Data Mart design: structuring flat tables
A Data Mart is designed in six steps: define the question, choose the architecture, gather the source tables, structure the schema, build it and open access. This guide walks through each with a sales Data Mart example, then covers the flat-table mistakes that make reports disagree.
Managing access to business data – whether raw or prepared – is a challenge, especially when multiple source systems feed the same warehouse.
Data Marts help teams cut through the noise, giving each department fast, reliable access to exactly the data they need. But designing a Data Mart correctly – by structuring flat tables thoughtfully – is what separates a well-governed analytics layer from a brittle mess of ad-hoc queries.
Without proper design, Data Marts become bloated, slow, and expensive to maintain. The right approach balances usability, query speed, and performance efficiency while ensuring clean, reliable data for every team that depends on it.
In this guide, we walk through the step-by-step process of building a Data Mart, the common pitfalls to sidestep, and the best practices that make reporting sustainable at scale.
What are Data Marts?
A Data Mart is a focused, analyst-defined data artifact designed to serve the needs of a specific business function – marketing, sales, finance, or any other team that needs reliable numbers fast.
Unlike a data warehouse, which stores enterprise-wide raw and processed data, a Data Mart is smaller, more focused, and optimized for a particular problem or reporting area.
With OWOX Data Marts, analysts write SQL once, publish it as a governed artifact, and business users access it from the Data Mart library – inside Google Sheets, Looker Studio, or wherever they already work. No ad-hoc queries, no duplicate logic, no one-off spreadsheets.
Why Data Marts are essential for efficient data structuring
Why Data Marts are essential for efficient data structuring
A well-designed Data Mart simplifies data access, improves reporting accuracy, and enhances performance for specific business functions. Here are the key advantages that make Data Marts the right foundation for business intelligence and self-service analytics.
Reliable source of truth for reporting
A Data Mart provides a centralized and trusted source of data for a specific department, ensuring all team members work with consistent, accurate information. For example, a Data Mart defining Session or Pageviews for Marketing, or Revenue or Orders for e-commerce or finance teams.
This eliminates the discrepancies that arise when different teams pull data from multiple sources and apply different calculation logic. Everyone uses the same Data Mart, the same numbers, the same definitions.
Seamless integration with spreadsheets and BI tools
Data Marts are designed to integrate seamlessly with both spreadsheet tools and business intelligence (BI) tools like Tableau, Power BI, Looker Studio, and QlikView.
This allows users to analyze, visualize, and create reports without writing SQL themselves. Data Marts serve as a governed alternative to raw BigQuery views – the analyst defines the logic, OWOX governs and schedules it, and the team self-serves from there.
Reducing processing costs
Since Data Marts store only a relevant subset of data, they require fewer resources for processing. This results in lower infrastructure costs and faster data retrieval.
Rather than scanning full raw tables on every report run, business users query a pre-defined, pre-filtered Data Mart that contains only what they need.
Optimizing query performance
Because Data Marts focus on specific data points, queries execute faster compared to retrieving data from full-scale raw tables or joining multiple source tables on the fly.
Indexing, partitioning, and clustering techniques further improve performance, ensuring quick response times for reports and analytics at any scale.
Faster access to actionable insights
A well-structured Data Mart enables business teams to access relevant data quickly, without waiting for enterprise-wide report runs. This speed is crucial for departments that need timely insights to make informed decisions – without filing a ticket to the data team every time.
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Step-by-step guide to building a Data Mart
Step-by-step guide to building a Data Mart
Building a Data Mart involves defining business goals, selecting the right architecture, and structuring data for efficient reporting. To illustrate the process, we’ll follow an e-commerce company building a Sales Data Mart to track customer purchases, product performance, and revenue trends.
Step 1: Define your business goals and objectives
Before writing any SQL, identify the business problem the Data Mart solves. Clear objectives ensure that the Data Mart aligns with business needs and prevent collecting irrelevant data that inflates storage costs.
