Data Vault FAQ

 

Data Vault Interview Questions

  

5) What is a Data Vault?

Data Vault is a modern database design framework that supports long-term historical storage of data. It streamlines the working process with historical data and allows users to audit, track, and understand data changes. Data Vault helps users to understand the source of each data in the database by recording attributes such as load date and source.

Besides historical storage tracking, Data Vault helps organizations build a robust and scalable database that supports enterprise-grade analytics, data science requirements, business intelligence, etc.

Want to learn data modeling from scratch? Checkout our hands-on  Data Vault Training program

 

6) What are the different entities of the Data Vault?

Data Vault comprises the following three entities:

1. Hubs: It represent core business concepts (Cus ID/ Product No/ Email, etc.) 
2. Links: Demonstrates the relationship between Hubs
3. Satellites: Stores hub information and relationships between different hubs.

7) What benefits does Data Vault bring in?

Data Vault makes the analytics process far more straightforward than ever and offers the following benefits:

• Agile methodology
• Highly scalable up to PBs
• Flexibility for refactoring
• Support ETL

8) Does Data Vault support Big Data?

Yes, Data Vault has a highly scalable architecture and supports massive volumes of data. Its architecture has been designed to satisfy enterprise-grade extensive data requirements, and some users are even running multi-petabytes using a Data Vault.

Data Vault architecture has been developed to meet growing data requirements and scales up and down based on your requirements. It eliminates the need for reengineering by quickly adopting changing analytics requirements.

9) State the difference between Data Vault & Data Vault 2.0.

The initial release of the Data Vault was designed to support data modeling and data loading processes. To meet growing data demands and satisfy modern data warehousing requirements, Data Vault has developed a 2.0 version. The latest version offers modern features such as scalable architecture, agile project delivery, operational processes, continuous improvement, integrations, automation, etc.

10) What are the Different ways to load data into the Data Vault?

We can use two main ways to load data into the Data Vault. The first method to load data is using the Data Vault loader feature, built to meet any data loading requirements in the Data Vault. The second option used for data loading is the ETL process. In this process, the data is extracted from the source, required transformations are applied, and finally, loaded into the Data Vault.

11) Define the Business Key.

In data engineering terminology, a business key is a unique identifier of a piece of information in a database. It links the data to different data sets and systems and helps engineers to perform data backtrace.

12) State the difference between type 1 and type 2 data change in the data loading context.

Type 1 and Type 2 both are used to demonstrate the data changes to a table. We call it a type-1 change when a new column is added to an existing table.  We call it the type-2 change when any cell is updated with new data.

13)  What do you know about operational data sources?

Operational data sources are called ODS in short, and they are lightweight databases. ODS are connected to various data sources that support real-time analytics and operational reporting tasks.

14) Can you create multiple fact tables from a single database?

Creating more than one fact table from a database in Datavault is possible. It can be done using hubs. Creating multiple hubs helps us to build separate fact tables.

15) Can you name some of the top companies using Data Vault?

The top companies are using Data Vault for their data warehouse and data lake requirements:

• Google
• Meta
• Amazon

16) What makes Data Vault architecture unique compared to all other architectures?

When we consider other architectures, we have star schema and snowflake schema for data modeling, but Data Vault stands out with its capabilities. The most significant advantages of using a Data Vault are scalability, ease of maintenance, and flexibility to accommodate any data changes.

17) What is the use of a staging area in a Data Vault?

Before loading any data into a Data Vault, we must ensure that the data is transformed and available in the required format. Staging is a temporary storage location that ensures all data is cleaned and formatted before loading it into a Data Vault.

18) Explain the primary key & its importance in the data model.

The primary key is an essential concept for data and helps users uniquely identify each record in a table. It is also used in Data Vault models and helps identify records. Moreover, it is essential for achieving data integrity and ensuring data in the table is linked to other data in the Data Vault.

19) What is Slowly changing dimensions?

Changing dimensions means changes occurred to a table over some time. A slowly changing dimension is a data warehouse table that captures and stores different versions of data. It helps us to have a record of each data version at a specific point in time.

20) What is a Semantic Layer?

The semantic layer is a data warehouse layer that helps users to understand data inside a data warehouse. It simplifies understanding of the relationship between different layers in the data and acts as a simplified user interface for data access.

 

 

FREQUENTLY ASKED QUESTIONS ABOUT DATA VAULT

https://data-vault.com/what-is-data-vault/

What is Data Vault?

Data Vault is a method and architecture for delivering a Data Analytics Service to an enterprise supporting its Business Intelligence, Data Warehousing, Analytics and Data Science requirements. At the core it is a modern, agile way of designing and building efficient, effective Data Warehouses.

  

Where did Data Vault come from?

 

What are Hubs in Data Vault?

What are Satellites in Data Vault?

What are Links in Data Vault?

Is Data Vault scalable to work with big data?

Is Data Vault proven?

How do I migrate from an Inmon or Kimball solution to Data Vault?

