8 best data warehouse tools: Features and costs

Data warehouse tools centralize, store, and query data from multiple systems, making it easier to analyze information across your organization. For example, combining sales, billing, and product data helps you answer questions like how much revenue each customer generates. Still, realizing these data warehouse benefits depends on choosing a platform your team can operate and afford.

This guide compares eight data warehouse solutions by their capabilities, pricing models, and fit for different workloads. Use it to narrow down your options, then test each shortlisted platform with your own queries before committing. Performance can vary significantly depending on the workload, from scheduled reports to hundreds of simultaneous dashboard requests.

What are data warehouse tools?

Data warehouse tools are software solutions that collect, centralize, and analyze business data from multiple sources. Data warehouse software provides a central environment for storing this data and running analytical queries to identify trends, measure performance, and support business decisions.

Unlike an operational database like Postgres, which handles application transactions, a warehouse is designed to analyze data across many records, often using column-oriented storage. A data lake stores files in varied formats, while a data lakehouse adds table management and analytical capabilities to that storage.

What are the best data warehouse tools?

Snowflake, Google BigQuery, Amazon Redshift, and Databricks SQL are established options, alongside Microsoft Fabric Data Warehouse, ClickHouse Cloud, MotherDuck, and Teradata VantageCloud.

Keep in mind that the following list isn’t a ranking; each platform makes different choices about compute, storage, and data workflows.

Snowflake

Snowflake is a managed data platform that helps you provision analytical compute separately from stored data.

• Independent warehouses: Allocate compute to different teams or workloads without duplicating their underlying tables.
• Data sharing: Give other Snowflake users access to selected data without building a separate export pipeline.

Snowflake pricing combines compute credits with storage charges, with rates varying by edition, cloud, and region. It’s a fit for teams that need isolated compute resources for different workloads and controlled data sharing.

Fivetran loads data into Snowflake and also offers a source connector for replicating data out of Snowflake.

Google BigQuery

BigQuery is Google Cloud’s serverless analytical warehouse, which removes the need to provision and manage warehouse clusters.

• Managed execution: Submit SQL queries while Google manages the infrastructure that runs them.
• Capacity options: Use shared on-demand resources or reserved capacity to match your query workload.

BigQuery pricing separates storage from compute, with compute charged by data processed or capacity consumed. It’s a fit for Google Cloud teams seeking managed infrastructure with different ways to budget query costs.

Fivetran loads data into BigQuery, while its BigQuery source connector supports replication out.

Amazon Redshift

Amazon Redshift provides analytical SQL on Amazon Web Services (AWS) through provisioned clusters or serverless compute.

• S3 access: Analyze supported data stored in Amazon S3 alongside data in warehouse tables.
• Serverless scaling: Adjust compute automatically as demand changes, with capacity limits to help control spending.

Redshift pricing uses node-hours for provisioned compute or RPU hours for serverless, with managed storage billed separately. It’s a fit for teams keeping their analytical stack within AWS.

Fivetran’s Redshift destination integration loads source data into warehouse tables, where you can combine it with your existing AWS data sets.

Databricks SQL

Databricks SQL supplies warehouse capabilities within the Databricks lakehouse platform.

• Photon engine: Execute SQL using a vectorized engine designed for analytical queries.
• Serverless SQL warehouses: Run business intelligence (BI) workloads without managing the underlying compute infrastructure.

Databricks pricing measures compute in Databricks Units, with rates depending on workload and deployment. Include storage and any separately billed cloud infrastructure when comparing costs. It’s a fit for teams combining SQL analytics with data engineering and machine learning.

Fivetran loads data into Databricks for downstream queries and transformations on lakehouse tables.

Microsoft Fabric Data Warehouse

Microsoft Fabric Data Warehouse offers SQL warehousing on Fabric’s shared OneLake storage.

• Shared Delta tables: Use warehouse data across compatible Fabric analytical engines.
• Migration assistance: Microsoft’s migration assistant helps move workloads from Synapse dedicated SQL pools.

Fabric pricing combines shared compute capacity with OneLake storage charges. It’s a fit for teams already using Microsoft’s analytics environment, including Power BI.

Fivetran’s Managed Data Lake writes managed tables to Azure Data Lake Storage. Its OneLake catalog integration publishes Delta Lake metadata so you can query those tables through a SQL analytics endpoint. Consider this lake-based access route when evaluating how Fabric fits into your data architecture.