Example
For our e-commerce company, the goal is to analyze customer purchases and product performance to improve marketing strategies and optimize inventory. The Sales Data Mart should answer questions like:
By defining these objectives upfront, we ensure that the Data Mart only includes relevant sales data – avoiding unnecessary storage and processing overhead.
Step 2: Choose the right Data Mart architecture
Selecting the right Data Mart architecture is crucial for ensuring scalability, efficiency, and data consistency. The choice depends on business needs, existing infrastructure, and data integration requirements.
There are three main types of Data Mart architectures:
Example: For our e-commerce sales reporting, we select a dependent Data Mart because the data already exists in a centralized warehouse. Extracting only relevant sales data from the warehouse ensures data consistency across departments while improving query speed for sales analysis.
SQL to create the Sales Data Mart table in BigQuery:
This structure ensures that only relevant data is extracted into the Data Mart.
Step 3: Gather the tables and models required
After selecting the architecture, identify the tables and models that will store relevant data. These should align with the business goals defined in Step 1.
Example: For the Sales Data Mart, the essential tables include:
SQL to join these tables and extract relevant data for reporting:
This ensures that the Sales Data Mart contains all relevant sales metrics in a single, easy-to-query structure.
Step 4: Develop and structure the Data Mart schema
The schema design determines how data is organized for fast retrieval and efficient reporting. Two common schema types are:
Example: For the Sales Data Mart, we choose a Star Schema, with a fact table (Sales_Fact) and multiple dimension tables (Products_Dim, Customers_Dim, Date_Dim).
Fact table:
Dimension tables:
By structuring data separately into dimensions and facts, queries run faster and business users get cleaner, more intuitive tables to work with.
Step 5: Build and run the Data Mart
After defining the schema, populate the Data Mart by writing the SQL that extracts and loads data. This is where the analyst applies business logic – session definitions, revenue calculations, order attribution – as SQL inside the Data Mart.
This involves:
Example: To populate the Sales_Fact table, the analyst extracts relevant sales data with SQL:
The analyst writes this logic once. OWOX Data Marts then governs, schedules, and fans out that SQL – so the Sales Data Mart stays accurate, updated, and optimized for reporting without manual intervention.
Step 6: Enable user access to the Data Mart
Once the Data Mart is populated and published, business users need easy access to reports via Google Sheets, Looker Studio, Tableau, and Power BI.
With the OWOX Sheets Extension, this workflow looks like this:
This ensures that sales and marketing teams have access to governed, scheduled reports without needing SQL knowledge. The analyst defines the logic; the team self-serves from there.
Because data stays in your warehouse throughout this process – OWOX never copies it to a vendor cloud – your team retains full ownership of the numbers. No lock-in, no access risk if you switch tools.
Avoiding common pitfalls in flat table design
Avoiding common pitfalls in flat table design
Designing flat tables in a Data Mart requires careful planning to prevent inefficiencies, data corruption, and performance issues. Here are the most common pitfalls – and how to avoid them.
Redundant records
Pitfall: Storing duplicate records in multiple locations leads to unnecessary database growth, slower queries, and increasing storage costs. Redundancy also risks data inconsistency when multiple versions coexist.
Solution: Regularly identify and remove duplicate records. Normalize the data where necessary, and store repeated values in lookup tables. Apply primary keys and constraints to maintain data integrity.
Poor naming conventions
Pitfall: Inconsistent or unclear naming makes it difficult for teams to understand and manage the data. Generic names, special characters, or cryptic abbreviations create confusion and slow down onboarding.
Solution: Follow consistent, descriptive naming conventions. Use clear names that define the purpose of a column or table (e.g., Customer_Orders instead of Data_Table). Avoid unnecessary underscores and special characters.
Single Data Mart for all business contexts
Pitfall: Forcing multiple departments to share a single Data Mart results in bloated tables, slow queries, and competing business rules. It also makes it harder to apply consistent logic to specific datasets.
Solution: Design separate Data Marts for different business functions – Sales, Finance, Marketing. Each Data Mart should serve only its intended purpose, keeping complexity manageable and governance clear.
Not setting source-level filters
Pitfall: Pulling all available data into a Data Mart inflates storage costs and slows performance. Without source-level filters, unnecessary data floods the Data Mart and makes every query slower.