What technologies work with Data Vault?

What is the difference between Data Vault and Data Vault 2.0?

What is Data Vault data modelling?

Are Data Vaults compatible with Star Schemas?

Who owns Data Vault?

Is Data Vault suitable for my business?

Is Data Vault free?

Do data lakes work with Data Vault?

 

https://climbtheladder.com/data-vault-interview-questions/

Data Vault Interview Questions and Answers

1. Explain the concept of Hubs, Links, and Satellites.

The Data Vault methodology is a data modeling approach designed to provide a scalable and flexible architecture for data warehousing. It consists of three core components: Hubs, Links, and Satellites.

• Hubs represent core business entities and contain unique business keys. They serve as the central point of reference for the data model, ensuring that each business entity is uniquely identified. Hubs are immutable, meaning that once a business key is inserted, it is never updated or deleted.
• Links capture the relationships between Hubs. They model associations and transactions between business entities, ensuring referential integrity by connecting business keys from different Hubs. Like Hubs, Links are also immutable and only grow over time as new relationships are discovered.
• Satellites store the descriptive attributes and context for Hubs and Links. They contain historical data and track changes over time, allowing for a detailed audit trail. Satellites are flexible, enabling the addition of new attributes without altering the core structure of Hubs and Links.

2. Write a SQL query to create a Hub table given a set of business keys.

In a Data Vault model, a Hub table stores unique business keys along with metadata such as load date and record source. The Hub table is central to the Data Vault architecture, linking together various Satellite and Link tables.

Here is an example SQL query to create a Hub table:

CREATE TABLE Hub_Customer (

Customer_HKey INT PRIMARY KEY,

Customer_BusinessKey VARCHAR(255) NOT NULL,

LoadDate TIMESTAMP NOT NULL,

RecordSource VARCHAR(255) NOT NULL

);

In this example, the 

Customer_HKey

 is a surrogate key that uniquely identifies each record in the Hub table. The 

Customer_BusinessKey

 is the unique business key for the customer, 

LoadDate

 is the timestamp when the record was loaded, and 

RecordSource

 indicates the source of the data.

3. Write a SQL query to create a Link table that connects two Hubs.

A Link table represents the many-to-many relationships between two or more Hub tables. It contains foreign keys that reference the primary keys of the connected Hub tables, along with metadata such as load date and record source.

Here is an example SQL query to create a Link table that connects two Hubs:

CREATE TABLE Link_Customer_Order (

Link_Customer_Order_ID INT PRIMARY KEY,

Customer_Hub_ID INT,

Order_Hub_ID INT,

Load_Date TIMESTAMP,

Record_Source VARCHAR(50),

FOREIGN KEY (Customer_Hub_ID) REFERENCES Hub_Customer(Customer_Hub_ID),

FOREIGN KEY (Order_Hub_ID) REFERENCES Hub_Order(Order_Hub_ID)

);

In this example, the Link_Customer_Order table connects the Hub_Customer and Hub_Order tables. The Link table includes the primary key Link_Customer_Order_ID, foreign keys Customer_Hub_ID and Order_Hub_ID, and additional metadata columns Load_Date and Record_Source.

4. Explain the role of hash keys in Data Vault modeling.

In Data Vault modeling, hash keys are used to create unique identifiers for records in hubs, links, and satellites. These hash keys are typically generated using a hashing algorithm, such as SHA-256, applied to the business keys or a combination of attributes that uniquely identify a record. The use of hash keys offers several advantages:

• Uniqueness: Hash keys ensure that each record has a unique identifier, which is important for maintaining data integrity.
• Consistency: Hash keys provide a consistent way to identify records across different systems and environments, making it easier to integrate data from multiple sources.
• Performance: Hash keys can improve query performance by enabling efficient indexing and partitioning of data.
• Scalability: Hash keys support the scalability of the Data Vault model by allowing for the easy addition of new data sources and changes to existing data structures without disrupting the existing data.

5. Write a SQL query to create a Satellite table for a given Hub.

A Satellite table stores the descriptive attributes and their historical changes for the business keys stored in the Hub table. The Satellite table is linked to the Hub table via a foreign key relationship.

Here is an example SQL query to create a Satellite table for a given Hub:

CREATE TABLE Satellite_Table (

Hub_Key INT NOT NULL,

Load_Date TIMESTAMP NOT NULL,

End_Date TIMESTAMP,

Attribute1 VARCHAR(255),

Attribute2 VARCHAR(255),

Attribute3 VARCHAR(255),

PRIMARY KEY (Hub_Key, Load_Date),

FOREIGN KEY (Hub_Key) REFERENCES Hub_Table(Hub_Key)

);

6. How do you manage slowly changing dimensions (SCD) in a Data Vault model?

In a Data Vault model, slowly changing dimensions (SCD) are managed using a combination of Hub, Link, and Satellite tables. The Hub table captures the unique business keys, the Link table captures the relationships between these keys, and the Satellite table captures the descriptive attributes and their changes over time.