ClickHouse Cloud

ClickHouse Cloud is a managed analytical database suited to querying large volumes of event data.

• ‍Columnar execution: Scan the columns needed for aggregations over logs, telemetry, or product events.

• Separate scaling: Adjust compute independently of stored data, with autoscaling controls for supported service tiers.

ClickHouse Cloud pricing accounts for compute, storage, data transfer, and applicable ingestion charges. It’s a fit for teams serving interactive analytics over high-volume event data sets, provided their query patterns perform well in testing.

Fivetran loads source data through the ClickHouse Cloud destination, a partner-built integration supported by ClickHouse.

MotherDuck

MotherDuck brings the DuckDB analytical engine into a managed cloud warehouse.

• Isolated compute: Give users separate compute instances, so workloads can scale independently.
• Local and cloud execution: Query across local data and cloud tables within the same workflow.

MotherDuck pricing includes a Lite plan with usage allowances, a Business platform fee plus compute and storage, and custom Enterprise pricing. It’s a fit for teams that want to extend DuckDB workflows into shared cloud analytics.

The MotherDuck destination integration is built and maintained by MotherDuck, which also provides support for the integration.

Teradata VantageCloud

Teradata VantageCloud combines enterprise analytical workloads with cloud deployment options.

• Workload isolation: VantageCloud Lake uses separate compute clusters to keep different workloads from competing for the same compute.
• Advanced analytics: ClearScape Analytics adds analytical functions within the platform.

Its VantageCloud Lake pricing guide describes compute consumption units and per-terabyte storage charges, with committed and on-demand options. It’s a fit for enterprises evaluating complex analytical workloads or extending existing Teradata deployments.

Fivetran offers a partner-built Teradata destination, currently in private preview. Confirm availability and deployment support before planning production data ingestion.

How to choose a data warehouse platform

The differences between data warehouse platforms become clearer when you evaluate them against your team's actual workloads and requirements. Consider these five criteria when comparing your options:

• Data types: Most cloud warehouses handle structured and semi-structured data like JSON, while lakehouses also accommodate unstructured files. Consider your likely use cases and intended queries.
• Scale and workload performance: Test representative joins, aggregations, and concurrent dashboard requests at expected data volumes. Measure query completion times and additional compute consumption during peak periods.
• Maintenance capacity: Decide who will manage access, monitor costs, and tune queries after deployment. Serverless infrastructure reduces provisioning work, but your team still owns data models and workload design.
• Total costs: Include storage, compute, ingestion, data transfer, and administrative time in the estimate. Compare a typical month with a peak reporting period to ensure occasional spikes in usage aren’t obscured by average costs.
• Ecosystem and deployment: Check compatibility with your cloud environment, BI tools, required regions, and security controls. Existing skills and integrations matter because replacing them adds migration work beyond simply moving data.

Load data into any data warehouse with Fivetran

Choosing a data warehouse determines where your team will store and analyze data — but you also need reliable pipelines to move data from source systems into the warehouse and keep it current as applications and databases change.

Fivetran offers more than 700 fully managed connectors that load data into major cloud warehouses and automatically handle supported schema changes. Once the data arrives, Fivetran Transformations runs dbt Core models inside supported destinations to prepare it for analysis. Following the merger of Fivetran and dbt Labs, the combined company brings data ingestion and transformation capabilities together.

As your data pipelines scale, Fivetran’s pipeline management capabilities let you use its REST API, Terraform, and Airflow to configure and orchestrate workflows. This helps standardize pipeline management across supported data warehouses, giving your team a consistent approach to data integration and orchestration.

Start a free trial with Fivetran today.

FAQ

What are ETL tools in a data warehouse?

Extract, transform, and load (ETL) tools in data warehouse architectures extract data from source systems, transform it into a consistent and usable format, and load it into a data warehouse for analysis and reporting. Modern cloud-based architectures often use extract, load, and transform (ELT) instead, loading data first and transforming it within the warehouse itself.

What are data warehouse automation tools?

Data warehouse automation tools automate tasks like loading, modeling, and maintenance. Within that workflow, data integration tools like Fivetran automate loading, while dbt Core automates transformations inside supported warehouses.

Is Databricks a data warehouse?

Databricks SQL provides data warehouse capabilities within Databricks’ broader lakehouse platform. You can use it for SQL analytics and BI, while other teams use the same platform for data engineering or machine learning.

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