- Data warehouse platforms at a glance
- Eleven data warehouse tools to look at
- Warehouse, lake or lakehouse?
- Set retention rules before you load everything
- Questions to ask a data warehouse vendor
- Signs you've outgrown spreadsheet reporting
- My plain advice on choosing a data warehouse
- Frequently asked questions
The best data warehouse tools store data from all your business systems in one place, structured for fast reporting and analysis: companies already on a big cloud often use that cloud's own warehouse, while specialist and open source options suit very fast queries, smaller teams or data kept on-premises. Eleven platforms follow in no particular order.
Sales numbers live in the CRM, orders in the shop, invoices in the accounts package and ad results in four different dashboards. Getting one honest answer to a simple question, such as which customers are most valuable, means exporting and stitching spreadsheets every month.
A data warehouse is where those numbers finally meet. The eleven platforms below are listed alphabetically and in no particular order, from the big clouds to smaller specialists, and every description comes from the vendor's own site, read this week. If you're still working out which reports you need, my roundup of data analytics tools is a good starting point.
Data warehouse platforms at a glance
In the free plan column, Yes means the tool's own site names a free plan, a free edition or an open source version. Trial only means a time-limited trial. Not stated means the pages I read named neither, even where there's a button inviting you to try it or book a demo.
| Tool | Type | Good for | Free plan |
|---|---|---|---|
| Amazon Redshift | Cloud data warehouse on AWS | Teams already on AWS | Not stated |
| BigQuery | Serverless data warehouse and AI data platform | Teams on Google Cloud | Not stated |
| ClickHouse | Open source analytical database | Very fast queries on large event data | Yes |
| Databricks SQL | Lakehouse data warehouse | Data and AI work on one platform | Yes |
| Dremio | Lakehouse query engine | Querying data lakes without copying data | Yes |
| Exasol | Analytics database | Speeding up existing BI dashboards | Yes |
| Firebolt | Analytical database for engineers | Low-latency analytics inside applications | Not stated |
| Microsoft Fabric | End-to-end data analytics platform | Microsoft-centric organisations | Trial only |
| MotherDuck | Cloud data warehouse built on DuckDB | Small teams wanting a lightweight warehouse | Not stated |
| Snowflake | AI data cloud | Analytics, data engineering and AI in one | Not stated |
| Yellowbrick | SQL data platform | Running the warehouse anywhere, even on-premises | Trial only |
Eleven data warehouse tools to look at
Amazon Redshift
Best for: Companies already storing data on Amazon Web Services that want a managed cloud data warehouse in the same account.
BigQuery
Best for: Businesses on Google Cloud that want a serverless data warehouse with machine learning built in and no servers to manage.
ClickHouse
Best for: Engineering teams that want very fast analytical queries on large data sets from an open source database, self-hosted or in the cloud.
Databricks SQL
Best for: Data teams that want their warehouse and AI work on one lakehouse platform, with a free edition to learn on.
Dremio
Best for: Organisations with data in open table formats like Apache Iceberg that want lakehouse analytics without moving everything into a warehouse.
Exasol
Best for: Analysts and data teams that want a fast analytics database, with a free personal edition for trying it on their own data.
Firebolt
Best for: Engineers building data-heavy applications who want a high-speed analytical database that speaks a Postgres SQL dialect.
Microsoft Fabric
Best for: Microsoft-centric organisations that want data warehousing, data engineering and reporting together in one analytics platform.
MotherDuck
Best for: Small data teams and analysts who like DuckDB and want a simple, lightweight cloud data warehouse built around it.
Snowflake
Best for: Enterprises that want a widely adopted cloud data platform for analytics, data engineering and AI work in one place.
Yellowbrick
Best for: Organisations that need a secure SQL data platform they can run in their own data centre, a private cloud or a public cloud.
The two biggest names are set out in my Databricks and Snowflake comparison.
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Warehouse, lake or lakehouse?
A data warehouse stores tidy, structured data ready for reporting. A data lake stores raw files of every kind in bulk. A lakehouse tries to give you both, running warehouse-style queries over data held in open file formats.
For most small and mid-sized businesses, a warehouse is the simplest route to reliable reports. Lakehouse platforms make more sense when you've got huge volumes, lots of raw files or data science work alongside the reporting.
Set retention rules before you load everything
The ICO sets out UK GDPR Article 5(1)(e): personal data shall be kept in a form which permits identification of data subjects for no longer than is necessary for the purposes for which it's processed.
Warehouses make it very easy to keep everything forever, which is exactly what this principle warns against. Decide how long you need customer-level detail, set deletion or anonymisation rules, and keep aggregated numbers for the long-term trends instead.
Clean inputs matter, so see master data management software.
Questions to ask a data warehouse vendor
- Which clouds do you run on, and can we keep data in a region we choose?
- How do you bill for storage and for queries, and how can we keep that predictable?
- Which loading tools and BI tools connect to you out of the box?
- How quickly do typical reporting queries run on data our size?
- How do we delete or anonymise personal data when we need to?
- Can we take our data and move elsewhere easily?
Signs you've outgrown spreadsheet reporting
- Monthly reports take days. Most of it's copying and pasting.
- Two reports disagree. Nobody knows which is right.
- Files are too big to open. Spreadsheets keep crashing.
- AI projects stall. The data isn't in one place.
Do you make data warehouse software? Tool makers can ask to be considered for a featured place on this list.
My plain advice on choosing a data warehouse
Start with the questions you want answered, not the technology. If three reports drive most of your decisions, choose the warehouse that makes those three easy and quick to keep fresh, and expand from there.
If you already live on one of the big clouds, its warehouse is usually the least effort. Look at the specialists when speed, on-premises hosting or a lighter set-up matters more. Once the data's in, my dashboard build checklist will help you turn it into reports people read.
Frequently asked questions
What is a data warehouse?
It's a central store for structured data from many business systems, organised for fast reporting and analysis.
Do small businesses need a data warehouse?
Not always. Once you're stitching exports from several systems every month, a small warehouse usually saves time and mistakes.
What's the difference between a data warehouse and a database?
An ordinary database runs an app day to day. A warehouse brings data from many apps together for analysis.
What is a lakehouse?
A lakehouse runs warehouse-style queries over data held in open file formats in a data lake, aiming to combine both approaches.
Is there a free data warehouse?
Some analytical databases are open source, and some platforms offer free or personal editions for learning and small projects.
How long should I keep data in a warehouse?
Only as long as you need it. Under UK GDPR, personal data shouldn't be kept in an identifiable form longer than necessary.
