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What is Data Mart?

A data mart is a curated, subject-specific subset of a data warehouse designed to serve a single business unit or analytical use case. In scraping infrastructure, while the data lake holds the raw HTML and the warehouse stores the normalized records, the data mart is where the final, aggregated metrics — like daily pricing trends or competitor inventory levels — live. It isolates query workloads and enforces strict access controls for end consumers.

Data EngineeringOLAPStar SchemaData DeliveryAnalytics

// 02 — definitions

Curated for
consumption.

Why dumping raw scraped records onto a business analyst's desk is a failure of data engineering, and how data marts bridge the gap.

Ask a DataFlirt engineer →

TL;DR

A data mart is the final delivery layer in a modern data stack. It takes the massive, complex tables of a central data warehouse and distills them into a focused schema (usually a star schema) optimized for specific BI tools and specific teams. It reduces query cost, improves performance, and limits data exposure.

01Definition & structure

A data mart is an access layer of the data warehouse environment that is used to get data out to the users. It is a subset of the data warehouse and is usually oriented to a specific business line or team. While a data warehouse contains highly normalized data from across the entire enterprise, a data mart contains denormalized, aggregated data structured specifically for fast querying and reporting.

Fact Tables: The quantitative metrics (e.g., scraped price, stock count, review rating).
Dimension Tables: The descriptive context (e.g., product details, competitor info, time/date).
ETL/ELT: The pipeline that extracts from the warehouse, transforms the data, and loads it into the mart.

02How it works in practice

In a scraping context, the raw HTML/JSON is dumped into a data lake. An extraction job parses this into structured records and loads them into a central data warehouse. Finally, a transformation tool (like dbt) runs on a schedule to aggregate this normalized data into a data mart. A pricing team connects their Tableau or Looker dashboard directly to this mart. Because the data is pre-joined and pre-aggregated, their dashboards load in milliseconds instead of minutes, and compute costs are kept minimal.

03Dimensional modeling

Data marts almost exclusively use dimensional modeling — specifically the star schema. This design deliberately introduces data redundancy (denormalization) to optimize for read performance. Instead of joining a prices table to a products table, to a categories table, to a brands table (as you would in a 3NF warehouse), the star schema flattens the descriptive data into a single dim_products table. The BI tool only has to perform one join to the fct_prices table.

04How DataFlirt handles it

We don't just deliver raw JSON lines. For enterprise clients, DataFlirt provisions managed data marts using Snowflake or BigQuery. We handle the entire ELT pipeline: scraping the targets, normalizing the schema in our central warehouse, and materializing the final star schema in your dedicated mart. We enforce strict RBAC so your analysts only see the modeled data, and we monitor the dbt runs to ensure the mart is refreshed within minutes of the scrape job completing.

05The risk of data silos

The biggest failure mode of data marts is when they are built independently without a central data warehouse (independent data marts). If the marketing team builds a mart for scraped reviews, and the pricing team builds a mart for scraped prices, but they use different definitions for "Product ID", the business ends up with conflicting numbers. Modern data engineering solves this by using a "hub and spoke" model: all marts must be derived from the same central warehouse using conformed dimensions.

// 03 — the performance model

Why build a
data mart?

Data marts trade storage redundancy for query speed and compute efficiency. DataFlirt provisions client-specific data marts to ensure heavy analytical queries don't scan petabytes of raw pipeline history.

Query Cost = C = bytes_scanned × compute_rate

Marts pre-aggregate data, dropping bytes_scanned by 90%+ compared to warehouse queries. Cloud Data Warehouse Pricing

Join Complexity = J = fact_table ⨝ Σ dimension_tables

Star schema design minimizes join depth compared to 3NF warehouses. Dimensional Modeling

DataFlirt Delivery Latency = L = T_scrape + T_{warehouse_load} + T_{mart_refresh}

Typically < 15 mins for spot-price feeds from fetch to BI dashboard. DataFlirt Pipeline SLO

// 04 — the ELT pipeline

Materialising a
pricing data mart.

A dbt run executing the transformation from the central warehouse (normalized records) into a client-facing data mart (aggregated daily pricing).

dbtSnowflakeStar Schema

edge.dataflirt.io — live

CAPTURED

// dbt run: updating client_pricing_mart
14:02:11 | Concurrency: 4 threads (target='prod')
14:02:12 | 1 of 4 START table model mart.dim_products...
14:02:15 | 1 of 4 OK created table model mart.dim_products [SUCCESS 1 in 3.1s]
14:02:15 | 2 of 4 START table model mart.dim_competitors...
14:02:17 | 2 of 4 OK created table model mart.dim_competitors [SUCCESS 1 in 2.4s]
14:02:17 | 3 of 4 START incremental model mart.fct_daily_pricing...
14:02:42 | 3 of 4 OK created incremental model mart.fct_daily_pricing [SUCCESS 1 in 25.8s]

// running schema tests
14:02:45 | PASS not_null_fct_daily_pricing_price_usd
14:02:46 | PASS relationships_fct_daily_pricing_product_id__dim_products
14:02:48 | WARN accepted_values_dim_products_category // 3 unknown categories

14:02:50 | Done. PASS=12 WARN=1 ERROR=0

// 05 — design constraints

Where data marts
become bottlenecks.

