Learn to build a data warehouse that scales with your data.
Imagine all your data living under one roof: easily accessible, neatly organized, and ready to power decision-making. That’s the magic of a data warehouse. It pulls together information from multiple sources into a centralized repository, making it easier to manage, access, and analyze.
Did you know Malaysia’s Department of Statistics’s enterprise data warehouse has historical data from 1974? That’s over four decades of storage. And yes, a reliable data warehouse really can hold data for that long.
In this blog, we’ll dive into everything you need to build a solid data warehouse. We’ll examine different approaches, weigh the pros and cons, and share practical steps to break ground on your new warehouse (no hard hats required). Let’s dive in!
Think of data warehouse architecture as the blueprint for how your data flows and grows. It’s the structure that helps you store, manage, and analyze data at scale.
It all starts with data sources, where your raw data lives. An ETL/ELT process moves that data into a centralized data store (your warehouse). From there, tools like Hex pull that data to power forecasting, visualization, and everyday decisions.
In any data engineering workflow, a data warehouse system should have the following components to standardize the flow of data:
Staging area: This is the landing zone of a data warehouse, where your raw data from various sources arrives first. It’s held here temporarily, like baggage waiting for inspection, and goes through basic quality checks before heading into the transformation process.
Transformation and integration: This layer is responsible for intense cleaning and formatting. It’s where joins and aggregations are applied, turning raw data into polished versions ready for complex queries. Then, it’s permanently stored in the warehouse.
Presentation layer: This is the serving layer, the final step before your data hits the stage. It creates optimized datasets, ready-to-power dashboards, and reports, which end users rely on to make decisions.
There are two main architecture styles: single-tier vs. multi-tier. And two hosting options: cloud vs. on-prem. So… which one best fits your team? Let’s see.
The single tier is a simple setup with just three layers: a source layer, a data warehouse layer, and an analytics layer. The goal is to simplify data management and reduce storage by creating condensed datasets and removing redundancy.
This setup isn’t too popular among businesses because it’s too basic for organizations that handle multiple data streams and/or sources.
Multi-tier architecture, on the other hand, has multiple layers, each one optimized for specific purposes like security, performance, storage, and accessibility.
Let’s look a little closer at each layer:
The source layer extracts raw data from various source systems — APIs, external databases, CRM systems, and even social media platforms.
Your staging layer is where data is temporarily stored. This is also where it gets cleaned up and transformed for permanent storage.
The warehouse layer is the heart of your data architecture, where all the data is stored together for querying and analysis. This layer also handles business logic. For example, while “sales” is a universal term, your company might call profits something unique, like "capture rate" (which is sales in dollars divided by inventory in dollars). With business logic in place, this formula is applied uniformly, so users can easily query this metric without worrying about human errors in manual calculation.
Lastly, your consumption layer serves as the end-user access layer where your data finally meets its audience. It dishes out transformed, standardized, and fully governed data — the kind of data you can actually trust.
Consumption layers also act as an abstraction layer, so users cannot modify the source/raw data, only access a single source of truth.
With an on-prem data warehouse, you own the entire infrastructure — hardware, software, and maintenance. That means your team sets up the infrastructure, manages it, troubleshoots it, and scales it when needed.
Cloud providers flipped that script with DWaaS (Data Warehouse as a Service). Here, you just need to store and manage data while the provider handles all the infrastructure behind the scenes.
Knowing which data warehouse platform to use depends on various factors. Here are the key variables:
Cloud vs on-premises: If you’re handling sensitive, high-stakes data and have the budget, on-prem wins on control and security. The data never leaves your walls. But if you’re after speed, scale, and fewer infrastructure headaches, cloud warehouses are the clear winner.
Data platforms: The big four cloud data warehouses are Redshift, Snowflake, BigQuery, and Azure Synapse Analytics. Each brings something different to the table, so your ideal match depends on your priorities.
Businesses that need the flexibility of multi-cloud and strong support for semi-structured data like JSON often go with Snowflake.
Those deeply embedded in the AWS ecosystem and looking for full control over nodes, servers, and clusters tend to choose Redshift.
BigQuery handles large-scale workloads well thanks to clustering. It’s a second layer of optimization: you can partition by one column and cluster by another column to scan less data and speed up queries — especially helpful when you're slicing large datasets.
Companies that have invested in Microsoft tools and prefer an SQL-first approach choose Azure Synapse Analytics.
Integration capabilities: Customer service lives in the CRM. Finance sticks to accounting software. When different departments use specialized tools like these, your data warehousing should be compatible to bring it all together. That’s where flexible platforms like Snowflake shine. Snowflake is built to seamlessly integrate with various external tools.
After looking at multiple warehouse tiers and their components, it’s clear that the implementation requires a structured approach. Follow these five steps to hit your data warehouse goals faster.
Before spinning up tables or debating Snowflake vs Redshift, get crystal clear on your goals. Start asking questions like:
What business cases are you solving? This helps you figure out what data actually needs to go in. For example, if you’re chasing better customer insights, you would source CRM data.
What are your budget constraints and security requirements? This resolves on-prem vs cloud debates.
What specific pain points do you want to solve? Think data silos or inconsistent metrics. Identify these early so you can design the warehouse around them. For example, fix inconsistencies in the business layer, and then improve accessibility via the consumption layer.
