80 ) Data vault modeling steps

 

Data Vault Modeling in Banking:  ,  

Step 1: The Client's Existing System and Tables (OLTP)

In my banking project, the client operated multiple legacy core banking systems and siloed transaction platforms (Savings, Credit Cards, and Loans) running on separate transactional OLTP databases (Oracle and DB2):

• Core Banking System (Oracle OLTP): Handled everyday customer deposits and account activities.

  • CUST_MASTER: Stored customer ID, personal info, national ID, and address.

  • ACCT_MASTER: Stored account numbers, account types (Savings, Current), and live balances.

  • TRANS_LOG: Stored real-time deposit and withdrawal logs.

• Loan Management System (DB2 OLTP): Handled lending operations and mortgage applications.

  • LOAN_APPLICANT: Stored applicant details and credit scores.

  • LOAN_CONTRACT: Stored loan IDs, approved amounts, interest rates, and active statuses.

Step 2: The Problems They Faced

• Rigid Source System Integration: Whenever the bank acquired a smaller regional bank or launched a new product line with a different database schema, integrating those tables into a traditional dimensional Star Schema broke existing ETL pipelines and forced massive redesigns of surrogate keys.

• Lack of Complete Auditability: Regulatory authorities required a strict, unalterable audit trail showing the exact source system, exact load timestamp, and exact record state for every single banking transaction over a 10-year period. Dimensional models (like SCD Type 2) often overwrote or compressed history and struggled to trace multi-source lineage cleanly.

• Collaborative Loading Bottlenecks: Multiple data engineering teams could not load data into a traditional dimensional warehouse simultaneously because dimension and fact tables had strict foreign key dependency locks. Loading fact tables required dimensions to be fully populated first, causing severe delays in nightly batch processing windows.

Step 3: The Client's Expectation and Why

• The Goal: They wanted an enterprise-grade data warehouse built on Data Vault 2.0 architecture that could ingest data from multiple disparate banking sources without breaking when source structures changed.

• The Reason: They needed a centralized, fully auditable historical repository that separated business keys, descriptive attributes, and relationships so that audit compliance teams and downstream data marts could trace financial data lineage instantly and load data in parallel without dependency locks.

Step 4: The Solution Design I Proposed (Data Vault 2.0 Architecture)

To meet these requirements, I designed a Data Vault 2.0 model consisting of three core table types:

1. Hubs (Business Keys): Stored unique business keys with no descriptive data or foreign keys.

   • HUB_CUSTOMER: Stored unique Customer_ID, Load_Timestamp, and Record_Source.

   • HUB_ACCOUNT: Stored unique Account_Number, Load_Timestamp, and Record_Source.

2. Links (Relationships / Associations): Stored associations between business keys to represent transactions or structural links.

   • LINK_CUSTOMER_ACCOUNT: Connected HUB_CUSTOMER and HUB_ACCOUNT.

   • LINK_ACCOUNT_TRANSACTION: Connected HUB_ACCOUNT to transaction events.

3. Satellites (Descriptive Context & History): Stored descriptive attributes and historical changes tied directly to Hubs or Links.

   • SAT_CUSTOMER_DETAILS: Attached to HUB_CUSTOMER, storing names, addresses, and credit scores with full audit tracking.

   • SAT_ACCOUNT_BALANCE: Attached to HUB_ACCOUNT, tracking balance updates and status flags over time.

Interview Q&A: Data Vault vs. Dimensional Modeling

Q1: Did you work on Data Vault modeling for banking? Give all steps.

Answer:

Yes, I implemented Data Vault 2.0 for a multi-system banking client. The step-by-step implementation process included:

1. Source Analysis & Business Key Identification: Mapped out core business concepts across Oracle and DB2 systems to identify unique business keys (like Customer ID, Account Number) for Hubs.

2. Relationship Mapping: Identified how business entities interacted (such as customers holding accounts or accounts executing transactions) to design Links.

3. Attribute & History Separation: Extracted descriptive details (like names, balances, and risk ratings) into Satellites to maintain a pure audit trail.

4. ETL Pipeline Configuration: Built automated hash-key generation pipelines (using MD5/SHA hashes) in the staging layer to load Hubs, Links, and Satellites in parallel without foreign key dependency bottlenecks.

5. Information Mart (Star Schema) Generation: Built downstream dimensional data marts on top of the Data Vault presentation layer to serve fast business reports and dashboards.

Q2: What is the difference and advantage over dimensional modeling?