The operational friction points when maintaining dozens of client-specific data marts across a high-volume scraping infrastructure.

MARTS MANAGED · · · · 140+ active

AVG REFRESH · · · · Hourly

UPDATED · · · · · · 2026-05-19

01

Pipeline dependency delays

% of SLA breaches · Waiting on upstream warehouse loads to finish

02

Schema drift propagation

% of SLA breaches · Source site changes breaking mart aggregations

03

Stale dimension tables

% of SLA breaches · Slowly changing dimensions out of sync

04

Over-aggregation

% of SLA breaches · Losing the grain required for drill-down

05

Storage redundancy costs

% of SLA breaches · Duplicating data across multiple marts

// 06 — delivery architecture

Isolated compute,

curated schemas, zero noisy neighbors.

DataFlirt delivers scraped datasets not just as raw S3 files, but as fully managed data marts. We provision isolated Snowflake or BigQuery environments where the data is already modeled into fact and dimension tables. Your BI tools connect directly to the mart. You never pay to scan our raw extraction logs, and your queries never compete for compute with our ingestion pipelines.

client_pricing_mart_prod

Live status of a managed data mart provisioned for a retail client.

database.engine Snowflake

schema.type Star SchemaKimball

refresh.cadence Hourlycron: 0 * * * *

last.refresh 14:02:50 UTCsuccess

query.latency.p95 1.2soptimized

access.control RBAC enforced

storage.footprint 42.8 GB

Stay ahead of the pipeline

Data engineering
intel, weekly.

Anti-bot shifts, scraping infrastructure updates, dataset delivery patterns, and business outcomes from our pipelines. Short, technical, no fluff.

// 07 — FAQ

Common
questions.

About data mart architecture, dimensional modeling, query performance, and how DataFlirt delivers analytics-ready scraped data.

Ask us directly →

What is the difference between a data warehouse and a data mart? +

A data warehouse is the central repository for all enterprise data, usually stored in a normalized format (like 3NF) to maintain single-source-of-truth integrity. A data mart is a subset of that warehouse, denormalized and structured (usually as a star schema) specifically for fast querying by a single department or for a specific use case. Warehouse = storage focus; Mart = query focus.

Why not just use views on the data warehouse? +

Compute cost and performance. If an analyst queries a view that joins five billion-row normalized tables, the warehouse engine has to perform those joins on the fly. Materializing a data mart pre-computes those joins and aggregations. It prevents BI dashboards from accidentally running full table scans on petabyte-scale raw tables, saving massive compute costs.

What is a star schema? +

A dimensional modeling technique where a central 'fact table' (containing measurable, quantitative data like prices or inventory counts) is surrounded by 'dimension tables' (containing descriptive attributes like product names, categories, or competitor details). It looks like a star. It's the standard design for data marts because it requires fewer joins and is highly intuitive for BI tools.

How does DataFlirt handle schema changes in the mart? +

We use versioned dbt models. If a scraped field changes on the target site, our extraction schema bumps a version. The data mart view is explicitly mapped to handle the transition — either by coalescing the old and new fields or by providing a default value — ensuring that downstream BI dashboards don't break while the data engineering team reviews the drift.

Top-down vs bottom-up data marts? +

The Inmon (top-down) approach builds the central enterprise data warehouse first, then provisions dependent data marts from it. The Kimball (bottom-up) approach builds individual data marts first and integrates them via conformed dimensions to form the warehouse. DataFlirt uses the top-down approach: we land all scraped data in a central lakehouse, normalize it, and then spin out client-specific marts.

Can I bring my own data warehouse? +

Yes. While we offer managed data marts, we can also push raw or normalized data directly to your S3/GCS buckets, or use reverse ETL to push modeled data directly into your existing Snowflake, BigQuery, or Redshift infrastructure. We adapt to your data stack.

$ dataflirt scope --new-project --target=data-mart READY

Tell us what
to extract.
We do the rest.

20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off catalogue dump or a continuous feed across millions of records — we scope, build, and operate the pipeline.

Start a pipeline → View pricing

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