December 15th, 2025
Data Warehousing & Business Intelligence: How It Works in 2025
By Drew Hahn · 10 min read
After helping teams connect their data sources and build reporting workflows, I've seen how data warehousing and business intelligence fit together in practice. Here's how modern organizations store, organize, and analyze their data to make faster decisions in 2025.
What is data warehousing?
Data warehousing means collecting data from different sources and centralizing it in one repository to make analysis and reporting easier.
A data warehouse stores all your company's critical information in one location, whether that’s sales, marketing, finance, or customer data. With everything centralized, you can generate reports and spot trends across your entire business.
Once your data is in one place, BI tools can connect to the warehouse to create dashboards, run reports, and answer business questions.
Data warehouses vs traditional databases
A data warehouse differs from a traditional database in a few key ways. These include:
Purpose: Databases handle day-to-day transactions like processing orders or updating customer records. Data warehouses handle analysis and reporting across historical data.
Structure: Databases organize data for fast reads and writes. Data warehouses organize data for fast queries across large datasets.
Time range: Databases typically store current information. Data warehouses store months or years of historical data for trend analysis.
Many companies start with individual databases for each tool they use. Your CRM has one, your accounting software has another, and your marketing platform has a third. A data warehouse pulls all of these into one location.
I ran into this when I wanted to know which campaigns brought in customers with the highest lifetime value. That question required data from three separate systems. Without a data warehouse, I would have exported from each tool and combined everything manually in a spreadsheet. With a warehouse, I queried all three datasets together.
What is the role of data warehousing in business intelligence?
The role of data warehousing in business intelligence is to provide one central source of organized data that business intelligence (BI) tools can query, visualize, and report on.
BI relies on complex questions that pull from multiple sources. When I wanted to know which customer segments had the highest churn rate, I needed data from my CRM, payment system, and support tickets. A data warehouse centralizes everything in one queryable location.
Without a warehouse, BI tools connect directly to each source system, which slows down production databases and creates inconsistent definitions. I've seen "active customer" mean different things in a marketing database versus a billing system.
A warehouse solves both problems. It handles analytical queries without affecting production performance and gives you one place to define business logic. When you say "active customer," everyone pulls from the same definition.
How data gets prepared for a warehouse (ETL vs ELT)
Before your BI tools can analyze anything, your data needs to be extracted from source systems, transformed into a usable format, and loaded into the warehouse. The order of these steps matters more than you'd expect. Here’s how each approach works:
ETL (extract, transform, load)
ETL is the traditional approach. Data gets pulled from sources, cleaned, and restructured in a staging area, then loaded into the warehouse in its final form. This method made sense when storage was expensive and processing power was limited. You only wanted clean, ready-to-use data taking up space in your warehouse.
ELT (extract, load, transform)
ELT flips the last two steps. Data gets pulled from sources and loaded directly into the warehouse (or a data lake first), then transformed inside the destination system. This approach has become more popular as cloud warehouses like Snowflake and BigQuery have made storage cheap and processing power abundant.
Which approach works better?
The practical difference comes down to flexibility. With ETL, you decide upfront exactly how data should be structured. If you need a different view later, you often have to re-extract from the source. With ELT, you keep the raw data available and can transform it in new ways as your questions change.
I've switched to ELT for most projects because it gives me room to adjust. When a stakeholder asks for a new breakdown I didn't anticipate, I can reshape the data without going back to the source system.
Many BI setups use ELT because cloud warehouses handle the heavy lifting. Tools like Fivetran or Stitch extract and load the data. Then, transformation tools like dbt reshape it into the tables and views your team needs for analysis.
How do data warehouses work?
For BI to work well, your data warehouse needs three things running smoothly: storage, software, and ongoing management. Here's how each part supports your reporting and analysis:
Storage
Your data has to live somewhere. You can host it on-premises on servers your company owns, in the cloud through providers like Amazon Redshift, Google BigQuery, or Snowflake, or use a hybrid setup that mixes both.
