics platform that combines enterprise data warehousing with big data analytics and integration. It supports serverless SQL for on-demand queries and provisioned dedicated SQL pools for scalable, high-performance workloads.

You can build data pipelines, query data in-place, and integrate with analytics tools like Power BI, within Synapse Workspace.

Synapse contains the same data integration engine as Azure Data Factory, which enables at-scale ETL pipelines without leaving the workspace.

Standout features

• Industry-leading SQL engine: Synapse SQL extends T-SQL for data warehousing, streaming, and machine learning.
• Deep Apache Spark integration: Run Spark for data prep, ETL, and ML with fast startup, autoscaling, built-in Delta Lake support, and the ability to reuse existing .NET and C# code.
• Unified SQL + Spark on your Data Lake: Define tables directly on Parquet, CSV, TSV, or JSON files. Switch between SQL and Spark engines for exploration and analytics.
• Rich data integration: Supports code-free ETL, ingestion from 90+ sources, and orchestration of Spark, SQL, and notebooks inside the workspace.
• Data Explorer runtime: Purpose-built for log and telemetry analytics with automatic free-text and semi-structured data indexing. These enable real-time log insights, anomaly detection, and IoT analytics solutions.

Fivetran supports Azure Synapse as a SaaS or Hybrid destination, though serverless Synapse is not supported. The connector uses AVRO files for loading, with automatic type mapping and no added Synapse costs for Fivetran syncs.

Pricing

• Azure Synapse Pricing Models:
  
  • Serverless SQL: $5 per TB of data processed.
  • Dedicated SQL pools: Billed hourly based on Data Warehouse Units (DWUs), starting at $1.20/hour for DWU100.
• Additional Costs (separate and pay-as-you-go):
  
  • Data pipelines
  • Apache Spark pools
  • Storage

Amazon Redshift

Amazon Redshift is a cloud service from AWS designed for large-scale analytics. It stores and processes data using a massively parallel architecture, which speeds up queries across billions of rows.

The platform runs in 2 modes: provisioned clusters for steady workloads and a serverless option that automatically adjusts compute to match demand.

Redshift supports zero-ETL integrations and can query warehouse tables and data stored in S3. That’s why it is a flexible and scalable choice for analytics across structured and semi-structured workloads.

Standout features

• Decoupled compute and storage (RA3): You pay separately for compute and managed storage, allowing each to scale independently.
• Concurrency scaling with free credits: Handle query surges by adding transient capacity; most customers earn enough credits for an hour of free scaling per day.
• Secure and isolated networking: Multi-AZ availability, VPC deployment, and end-to-end encryption ensure data safety by default.
• Efficient compressed columnar storage: Zone maps and purpose-built encodings like AZ64 for data type-specific compression and I/O reduction.
• Result caching for instant responsiveness: Repeat queries return instantly when cached, and the underlying data remains unchanged.

Fivetran integrates with Redshift provisioned and serverless warehouses, giving teams flexibility regardless of how they run workloads. Connections are quick to set up, and SaaS and Hybrid deployment models are supported. Hybrid is reserved for Enterprise or Business-Critical plans.

Pricing

• Redshift Provisioned Clusters:
  
  • Starts at approximately $0.543/hour.
  • Billed hourly or with reserved nodes for cost savings.
• Redshift Serverless:
  
  • Charges $1.50/hour or per compute unit consumed.
  • Billed only during active use; idle time is not charged.
• Managed Storage:
  
  • Priced separately (e.g., about $0.024/GB-month).
• Redshift Spectrum (S3 queries):
  
  • Billed per TB scanned.

Teradata Vantage

Teradata Vantage is an analytics platform that runs in the cloud or on-premises and is powered by a massively parallel relational engine. It supports hybrid deployments across AWS, Azure, and private cloud environments.

The platform enables unified analytics (from SQL queries to AI and ML workflows) over structured, semi-structured, and unstructured data.

Vantage simplifies operations with consistent software across environments, smooth licensing portability, and multi-cloud flexibility, helping teams modernize without rearchitecting or retraining users.

Standout features

• Consistent hybrid cloud architecture: Run the same Vantage software on-prem, public clouds, or hybrid setups without changes.
• Consumption-based pricing model: Pay only for compute and storage used during successful operations, with system scaling handled automatically.
• AI and ML built-in support: Includes ClearScape Analytics for advanced modeling and AI-driven insights.
• Connected lakehouse support: Integrates seamlessly with data lake and lakehouse architectures, combining structured warehousing with open-format analytics.
• Industry-specific data models: Vantage offers prebuilt industry data models, such as standardized, modular metadata frameworks across sectors like retail and finance. This accelerates warehouse deployment time.

Fivetran integrates directly with Teradata Vantage, whether you run it in the cloud or hybrid environments. Fivetran automatically maps data types to Vantage standards and natively handles JSON and semi-structured inputs. This reduces manual engineering and ensures data arrives ready for reporting and analysis.

Pricing

• VantageCloud Lake:

• Compute: Starts at $4.80 per hour.
• Storage: Approximately $1,445 per TB annually for block storage; around $276 per TB annually for object storage.

