A Data Warehouse is a centralized storage system for essential business information. It serves as a data management system designed to enhance business intelligence (BI) by enabling the querying and analysis of extensive data sets, including application logs, transactional data, and user data. Consequently, the design of data warehouses must prioritize performance based on business needs. For example, a business with high data influx requires efficient write capabilities, while a business focused on querying may prefer a model optimized for faster reads.
Data warehouses are defined by five key characteristics: subject-oriented, integrated, time-variant, and non-volatile. These traits are essential for creating an efficient, business intelligence-ready data warehouse.
First, being subject-oriented means that a data warehouse is designed to store data relevant to specific themes, such as sales, distribution, and marketing.
Second, integration signifies that data from multiple sources can be combined and consolidated within the warehouse.
Third, the time-variant aspect refers to the organization of data according to different time periods, enabling historical analysis.
Finally, non-volatile means that once data is entered into the warehouse, it remains unchanged—data can be added but not altered or deleted.
There are three main data warehouse models namely: Kimball, Inmon, and Data Vault. Each model has its own strengths and weaknesses. Hence, their usage highly depends on business requirements.
Source: Geeksforgeeks.com
The Inmon approach involves an enterprise data warehouse that logically separates (normalizes) data into data marts. Data marts represent one particular area of focus and usually correspond to its own reporting tool. Examples of particular areas of focus include product details, business drivers, aspects, relationships, dependencies, and affiliations.
The enterprise data warehouse acts as the single source of truth to provide data consistency. The advantage of this model is that writing data is easy due to the normalized nature of the model. However, querying data is more difficult for the very same reason.
Source: Geeksforgeeks.com
The Kimball approach is also often referred to as the star schema. A dimensional data warehouse logically segregates multiple interconnected data marts. A data mart in the Kimball model represents one subject area with several attributes. Common attributes connect data marts to each other.
Compared to the Inmon approach, the denormalized nature of the Kimball model makes it easy to query data because of its ground-up model. However, the tradeoff lies in the fact that there is no single source of truth--meaning there may be data irregularities.
Source: Researchgate
The Data Vault Model (DVM) was created by Dan Linstedt with the tagline “Turning Big Data into Sensible Business Information”. It is more than a model. It is also an architectural approach designed for agile warehousing. It is meant to deliver an enterprise data warehouse (similar to the Inmon approach) whilst removing the drawbacks of the 3rd Normal Form (3NF) and dimensional modeling techniques.
Dan Linstedt describes the Data Vault as a collection of normalized tables that meticulously track historical data and maintain unique links, supporting various business functions. Its primary characteristics include:
This layer collates data from multiple sources.
This is considered the “core” layer. It contains untransformed data and entities such as hubs, links, and satellites. The Business Data Warehouse contains the resulting data from the application of business logic.
This layer is also known as the “presentation” layer. Data from the business data warehouse is exploited for reporting and mining by the OLAP cubes. Additionally, this layer is deployed via dimensional modeling.
Entities
Source: DWGeek.com
As mentioned earlier, there are 3 entities in a data vault: The hub, link, and satellite.
Contains a list of unique business keys having its own surrogate key. These business keys represent core business concepts such as customer, vendor, sale, or product. They are used to track and locate information within the business data warehouse.
Establishes connections between hubs (business keys) and other links. Note that links do not hold data--they simply represent the relationship between data contained in the hub.
Holds temporal attributes that can change over time and simultaneously acts as a historical record. It is a time-dimensional table that contains descriptive information which provides context to the hub’s/link’s business keys.
Though all data models deliver relative to what is needed, today’s needs have evolved over the past decade. Some of today’s common business requirements include:
Data Vault is designed to meet modern business requirements by addressing key needs. Here are some of the main benefits of using Data Vault:
Data Vault 2.0 represents the latest advancements in Data Vault Modeling (DVM), incorporating contemporary best practices. Here are the key enhancements made in DVM 2.0 compared to DVM 1.0:
These improvements make Data Vault 2.0 a robust framework for modern data warehousing needs.
There are numerous data vault automation tools available today. The unifying feature among these tools is their metadata-driven nature, ensuring consistency and flexibility throughout the entire lifecycle of a data warehouse. Consequently, data vault automation is predominantly template-driven. Metadata dictates the generation of data vault-specific objects, tables, and load processes, significantly reducing the development time required for ETL scripts. The automation process encompasses the following elements:
A health organization specializing in connecting health and life companies with members of the healthcare ecosystem sought to enhance its marketing efforts by transitioning from legacy data silos to Data Vault 2.0. The company had amassed vast amounts of data over the years to tailor personalized campaigns for its clients. However, each new acquisition introduced additional data silos, making it difficult to integrate 12-year-old legacy data with new data for effective analytics and business intelligence. Compounding the issue, their on-premises data warehouse lacked documentation, rendering the ETL processes cumbersome and hampering business intelligence efficiency, which negatively impacted overall operations.
To address these challenges, the organization decided to automate the Data Vault lifecycle to accelerate time-to-value. They migrated their data from an on-premises Microsoft SQL Server environment to a cloud data warehouse. By adopting the Data Vault 2.0 modeling method, they could automate the entire process from development to deployment. This transition enabled them to quickly generate entities, analyze data lineage, establish robust data governance policies, and create DDL, DML, and ETL scripts. As a result, the organization significantly improved its data integration, analytics, and business intelligence capabilities, driving better business outcomes.
At Codvo, our goal is to ensure that your business intelligence truly adds value. We embrace the agile Data Vault 2.0 methodology for modeling data warehouses, staying current with industry best practices to deliver efficient and streamlined BI processes. If you're interested in learning more, reach out to us at marketing@codvo.ai.
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