Features
Data modeling is the process of designing a logical and physical structure for data so it can be stored, queried, and maintained efficiently and accurately.
Data Modeling Explained
Data modeling is the technical blueprint for how information is organized, connected, and stored so that analytics, applications, and AI can use it reliably and at scale.
Data modeling is the disciplined practice of translating real-world business concepts—customers, orders, subscriptions, sensors—into a structured representation that can live inside relational, document, graph, or columnar databases. It defines entities (tables or collections), their attributes (columns or properties), and the relationships that bind them together. The goal is to create an unambiguous, semantically rich map of data that supports efficient storage, query performance, data quality, and future change.
Without a well-thought-out model, data becomes a maze of ad-hoc tables, duplicated metrics, and conflicting definitions. This slows development, introduces bugs, and makes it impossible to build trusted analytics. Thoughtful modeling:
High-level diagram that captures what the business cares about—entities, major relationships, and key definitions—without technical detail. Stakeholders use it to validate that the data reflects the real world.
Adds technical precision: entities become tables, attributes get data types, relationships gain cardinality (one-to-many, many-to-many), and primary/foreign keys are defined. Platform-agnostic.
Maps the logical model to a specific database engine. Here we decide on VARCHAR lengths, indexes, partitioning, clustering keys, and distribution style for cloud warehouses like Snowflake or BigQuery.
A normalized approach that reduces redundancy by splitting data across many tables linked via keys. Common in OLTP systems where write efficiency and update consistency are critical.
Dimensional model with a central fact table (transactions, events) linked to denormalized dimension tables (customer, product, time). Optimized for analytics on columnar MPP warehouses.
Hybrid approach for modern lakehouses. Hubs store business keys, Links store relationships, and Satellites store attributes. Highly auditable and flexible for change-data-capture pipelines.
CREATE TABLE scripts managed in version control.Suppose an e-commerce startup wants near-real-time sales analytics.
sales_facts (order_id, customer_id, product_id, quantity, unit_price, order_ts)dim_customers (customer_id, signup_date, region, loyalty_tier)dim_products (product_id, category, brand, sku)dim_time (time_id, order_date, weekday, month, year)Galaxy’s modern SQL editor shines once your model exists—and even while it’s evolving:
Design schemas around the analytics queries you’ll actually run, not around the shape of incoming source data.
Use consistent prefixes (e.g., dim_, fct_) so join paths are instantly clear.
Store every ALTER TABLE in Git. Combine with CI tests that run sample queries to catch breaking changes.
“Revenue” vs “gross revenue” matters. Store definitions in a data catalog or inline via comments that Galaxy’s Copilot can surface.
Joining 15 tables for a simple KPI tanks performance. Denormalize hot dimensions or move to a star schema.
Natural keys (email addresses) can change. Use immutable surrogate keys to maintain referential integrity.
Transactional workloads and analytical workloads have conflicting patterns. Split OLTP and OLAP schemas or use hybrid patterns like Data Vault.
The following SQL illustrates creating a simple star schema in a Postgres-compatible warehouse:
-- Dimensions
CREATE TABLE dim_products (
product_sk SERIAL PRIMARY KEY,
product_id TEXT UNIQUE NOT NULL,
category TEXT,
brand TEXT,
sku TEXT,
created_at TIMESTAMP DEFAULT NOW()
);
CREATE TABLE dim_customers (
customer_sk SERIAL PRIMARY KEY,
customer_id TEXT UNIQUE NOT NULL,
signup_date DATE,
region TEXT,
loyalty_tier TEXT
);
-- Fact table
CREATE TABLE fct_sales (
sales_sk BIGSERIAL PRIMARY KEY,
order_id TEXT NOT NULL,
order_ts TIMESTAMP NOT NULL,
customer_sk INT REFERENCES dim_customers(customer_sk),
product_sk INT REFERENCES dim_products(product_sk),
quantity INT,
unit_price NUMERIC(10,2)
);
-- Example analytics query
SELECT d.region,
DATE_TRUNC('month', f.order_ts) AS order_month,
SUM(f.quantity * f.unit_price) AS gross_revenue
FROM fct_sales f
JOIN dim_customers d ON f.customer_sk = d.customer_sk
GROUP BY 1,2
ORDER BY 2 DESC;
Data modeling is the foundation of every analytics stack. A well-designed schema prevents data chaos, accelerates queries, and enables consistent metrics so teams can focus on insights rather than firefighting.
No. Although the principles originated in relational systems, you can model data for document stores, graph databases, and even data lakes. The core idea—entities, attributes, relationships—remains.
Whenever business rules, source systems, or performance requirements change. Use version-controlled migrations so updates are repeatable and auditable.
Surrogate keys are synthetic, immutable identifiers (usually integers) that stand in for natural business keys. They guarantee stable joins even when business data changes.
Galaxy’s AI Copilot autocompletes complex joins, flags missing relationships, and updates saved queries the moment your schema evolves—keeping everyone aligned with the latest model.
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