I've worked with cloud warehouses on most recent projects. You pay for what you use, scale up or down as needed, and skip the hardware maintenance. On-premises setups still make sense for organizations with strict data residency requirements or legacy systems that can't easily move to the cloud.
Your BI tools will query this storage layer directly, so where you host affects dashboard performance and data freshness.
Software
Your warehouse software determines how fast you can get answers. Tools like Amazon Redshift, Google BigQuery, Snowflake, and Databricks are designed to scan large datasets and run complex queries quickly.
When I ask "show me Q3 revenue by region, product category, and customer segment," the software pulls from millions of rows and returns results quickly. Traditional database software would take much longer because it's optimized for transactions, not analysis. That speed matters for BI, where users expect dashboards to load in seconds, not minutes.
Management
Someone needs to monitor query performance, manage user access, handle schema changes, and troubleshoot issues. This used to require a dedicated data engineering team. Cloud warehouses have reduced that burden, but they haven't eliminated it entirely.
I still spend time each month reviewing slow queries and updating permissions as team members change roles. Poor management shows up fast in BI: slow dashboards, wrong numbers, or users locked out of reports they need.
When should you use data warehouses for BI?
Not every team needs a full data warehouse setup. You need a warehouse when:
Data lives in multiple systems: If you're constantly exporting from three or four tools and combining data in spreadsheets, a warehouse will save significant time.
Questions span departments: When finance needs to see marketing data alongside sales data alongside product usage, you need a central repository.
Historical analysis matters: Warehouses excel at storing years of data for trend analysis. If you only care about the current state, a database might be enough.
Multiple people need access: When more than a few people run regular queries, a warehouse provides the performance and access controls you need.
Report consistency is critical: If different teams keep producing different numbers for the same metric, a warehouse gives you one source of truth.
You might not need a warehouse when:
You have one main data source: If everything lives in one tool already, connecting BI directly to that source could work fine.
Your data volume is small: Teams with a few thousand records can often get by with spreadsheets or lightweight tools.
You're a very early-stage company: The overhead of setting up and maintaining a warehouse probably isn't worth it until you have stable data sources and clear reporting needs.
The tipping point usually comes when manual data work starts eating significant time each week. If someone spends hours combining spreadsheets for a recurring report, that's a sign you've outgrown your current setup.
How BI tools and data warehouses work together
BI tools sit on top of data warehouses as the analysis and visualization layer. The warehouse stores and organizes data. The BI tool helps you query it, build dashboards, and share insights.
The connection typically works like this:
Data flows into the warehouse from your source systems through ETL or ELT pipelines.
The warehouse processes and stores that data in tables optimized for analysis.
BI tools connect to the warehouse and let users query data through drag-and-drop interfaces or SQL.
Results get visualized as charts, dashboards, and reports that update as new data arrives.
Many BI platforms like Tableau, Power BI, Looker, or Julius have native connectors for popular cloud warehouses. I've found the setup straightforward with most tools, usually just entering warehouse credentials and selecting which tables to expose.
The split between warehouse and BI tool means you can swap out either component without rebuilding everything. If you outgrow your BI tool, you can switch while keeping your warehouse intact. If you migrate to a new warehouse, your BI tool just needs updated connection settings.
Key features of data warehouse software for business intelligence
When you’re evaluating data warehouse options for BI use cases, these features matter most:
Query performance: BI dashboards need to load quickly. Look for warehouses that handle complex aggregations and joins without long wait times, especially as your data volume grows.
Scalability: Your data will grow. Cloud warehouses that scale compute and storage independently give you flexibility to handle spikes without overpaying during quiet periods.
Connector ecosystem: The warehouse needs to accept data from your source systems. Check for native integrations with your CRM, marketing tools, financial software, and any other systems you rely on.
SQL compatibility: Most BI tools query warehouses using SQL. Standard SQL support means easier integration and a larger pool of people who can write queries.