• VantageCloud Lake+:
  
  • Compute: Starts at roughly $7.20 per hour.
• General Terms:
  
  • Costs based on a 3-year agreement, billed annually.
  • Initial setup fees may apply depending on terms.

How to choose a data warehouse tool?

To choose the right data warehouse tool for your team, aligning the design with your data, workloads, and operational capacity is essential. Here are the main factors you must weigh before committing.

Identify the data you need to store

First, be clear about what data type you’ll be using frequently. A relational warehouse will fit if you’re working with a massive spreadsheet. If you rely on semi-structured formats like books, social media posts, or emails, you’ll want a non-relational database.

Consider whether a lake or a data lakehouse is more appropriate for unstructured sources.

Deciding early also helps you choose between ETL (transform before load) and ELT (transform after load). ELT works best if your warehouse has the processing muscle to handle transformations on demand.

Plan for the scale of your workloads and match performance

Think about how much data you’ll store today and how much growth you expect. Most cloud warehouses easily cover terabytes, but beyond that, scaling models differ. Some (like Redshift) require manual node additions. Others (like Snowflake) spin up and down automatically.

If you expect spikes or unpredictable workloads, auto-scaling works better. Or if your workloads are steady, reserved compute will give you more predictable costs.

Balance the need for immediacy against the cost of always-on performance.

Decide how much maintenance you can handle

Ask how much time your team can dedicate to upkeep. Some warehouses self-optimize, handling tasks like vacuuming, indexing, and partitioning in the background.

Others give administrators manual control, which allows fine-tuned performance but requires constant oversight.

Budget beyond storage and compute

Pricing models vary. Serverless SQL often charges per terabyte scanned, while dedicated pools are billed by the hour or DWUs (data warehouse units). If you run constant queries, focus on compute efficiency.

If you store more than you query, go for low storage costs. Always model costs based on your actual usage, not just vendor promises.

Cloud warehouses typically avoid large upfront hardware costs, but ongoing bills can scale quickly with usage.

Check ecosystem fit and deployment model

Your warehouse doesn’t live in isolation. It powers BI dashboards, feeds ML models, and connects to ingestion tools. Pick a warehouse that integrates natively with your stack to avoid building custom pipelines. A strong ecosystem also means faster onboarding and easier scaling as your team grows.

An additional decision is whether to build on-premises or go cloud-native. On-premise solutions give full control but demand heavy infrastructure investment and ongoing maintenance. Cloud warehouses provide elasticity and pay-as-you-go pricing, which usually align better with modern analytics teams.

Where Fivetran fits in the stack

A warehouse is only helpful if its data is complete, accurate, and current. With Fivetran, instead of building pipelines manually, your teams will connect the tool to the applications, databases, and files they already use.

With 700+ prebuilt connectors, coverage extends across the systems most businesses rely on.

• Its automated schema drift handling absorbs changes at the source without breaking dashboards. Self-healing pipelines adjust in the background, so analysts always work with in-sync data.
• Data centralization is built in. Fivetran pulls from hundreds of sources and consolidates them into a single warehouse. That removes the need for custom integrations and reduces the engineering overhead of maintaining multiple pipelines.
• Finally, ELT pipeline processing ensures efficiency. Raw data is replicated downstream first, and transformations happen in the warehouse.

That’s where Fivetran fits in the stack: right between your sources and your warehouse, quietly moving data at scale.

Final thoughts

Whether you lean toward Snowflake’s X independent scaling, BigQuery’s serverless architecture, or Redshift’s deep AWS integration, the best warehouse is the one that delivers fast, trusted insights without hidden costs.

Once you’ve selected the warehouse, the next step is to power it with reliable, automated data pipelines.

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FAQs

What is an ETL tool in data warehousing?

An ETL tool automates data integration by extracting data from multiple sources, transforming it via business rules or cleanup, and loading it into a data warehouse or analytics-ready system.

It turns raw data into structured, consistent sets ready for analysis.

Is Databricks a data warehouse?

Yes. Databricks offers Databricks SQL, a high-performance, cost-effective data warehouse built on the lakehouse architecture. It brings traditional warehouse capabilities directly to a data lake.

What is OLAP in a data warehouse?

OLAP (Online Analytical Processing) is the layer in a data warehouse that enables fast, multidimensional analysis of historical data.

It organizes information into dimensions (e.g., time, product, region) so users can slice, dice, and drill down for insights. OLAP comes in 3 forms:

• Multidimensional
• Relational
• Hybrid.

What are the 4 components of a data warehouse?

A data warehouse typically consists of 4 core components:

1. A central database that stores the data
2. ETL or data integration tools that bring information in from various sources
3. Metadata that provides context and definitions
4. Access tools that allow users to query and generate reports

What are the types of data warehouses?

Data warehouses generally fall into a few categories.

• Enterprise data warehouses (EDW): Serve as an organization’s central data and analytics hub.
• Operational data stores (ODS): Support near real-time reporting on current activities.
• Data mart: Narrow the scope to a single business area or department.
• Big data and cloud data warehouses: Support and scale large, diverse datasets.