Once you know your goals, it’s time to lay down the foundation. Most modern data warehouses follow a multi-tier architecture, which includes:
A staging area for raw, unfiltered data
Data transformation logic to clean and shape it
Business logic to apply definitions and calculations
Schema models that organize your data for actual use
Which schema model to choose? Understand your most frequent data use cases, like:
BI reporting and analytics: Go with a Snowflake or star schema. They seamlessly integrate with business intelligence tools and make it easier for stakeholders to navigate dashboards and slice metrics.
Machine learning and feature stores: Lean towards OBT (One Big Table). It flattens everything (facts + dimensions) into one place, so feature engineering is fast, and SQL stays simple.
Tailored datasets for specific teams: Create data marts (like HubSpot does) — small, domain-owned warehouses built for a single team. For instance, finance teams and sales teams can each maintain their own datasets.
Once you’ve locked in your schema and structure, it’s time to move some data. That’s where your data pipelines come in.
ETL (Extract, Transform, Load): This pipeline is great when your warehouse can handle heavy lifting. You clean and shape the data before it lands — ideal for structured data and tight control.
ELT (Extract, Load, Transform): When you’ve got big/unstructured data, dump everything into data storage (Amazon S3 or Delta Lake), then transform downstream. Modern architecture prefers this, as cloud object storage makes data storage dirt cheap.
You’ve already planned the data model in step 2; now it’s time to actually build it. Tools like dbt or Apache Spark help you transform raw data into clean, reliable models. Use them to create:
Staging layers for raw inputs
Data marts for team-specific use
Version-controlled models
Next, define your business logic using debt metrics, Cube, or Looker models; this is where KPIs, calculations, and definitions are created.
Now, your final table is ready to be queried.
Assign roles like domain owners (who own their data marts), data stewards (who ensure data quality), and general users (who are doing the querying). These ensure only the right people can access or edit the data.
Moreover, use lineage tracking (like OpenLineage or Debt Cloud) to know exactly who modified what and when. Add a data catalog with metadata information so your team gets a unified view of all your assets, with easier discovery and documentation.
Finally, don’t forget the non-negotiables: implementing governance frameworks like GDPR, HIPAA, or industry-specific compliance rules. They help build trust with your users.
In a three-tier architecture, your presentation layer, data layer, and logic layer run on multiple servers. Some nodes pull their weight. Others? Not so muc
h. Maybe one server’s crawling during peak hours. Or your data layer can’t keep up with user queries. Either way, you’ve got two options:
Vertical scaling: Upgrade the machine (more CPU, memory, or disk). Similar to how Hex scales resources for data science workflows.
Horizontal scaling: Add more nodes and distribute the workload.
We’ve mapped the pros and cons in this handy table:
Factor | Vertical | Horizontal |
|---|---|---|
Installation | Easy to install, as you’re just adding more resources to existing servers. | Comparatively complex, as you’ll need to configure load balancers and tweak your virtualization setup to work well with the new servers. |
Cost | Cost-effective for small and medium businesses. | Upfront costs are only high if you’re on-prem. In the cloud, it’s pay-as-you-go. This approach is permanent and pays you in the long run. |
Scalability | Limited | High |
Downtime risk | Higher (there’s a limit to how much one server can handle) | Lower with cloud auto-scaling |
Best for | Small business | Large enterprises |
Whether you're just starting out or scaling fast, following these best practices can help you build a warehouse that’s future-proof, cost-efficient, and easy to manage.
Go cloud-native for simplicity and scale. Cloud resources flex with your needs automatically. No more overpaying for unused servers or scrambling during traffic spikes. You only get billed for what you actually use. That means no upfront costs and no complex setup.
Partitioning and indexing big data tables break them into smaller, manageable chunks that can be spread across multiple servers. Efficient partitions not only promote horizontal scaling, they also optimize query performance, making the most out of your resources.
ETL pipelines move data from your source to the warehouse. To speed things up, you can use incremental models, which leverage Change Data Capture (CDC). Instead of reloading everything, CDC only syncs the data that’s been modified.
Here’s what that flow looks like:
Use dbt to define models that incrementally load data into your warehouse.
Then, Hex connects to the warehouse, pulling in the latest data for your analysis.
As data updates in the warehouse, Hex automatically syncs the new data across analysis notebooks and downstream tools.
Use DataOps to version-control ETL pipelines, transformation logic, and YAML configs so every change is tracked, auditable, and reversible.
Add unit tests and quality checks directly into your CI pipelines. That means automatic validation for every pull request, and faster debugging when things break.
Use orchestration tools to trigger builds and tests on every commit or deployment so your pipelines remain reliable, your data stays clean, and your team keeps shipping with confidence.
When all your data lives in one region, you're one outage away from chaos.
Single-region setups are a ticking time bomb — if one region goes down, so does everything else. That’s where multi-region deployments save the day. By spreading your infrastructure across globally distributed regions, you're not putting all your eggs in one basket.
If California goes dark, Seattle’s still humming. Your users get uninterrupted access, and you avoid the “global blackout” that happens when everything is pinned to one place.
Now that you have built the architecture that efficiently processes and stores your data, it's time to make that data actionable.
With Hex, data scientists can easily build data apps to deliver insights to all stakeholders. These apps come with interactive features like dropdowns, filters, and clickable elements, allowing users to explore and pull insights on their own.
And here’s the cherry on top: Hex AI. For users not familiar with SQL or Python, it’s like having a chatbot at your fingertips. Simply tell it what you need, and it will generate reports, SQL queries, or dashboards for you. So, why wait? Sign up for Hex and start using the data you’ve worked so hard to warehouse.