Solution: Apply filters at the data source to extract only relevant records. Define clear selection criteria before loading data into the Data Mart to avoid excessive, unused rows.
Best practices for Data Mart reporting – and where OWOX fits in
Best practices for Data Mart reporting – and where OWOX fits in
To maximize the efficiency of Data Marts, organizations need to follow best practices for data structuring, query optimization, and reporting governance. Here are the key strategies – and how OWOX Data Marts supports each one.
Design Data Marts for faster business reporting
A well-structured Data Mart eliminates redundant processing, allowing queries to execute faster. Using indexing, partitioning, and clustering techniques ensures that data retrieval stays efficient as the dataset grows.
This is also where the no-semantic-layer advantage becomes concrete: because metrics are defined at the Data Mart level – as analyst-written SQL – there’s no separate abstraction layer to build, maintain, or debug. The Data Mart is the definition.
Define the scope of each Data Mart
Before implementation, define the business requirements, key metrics, and reporting needs. Establishing scope prevents unnecessary data ingestion and optimizes processing costs. Each Data Mart should align tightly with specific business use cases – only essential data, nothing more.
Focus on the logical Data Mart structure
A logical Data Mart models data based on business rules, organizing it into meaningful relationships. Fact tables hold measurable metrics; dimension tables carry descriptive attributes. This separation keeps the Data Mart clean and makes it easier for business users to explore data without getting lost.
Identify relevant data
Defining what data is essential prevents unnecessary storage and speeds up queries. Separating numeric metrics (facts) from descriptive attributes (dimensions) simplifies reporting. Data Marts should store only the most relevant records – bloated tables hurt everyone.
Design the star schema
A star schema improves query efficiency by structuring data around central fact tables linked to multiple dimension tables. Surrogate keys rather than natural primary keys enhance query performance and simplify joins. This schema structure creates faster, more maintainable reports.
Automate business reporting from Data Marts
Manual reporting is time-consuming, error-prone, and inefficient – and it scales poorly as the business grows. Governing Data Marts and automating refresh scheduling ensures real-time data availability without manual intervention.
OWOX Data Marts simplifies report automation by integrating directly with Google BigQuery and Google Sheets. Analysts define the SQL logic and publish it as a governed Data Mart. The team schedules updates, pulls live data into Sheets, and generates reports without touching SQL – ensuring reports are always current and always traceable to analyst-approved logic.
Every number in every report traces back to a specific, auditable SQL definition. That means no surprises, no hallucinations, and no “where did this number come from?” conversations.
Turn your data into decisions.
Governed data marts give you the clean foundation ML needs to actually work.
- No AI hallucinations
- Analyst-governed definitions
- Every number traces to SQL
Frequently Asked Questions
What are common mistakes in flat table design?
Duplicate rows from joining tables of different grain, unclear column names, one Data Mart stretched across every business context, and missing source-level filters, which make each report apply its own.
When should you use flat tables versus normalized tables in a Data Mart?
Use a flat table when business users query it directly and the data volume is moderate. Keep dimensions in separate tables, as in a star schema, when several fact tables share them or when a hierarchy changes often and should be updated in one place.
What is an example of a Data Mart?
A sales Data Mart: one fact table of order lines with quantity and revenue, joined to customer, product and date dimensions, or flattened into one wide table. Sales, finance and marketing reports all read it, so they show the same revenue.
What are the steps to design a Data Mart?
Six: define the business question, choose the architecture (dependent on the warehouse, independent or hybrid), gather the source tables, structure the schema and its grain, build and run it, then open access to the people who report from it.
What are flat tables, and why are they used in Data Marts?
A flat table holds everything a report needs in one denormalized table, so no joins are required at query time. Data Marts use them because spreadsheet and BI users can filter and pivot one table without knowing how the source tables relate.
Why does a Data Mart need a defined grain?
The grain says what one row stands for, such as one order line. Without it, two users aggregate the same table differently and get different totals. State the grain in the Data Mart's description before choosing columns.