Security and access controls: You'll want to limit who can see what. Role-based access, column-level permissions, and audit logs become important as more people use the data.
Cost model: Cloud warehouses charge differently. Some bill by storage, some by compute time, some by data scanned. Understanding the model helps you predict costs and optimize usage.
Transformation support: If you're using ELT, you need a warehouse that can handle transformation workloads efficiently. Look for support for tools like dbt or built-in transformation features.
Challenges of data warehouse software for business intelligence
Building a data warehouse and BI setup isn't without hurdles. Here are challenges I've seen teams run into most often:
Data quality issues surface quickly. A warehouse exposes inconsistencies you could ignore when data lived in silos. Duplicate records, missing values, and conflicting definitions all become visible. I've spent weeks cleaning up data that looked fine in individual systems but fell apart once combined.
Transformation logic gets complicated. Turning raw data into analysis-ready tables requires business logic. Defining what counts as a "sale" or an "active user" seems simple until you dig into the details. These definitions need documentation and ongoing maintenance as your business changes.
Costs can surprise you. Cloud warehouses charge based on usage, and poorly optimized queries can run up bills quickly. I learned this the hard way when a dashboard that scanned our entire transaction history every time someone opened it doubled our monthly bill.
Adoption takes effort. Having a warehouse and BI tools doesn't mean people will use them. Training, documentation, and ongoing support help move teams away from spreadsheets and manual processes.
Maintenance never stops. Source systems change. New data sources get added. Business definitions evolve. Someone needs to keep the warehouse and transformation logic current.
Skills gaps create bottlenecks. Traditional BI setups require SQL knowledge to get answers from data. If only a few people on your team can write queries, they become a bottleneck for everyone else's questions.
This last challenge is where AI-powered tools are starting to change the equation. Instead of waiting for someone who knows SQL, business users can ask questions in natural language and get answers directly.
Analyze your data without SQL using Julius
Data warehousing and business intelligence give you the foundation for data-driven decisions, but traditional setups still require SQL or technical skills to get answers.
Julius is an AI-powered data analysis tool that lets you skip the code. Connect directly to your data sources and ask questions in natural language to get insights, charts, and reports.
Here’s how Julius helps:
Quick single-metric checks: Ask for an average, spread, or distribution, and Julius shows you the numbers with an easy-to-read chart.
Built-in visualization: Get histograms, box plots, and bar charts on the spot instead of jumping into another tool to build them.
Catch outliers early: Julius highlights suspicious values and metrics that throw off your results, so you can make confident business decisions based on clean and trustworthy data.
Recurring summaries: Schedule analyses like weekly revenue or delivery time at the 95th percentile and receive them automatically by email or Slack.
Smarter over time: With each query, Julius gets better at understanding how your connected data is organized. It learns where to find the right tables and relationships, so it can return answers more quickly and with better accuracy.
One-click sharing: Turn a thread of analysis into a PDF report you can pass along without extra formatting.
Direct connections: Link your databases and files so results come from live data, not stale spreadsheets.
Ready to see how Julius can help your team make better decisions? Try Julius for free today.
Frequently asked questions
How are business intelligence and data warehouses different?
Business intelligence refers to the tools and methods used to analyze data and create reports, while data warehouses are the storage systems that hold the data being analyzed. BI tools connect to warehouses to query, visualize, and share insights. You need both working together for a complete analytics setup.
What’s the difference between a data lake and a data warehouse?
A data lake stores raw, unprocessed data in its original format, while a data warehouse stores cleaned, organized data that's ready for analysis. Data lakes cost less but require technical skills to use. Many companies use both, with the lake feeding into the warehouse after transformation.
Do you need BI tools if you already have a warehouse?
Yes, you need BI tools if you already have a warehouse because a warehouse stores and organizes your data, but it doesn't create charts, dashboards, or easy exploration on its own. BI tools handle charting, interactive dashboards, and let non-technical users query data without writing code.
