Data Vault on Databricks Delta Lake - Detailed Analysis

Data Vault has emerged as a widespread data modeling technique for building scalable and agile data warehouses in recent years. Data Vault is a hybrid approach that combines the best features of normalized and denormalized data models, providing flexibility, scalability, and ease of use. This blog will discuss how to model Data Vault on Databricks and explore its pros and cons.

What is Data Vault Modeling?

Data Vault modeling is designed to create a flexible and scalable data warehouse. It is a hybrid approach that combines the benefits of both normalized and denormalized data models. Data Vault modeling comprises three main elements: Hubs, Links, and Satellites.

Hubs are the primary keys of business entities, representing the single source of truth for each entity. Links are the relationships between the Hubs, providing context for the connections. Satellites contain the attributes of the Hubs and Links, and they give the historical tracking of the data.

Modeling Data Vault on Databricks:

Databricks is a cloud-based data platform that provides a collaborative environment for data scientists, data engineers, and business analysts to work together. Here are the steps for modeling Data Vault on Databricks:

Step 1: Create Hubs, Links, and Satellites in Delta Lake:

The first step is to create Hubs, Links, and Satellites in Delta Lake. Delta Lake is a mighty open-source data lake that provides ACID transactions, schema enforcement, and Delta table versioning. You can create Delta tables for Hubs, Links, and Satellites in Databricks using SQL or DataFrame API.

Step 2: Load Data into Hubs, Links, and Satellites:

The next step is to load data into Hubs, Links, and Satellites. You can load data from various sources, such as CSV files, JSON files, Parquet files, or JDBC databases, using Spark connectors. You can use Spark SQL or DataFrame API to transform the data and load it into Delta tables.

Step 3: Create Views for Business Users:

The final step is to create views for business users. Views are SQL queries that provide a business-friendly representation of the data. Delta Lake and Spark SQL can create views that aggregate, filter, and join the data from Hubs, Links, and Satellites.

Pros of Modeling Data Vault on Databricks:

Scalability: Databricks provides a scalable cloud-based platform that can handle large-scale data processing and storage.

Flexibility: Data Vault modeling is a flexible approach that can handle changes to the data model, schema, or data types.

Collaborative Environment: Databricks provides a collaborative environment for data scientists, data engineers, and business analysts to work together, enhancing teamwork and productivity.

Open-Source Technology: Databricks uses open-source technology, including Delta Lake, Spark, and SQL, which are widely used and supported by the data community.

Cons of Modeling Data Vault on Databricks:

Learning Curve: Databricks is a complex platform requiring some expertise, which may lead to a steep learning curve for new users.

Cost: Databricks is a cloud-based platform that charges based on usage, which may lead to high costs for organizations that handle large-scale data.

Integration: Databricks may require integration with other tools and platforms, which can be time-consuming and costly.

Modeling Data Vault on Databricks can provide a scalable and flexible solution for building data warehouses. Databricks is a robust cloud-based platform that provides collaborative tools and open-source technology. However, it also has some challenges, such as a steep learning curve and high costs.