What Is a Fact Table?

A fact table is a table that contains all the business information used in the dimensional model as a central table in a star schema of a data warehouse. A fact table stores fields that represent facts. These include measurements and metrics used for analysis and reporting. There can be more than one fact table, depending on the type of data warehouse design.

What Are the Types of Fact Tables?

There are three types of Fact tables, let’s understand each one of them:

Transaction Fact Table

It represents a basic view of any business operation or event occurring at an instantaneous point in time. The transaction fact table loads a row for the occurrence of each event. Each customer and product appears in more than one transaction. So, there are multiple rows for every customer and product.

Snapshot Fact Table

It represents non-additive or semi-additive facts that describe the state of things at a particular point in time. For example, if a user want to know the performance of a salesman for a specific period like the previous month. Then with a periodic snapshot, user can take a snapshot of the salesman’s activity at the end of each month.

Accumulated Fact Table

It represents all the activities from the beginning to the end of a well-defined process. For example, An order goes through a series of steps such as order placement, shipping, in-transit, and delivery. The fact table associated with the order gets updated with the completion of each step.

To know a 360-degree view of customer data, user should understand the customer’s journey through the entire sales cycle. Since the customer data lives in multiple fact tables, analyzing it becomes challenging. Users often write complex SQL queries with joins across multiple fact tables. This multiplies records and creates many-to-many relationships. This, in turn, causes a data explosion.

How Does Kyvos Prevent Data Explosion and Handle Complex Data Models?

Kyvos makes it easier to deploy multi-fact physical schemas when working with complex hierarchies. It creates a semantic model on top of the source data. It begins by defining the relationships between datasets. Next, Kyvos simplifies data model design using an ML-powered recommendation engine. The engine looks at query patterns. Based on that, it suggests the right dimensions, measures, attributes, and hierarchies. This helps build an optimized model.

Instead of linking fact tables to each other, Kyvos treats each fact table as a separate entity. It uses a common dimension table to connect every fact table to its matching key. This approach prevents record multiplication during joins.

Once the semantic model is built, users can start querying the data. They can slice, dice, and drill down through hierarchies in a consistent and unified way.

Accurate representation of business logic is the key to success. Kyvos helps organizations turn complex business needs into data models. It handles large-scale data, delivers high performance, and provides useful insights.