To manage SCDs, the Satellite table includes metadata columns such as load date, end date, and record source. These columns help track the history of changes for each attribute. When a change occurs, a new record is inserted into the Satellite table with the updated attribute values and the corresponding metadata. This approach ensures that the historical data is preserved, and the changes can be tracked over time.

7. Describe your approach to integrating real-time data.

Data Vault is particularly well-suited for integrating real-time data due to its ability to handle large volumes of data and its focus on historical accuracy and auditability.

When integrating real-time data into a Data Vault, the approach typically involves the following components:

• Hubs: These store unique business keys and are the central point of integration for real-time data.
• Links: These capture the relationships between hubs and are used to track associations between different business entities.
• Satellites: These store descriptive attributes and context for the hubs and links, allowing for the capture of historical changes over time.

To integrate real-time data, the following strategies are often employed:

• Streaming Data Pipelines: Utilize technologies such as Apache Kafka, AWS Kinesis, or Google Pub/Sub to stream data in real-time from various sources into the Data Vault.
• Micro-batching: Implement micro-batching techniques to process small batches of data at frequent intervals, ensuring that the data is as close to real-time as possible.
• Change Data Capture (CDC): Use CDC tools to detect and capture changes in the source systems and propagate these changes to the Data Vault in real-time.
• Event-Driven Architecture: Design an event-driven architecture where data events trigger the ingestion and processing of data into the Data Vault.

8. Write a SQL script to generate a report combining data from Hubs, Links, and Satellites.

To generate a report combining data from Hubs, Links, and Satellites in a Data Vault model, you can use SQL joins. The Hubs contain the unique business keys, the Links represent the relationships between these keys, and the Satellites store the descriptive attributes.

Here is an example SQL script:

SELECT

h1.business_key AS hub1_key,

h2.business_key AS hub2_key,

s1.attribute1 AS hub1_attr1,

s1.attribute2 AS hub1_attr2,

s2.attribute1 AS hub2_attr1,

s2.attribute2 AS hub2_attr2

FROM

Hub1 h1

JOIN

Link1 l1 ON h1.business_key = l1.hub1_key

JOIN

Hub2 h2 ON l1.hub2_key = h2.business_key

JOIN

Satellite1 s1 ON h1.business_key = s1.business_key

JOIN

Satellite2 s2 ON h2.business_key = s2.business_key

WHERE

s1.load_date = (SELECT MAX(load_date) FROM Satellite1 WHERE business_key = h1.business_key)

AND s2.load_date = (SELECT MAX(load_date) FROM Satellite2 WHERE business_key = h2.business_key);

9. How would you automate the loading and maintenance of a Data Vault model?

Automating the loading and maintenance of a Data Vault model involves several key steps and considerations.

To automate the loading and maintenance of a Data Vault model, you can follow these steps:

• ETL Frameworks and Tools: Utilize ETL tools and frameworks that support Data Vault modeling. Tools like Apache NiFi, Talend, and Informatica can help automate the extraction, transformation, and loading processes.
• Metadata-Driven Approach: Implement a metadata-driven approach to define the structure and relationships of your Data Vault components. This approach allows you to dynamically generate ETL code based on metadata definitions, reducing manual coding efforts and ensuring consistency.
• Scheduling and Orchestration: Use scheduling and orchestration tools like Apache Airflow or Azure Data Factory to automate the execution of ETL jobs. These tools allow you to define workflows, set dependencies, and schedule jobs to run at specific intervals.
• Incremental Loading: Implement incremental loading strategies to efficiently load new and changed data into the Data Vault. This involves capturing changes from source systems and applying them to the appropriate hubs, links, and satellites.
• Data Quality and Validation: Incorporate data quality checks and validation rules into your ETL processes to ensure the accuracy and integrity of the data being loaded into the Data Vault.
• Monitoring and Logging: Implement monitoring and logging mechanisms to track the performance and status of your ETL jobs. This helps in identifying and resolving issues promptly, ensuring the smooth operation of your Data Vault model.

10. Compare and contrast Data Vault 1.0 and Data Vault 2.0.

Data Vault 1.0 and Data Vault 2.0 are two versions of this methodology, each with its own set of principles and practices.

Data Vault 1.0:

• Introduced by Dan Linstedt in the early 2000s.
• Focuses on modeling the data warehouse using three core components: Hubs, Links, and Satellites.
• Emphasizes historical tracking and auditability.
• Primarily designed for relational databases.

Data Vault 2.0:

• Introduced as an evolution of Data Vault 1.0 to address modern data warehousing challenges.
• Includes all the core components of Data Vault 1.0 but adds new components and practices.
• Incorporates Big Data and NoSQL technologies, making it more adaptable to various data storage solutions.
• Introduces the concept of “Business Vault” for derived and calculated data, and “Information Marts” for reporting and analytics.
• Emphasizes agile development, continuous integration, and automation.
• Includes best practices for data governance, security, and performance optimization.