Data warehouse examples in 2026: 8 platforms and real use cases

Data warehouse examples

Data warehouse examples come in two kinds: the platforms companies run their warehouse on, and the ways they use it. This post covers both, starting with the platforms. The most common today are cloud data warehouse systems you rent instead of servers you run, and these are the eight you are most likely to meet:

1. Snowflake: a cloud data warehouse that runs on AWS, Azure and Google Cloud. Used by companies of every size that want one warehouse independent of their cloud provider.
2. Google BigQuery: Google Cloud's serverless warehouse, billed per query or per reserved capacity. Popular with teams already on Google Cloud, Google Analytics 4 or Google Ads.
3. Databricks: a lakehouse platform that combines warehouse SQL with data lake storage. Used by teams that run data engineering, analytics and machine learning on the same data.
4. Amazon Redshift: the AWS data warehouse, available as provisioned clusters or Redshift Serverless. Used mostly by companies whose infrastructure already lives on AWS.
5. Microsoft Fabric and Azure Synapse Analytics: Microsoft's analytics platforms, with Fabric as the newer one. Used by organizations built on Azure, SQL Server and Power BI.
6. ClickHouse Cloud: the managed version of the open source ClickHouse database. Used for real time dashboards and customer facing analytics where queries must return in milliseconds.
7. Firebolt: a warehouse designed for low latency, high concurrency queries. Used by engineering teams that serve analytics inside their own products.
8. IBM Db2 Warehouse: IBM's warehouse for cloud and on premises deployment. Used by large enterprises with existing IBM estates and strict data residency needs.

Below you will find a side by side comparison, examples of how each industry uses a data warehouse, what each platform is good at, and how to pick between them.

Data warehouse comparison

For a deeper look at how these bills add up, see what to be aware of when it comes to data warehouse pricing. For head to head comparisons, read BigQuery vs Snowflake, Snowflake vs Redshift and PostgreSQL vs BigQuery.

Data warehouse examples by industry

The platform matters less than what goes into it. These are typical examples of how companies in different industries use a data warehouse, with the sources they combine and the questions it answers.

Retail and e-commerce

• Sources: Shopify or another store platform, point of sale, Stripe or Adyen, Google Ads, Meta Ads, Klaviyo, inventory and shipping systems.
• Typical tables: orders, order lines, customers, products, inventory snapshots, ad spend by day.
• Questions it answers: Which channels bring customers who come back? What is the margin per product after discounts, returns and shipping? Which products will run out of stock in the next two weeks?

SaaS and software

• Sources: the product database, product analytics events, Stripe or Chargebee, HubSpot or Salesforce, Intercom or Zendesk.
• Typical tables: accounts, subscriptions, invoices, feature usage by day, support tickets.
• Questions it answers: What are MRR, churn and net revenue retention this month? Which features do accounts use in the weeks before they upgrade, or before they cancel? Which trials should sales call today?

Financial services and fintech

• Sources: core banking or ledger systems, card and payment processors, KYC providers, the CRM.
• Typical tables: transactions, accounts, balances by day, customers, risk scores.
• Questions it answers: Which transactions look unusual for this customer? How do loan books perform by cohort? What do the regulatory reports need, and can we rebuild them for any past date?

Healthcare

• Sources: electronic health records, scheduling, lab systems, billing and claims.
• Typical tables: patients, encounters, diagnoses, procedures, claims, appointments.
• Questions it answers: Which patients are due for a follow up? Where are waiting times growing? Which treatments lead to fewer readmissions? Patient data needs strict access control, so the warehouse's row level security and masking matter here.

Manufacturing and logistics

• Sources: ERP systems such as SAP, NetSuite or Business Central, warehouse management, machine sensors, carriers.
• Typical tables: purchase orders, production runs, stock movements, shipments, sensor readings.
• Questions it answers: Which suppliers deliver late, and what does it cost? Which machines show early signs of failure? What is the true cost per unit by plant?

Marketing agencies

• Sources: Google Ads, Meta Ads, LinkedIn Ads, TikTok Ads, Google Analytics 4 and Search Console for every client.
• Typical tables: spend, impressions, clicks and conversions by client, channel, campaign and day.
• Questions it answers: What is each client's return on ad spend across all channels? Which campaigns should get more budget this week? Client reporting runs from one set of tables instead of one export per platform.

