How does Data Vault add value when we have the Delta Lake?
In the world of modern data management, businesses often find themselves navigating a maze of tools, architectures, and methodologies to meet their ever-evolving data needs. Among the popular approaches are Delta Lake and Data Vault. While both have their strengths, it’s important to understand how they complement each other and why Data Vault can be a game-changer even when you’re leveraging Delta Lake.
In this article:
Understanding Delta Lake
Delta Lake is an open-source storage layer that brings reliability to data lakes. Built on top of Parquet files, it provides ACID transactions, schema enforcement, and the ability to handle incremental data changes. It’s a robust foundation for modern data warehouses and data lakes, especially when using tools like Databricks.
However, Delta Lake primarily focuses on managing data storage and changes. It doesn’t inherently bridge the gap between raw source data and the business-ready reports and dashboards that users demand.
Enter Data Vault: Bridging the Gap
Data Vault is a modeling approach designed to address the disconnect between raw data and user needs. While Delta Lake handles data storage efficiently, Data Vault focuses on the *why* and *how* of transforming that data into actionable insights. Here’s where Data Vault excels:
- Data Modeling: Data Vault organizes data into Hubs, Links, and Satellites, ensuring a flexible and scalable structure. Hubs capture business keys, Links handle relationships, and Satellites store descriptive data.
- Data Integration: It helps integrate disparate data sources into a unified model that reflects the business context.
- Change Tracking: While Delta Lake tracks changes at the file or record level, Data Vault optimizes this by capturing deltas more efficiently, especially when splitting data into specialized Satellites.
- Target-Oriented Design: Data Vault focuses on producing business-ready data models like star schemas, flat tables, or dashboards, rather than being a consumption model itself.
Performance Challenges and Solutions
A frequent criticism of Data Vault on Delta Lake revolves around query performance, particularly due to the columnar storage of Parquet files. Joins can be slow, but this is more a characteristic of the storage mechanism than the modeling technique. Here are some strategies to address this:
- Denormalization: Flattening data into wide tables eliminates the need for joins, resulting in faster query performance.
- Materialized Views: Creating materialized Parquet views for end-user consumption ensures high performance without impacting upstream processes.
- Optimized Storage: Use technologies like Iceberg or Delta tables for Hubs and Links, and consider presenting Satellites as views to minimize storage overhead.
- Incremental Load: Design systems to handle insert-only incremental loads, reducing the complexity of updates and deletes.
Why Business Users Love Data Vault (Even If They Don’t Know It)
The ultimate goal of any data architecture is to serve business users. Reports, dashboards, and analytics are the end-products they care about. Data Vault excels here by enabling the creation of robust information models that align with user requirements:
- Flexibility: Business rules can be implemented on top of the Data Vault model to derive the desired target model.
- Scalability: Large data flows can be broken down into manageable pieces, making the system easier to maintain.
- Agility: Changes in business requirements can be accommodated without overhauling the entire model.
Delta Lake and Data Vault: Better Together
Rather than viewing Delta Lake and Data Vault as competing approaches, think of them as complementary. Delta Lake provides the foundation for reliable data storage and change tracking, while Data Vault transforms this raw data into meaningful, business-ready formats.
For example, Delta Lake can serve as the staging or landing zone, where raw data is ingested and stored. Data Vault then takes over to model this data into Hubs, Links, and Satellites, preparing it for business consumption. The combination ensures both robust data management and the flexibility to meet diverse analytical needs.
Final Thoughts
Data Vault is a powerful methodology for bridging the gap between raw data and actionable insights. Even in environments that leverage Delta Lake, Data Vault adds value by providing a scalable, user-focused approach to data modeling. By combining the strengths of these two technologies, organizations can achieve both reliability and agility in their data architectures.
As with any tool or methodology, the key is to tailor the implementation to your specific needs, ensuring that both performance and usability are optimized. Whether you’re dealing with Databricks, Parquet, or other tools, Data Vault provides the flexibility and structure to deliver what matters most: business value.
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Meet the Speaker
Michael Olschimke
Michael has more than 15 years of experience in Information Technology. During the last eight years he has specialized in Business Intelligence topics such as OLAP, Dimensional Modelling, and Data Mining. Challenge him with your questions!