What is a data warehouse?

A data warehouse is a database built for analysis. It collects data from many systems, such as your CRM, ad platforms, payment provider and product database, and stores it in one place, structured so that you can query years of history quickly with SQL. If you want the longer explanation, read what is a data warehouse.

Components of a data warehouse

Every data warehouse, whichever platform it runs on, is built from the same parts:

1. Data sources: the systems the data comes from, such as the CRM, ad platforms, payment provider, ERP and product database.
2. Ingestion: an ELT or ETL tool that copies data from each source into the warehouse on a schedule, and keeps it up to date.
3. Storage and compute: the warehouse platform itself, which stores tables in a columnar format and runs SQL against them.
4. Transformation: SQL models that clean raw data and join it into business tables such as orders, customers and revenue.
5. Metadata and governance: table descriptions, lineage, access rules and data quality checks, so people know what each table means and who may see it.
6. Access: BI tools, SQL editors, spreadsheets, AI agents and reverse ETL syncs that put the results back into business tools.

Data warehouse vs database vs data lake

A regular application database is built to read and write single records fast, like saving an order. A data warehouse database is built to scan millions of rows at once, like summing revenue by month and channel. That is why companies keep both: the database runs the product, the warehouse answers questions about the business.

A data lake is a third option: cheap file storage, such as Amazon S3, that holds raw data in any format, including logs, images and JSON. It is flexible but needs more work before anyone can query it. Lakehouse platforms like Databricks, and open table formats like Apache Iceberg, bring warehouse style tables to data lake storage.

An example of a data warehouse

Most data warehouses are organized as a star schema: one fact table of events surrounded by dimension tables that describe them. An online shop's warehouse might look like this:

• fct_orders: one row per order line, with quantity, revenue, discount and keys to the dimensions
• dim_customers: one row per customer, with country, signup date and acquisition channel
• dim_products: one row per product, with category, brand and cost price
• dim_date: one row per calendar day, with week, month, quarter and holidays

Data arrives from Shopify, Stripe, Google Ads and the CRM, is cleaned in layers (often called medallion layers), and ends up in tables like these. A question such as "revenue by acquisition channel last quarter" then becomes one query:

1select
2    c.acquisition_channel
3  , sum(o.revenue) as revenue
4from
5    fct_orders o
6    join dim_customers c on c.customer_id = o.customer_id
7    join dim_date d on d.date = o.order_date
8where
9    d.quarter = '2026-Q3'
10group by
11    1
12order by
13    2 desc;

To keep history when a customer moves country or changes plan, dimension tables often use slowly changing dimensions.

Sample data warehouses you can try for free

If you want to see a real data warehouse before choosing one, several of the examples above ship with sample data:

• Google BigQuery has hundreds of public datasets, such as Google Analytics sample data and Stack Overflow, that you can query within the free tier.
• Snowflake includes the SNOWFLAKE_SAMPLE_DATA database with the TPC-H benchmark tables in every trial account.
• Databricks includes a samples catalog with TPC-H and New York taxi trip data.
• ClickHouse runs a public playground with large example datasets you can query in the browser.

Common data warehouse use cases

• Reporting and BI: one source for revenue, marketing and product dashboards in tools like Looker Studio, Power BI or Metabase.
• Marketing analytics: combining ad spend from Google, Meta and LinkedIn with CRM and revenue data to measure return on ad spend.
• Finance: reconciling Stripe, accounting and bank data for monthly reporting.
• Customer data: building one view of each customer and sending it back to tools like HubSpot or Salesforce.
• AI and agents: giving language models and AI agents governed, structured company data to work from.

Types of data warehouse

The three classic types of data warehouse are:

1. Enterprise data warehouse (EDW): one central warehouse for the whole organization. All eight examples above are typically used this way.
2. Operational data store (ODS): a store refreshed close to real time for operational reporting, often with less history.
3. Data mart: a smaller, subject specific slice of the warehouse, for example only marketing or only finance data.

Separately, warehouses are either on premises, running on hardware you own, or cloud based, running as a managed service. Cloud data warehouses have become the default because they scale storage and compute on demand, need no hardware, and charge for what you use. Every example in this post is available as a cloud service.

The 8 data warehouses in detail

1. Snowflake

Snowflake is one of the most widely used cloud data warehouses. It runs on AWS, Azure and Google Cloud, so you can choose a region and provider without changing tools.

Storage and compute are separate: you size virtual warehouses for each workload and pause them when they are idle, so a heavy transformation job does not slow down your dashboards. Snowflake also supports semi structured data such as JSON, open table formats like Apache Iceberg, and built in AI functions through Snowflake Cortex. It is a strong default for teams that want a warehouse that is easy to run and independent of a single cloud.

2. Google BigQuery

Google BigQuery is Google Cloud's serverless data warehouse. There are no clusters to size: you load data and query it, and Google allocates the compute.

The free tier includes 10 GB of storage and 1 TB of queries each month, which makes it an easy place to start. BigQuery has native exports from Google Analytics 4 and Google Ads, machine learning in SQL through BigQuery ML, and Gemini built into the console. It is a natural choice if you are building a modern data stack on Google Cloud or work heavily with marketing data.

3. Databricks

Databricks popularized the lakehouse: data lives in open Delta Lake or Iceberg tables in your own cloud storage, and you query it with SQL warehouses or process it with Spark and Python.

Unity Catalog handles governance across tables, files and machine learning models. Databricks fits teams where data engineers, analysts and data scientists share the same data, and where machine learning is as important as reporting.

4. Amazon Redshift

Amazon Redshift was one of the first cloud data warehouses, launched by AWS in 2012. It now separates storage and compute through RA3 nodes and offers Redshift Serverless, which scales capacity automatically and bills only while queries run.

Redshift integrates closely with the rest of AWS: it can query data directly in S3 with Redshift Spectrum and share data across accounts. It is the usual pick for companies whose infrastructure already runs on AWS.

5. Microsoft Fabric and Azure Synapse Analytics

Microsoft Fabric is Microsoft's unified analytics platform, combining a data warehouse, lakehouse, data pipelines and Power BI on shared OneLake storage. Azure Synapse Analytics, the earlier service that grew out of Azure SQL Data Warehouse, is still widely used, and Microsoft points new projects towards Fabric.

Both use T-SQL, so they feel familiar to teams coming from SQL Server. They are the natural choice for organizations standardized on Azure and Power BI.

6. ClickHouse Cloud

ClickHouse Cloud is the managed service from the company behind ClickHouse, the open source columnar database. It is built for speed: aggregations over billions of rows return in milliseconds.

That makes it popular for real time dashboards, observability data and analytics shown directly to customers inside a product. The open source core means you can also run ClickHouse yourself.

7. Firebolt

Firebolt is a cloud data warehouse focused on low latency at high concurrency, meaning many users or applications querying at the same time with fast responses.

It separates storage and compute, supports semi structured data, and uses aggressive indexing to keep query times low. Firebolt suits engineering teams building data heavy applications more than traditional BI teams.

8. IBM Db2 Warehouse

IBM Db2 Warehouse is IBM's analytics warehouse, available as a managed cloud service and as software you run on premises or in your own containers.

It is known for reliability, workload management and in database machine learning. Db2 Warehouse fits large enterprises that already run IBM systems or need to keep data on premises. For a small team starting out, the newer cloud warehouses above are usually simpler and cheaper. Other enterprise examples in the same space include Oracle Autonomous Data Warehouse, SAP Datasphere and Teradata VantageCloud.

How to choose a data warehouse

The right data warehouse depends more on your existing stack than on benchmarks:

• Your cloud provider: on Google Cloud, start with BigQuery; on AWS, Redshift or Snowflake; on Azure, Fabric or Snowflake.
• Your workloads: mostly BI and reporting points to Snowflake or BigQuery; heavy machine learning to Databricks; real time product analytics to ClickHouse or Firebolt.
• Your team: serverless options like BigQuery and Snowflake need the least administration.
• Your budget: compare pricing models against your real query patterns, not list prices.

For most small and mid sized companies, Snowflake or BigQuery is the simplest place to start, and both can grow with you.

Getting data into your warehouse

A warehouse is only as useful as the data in it. Weld syncs data from more than 300 sources into Snowflake, BigQuery, Databricks, Redshift, Azure Synapse and ClickHouse, then lets you model it with SQL and sync it back to your business tools. You can try it free.

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