Answer:

• Structural Difference: A Dimensional model (Star Schema) is built for fast business reporting, combining business keys and descriptive text into centralized Fact and Dimension tables. A Data Vault model is built for enterprise integration and auditing, breaking data down into Hubs (keys), Links (relationships), and Satellites (history).

• Key Advantages of Data Vault over Dimensional:

  • Extensibility & Agility: If a source system adds new columns or changes tables, you only add a new Satellite table. It never breaks existing structures or downstream tables.

  • 100% Traceability & Auditability: Every single record in a Data Vault includes exact load timestamps and record source metadata, making it ideal for strict banking regulations.

  • Parallel Loading: Because Hubs, Links, and Satellites use hash keys instead of database-generated sequential surrogate keys, multiple data pipelines can load data simultaneously without locking errors.

Q3: How did you handle changes in Data Vault? Give all steps.

Answer:

Handling changes in a Data Vault is streamlined because the architecture is designed to accept schema evolution without rewriting historical data. The steps are:

1. Handling New Attributes (Column Changes): If a bank adds a new field to an existing source table (e.g., adding Customer_Email), we do not alter existing tables. We simply create a new Satellite table or add the column to a new version of the Satellite linked to that Hub, preserving past history cleanly.

2. Handling New Source Systems (Integration): When integrating a new bank acquisition, we ingest its unique business keys into the existing Hubs, use the same hash-key generation logic, and route its descriptive data into new or existing Satellites, mapping the Record_Source metadata to track origin.

3. Handling Changed Data (History Tracking): Satellites automatically capture history by inserting a new row whenever an attribute changes, driven by hash-diff comparisons between the incoming payload and the existing active satellite record.

Q4: When and how do you choose Data Vault over a dimensional model?

Answer:

• When to Choose Data Vault: We choose Data Vault when building an Enterprise Data Warehouse (EDW) for large organizations (like banks or insurance firms) that integrate multiple disjointed source systems, face frequent structural source changes, and require strict, long-term regulatory audit tracking.

• When to Choose Dimensional (Star Schema): We choose a dimensional model when building a department-specific data mart where the primary goal is fast, user-friendly business reporting and dashboarding for end-users.

• How We Combine Them: In enterprise architecture, we don't choose just one; we use Data Vault as the Enterprise Core Layer (single source of truth and audit storage) and generate Dimensional Star Schemas downstream from the Data Vault to feed end-user business reports efficiently.

Additional Interview Questions on Data Vault Modeling

Q5: What is a "Hash Key" in Data Vault 2.0, and why do we use surrogate sequence keys?

Answer:

In Data Vault 2.0, we use cryptographic hash keys (like MD5 or SHA-256) generated from natural business keys instead of database-generated sequential surrogate keys (like Auto-Increment integers). We use hash keys because they allow completely independent, parallel loading of data streams from multiple systems without needing database sequence lookups or causing locking bottlenecks.

Q6: How do you handle "Same-As Links" in Data Vault modeling? Give a banking example.

Answer:

A Same-As Link is used to handle situations where a single business entity has multiple business keys across different systems that need to be resolved. For example, if a banking customer has a retail customer ID in one system (CUST-101) and a corporate banking ID in another system (CORP-998), a Same-As Link connects the two Hub records together to indicate they belong to the same physical person.

Q7: What is a "Multi-Active Satellite," and when would you use one in a banking project?

Answer:

A Multi-Active Satellite is used when a single business key can have multiple active values at the same time for a single point in time, breaking the standard one-to-one relationship between a Hub key and its Satellite attributes. In banking, we use this for a customer who has multiple phone numbers, active email addresses, or multiple credit cards linked to a single account where history needs to be maintained for each individual attribute independently.

Q8: How do you handle Deletes or "Soft Deletes" from source systems in Data Vault?

Answer:

When a record is deleted or deactivated in the source system, Data Vault preserves complete historical integrity. We do not delete rows from Hubs or Links. Instead, we insert a new record in the Satellite table with a status flag (e.g., Is_Deleted = True or Record_Status = 'Inactive') driven by a hash-diff check, ensuring compliance and historical accuracy are maintained.

Q9: How do you query data from a Data Vault model for reporting, since data is split across Hubs, Links, and Satellites?

Answer:

Because Data Vault is normalized and spread across multiple tables, querying it directly for business reports requires complex inner joins across Hubs, Links, and Satellites. To make reporting practical, we build Information Marts (Star Schemas) downstream from the Data Vault presentation layer. Business users and BI tools query the Star Schema, while the Data Vault remains the enterprise audit core.

SQL DDL: Creating Data Vault Tables with Primary & Foreign Keys

Below is the SQL script to create a core Data Vault structure (Hub, Link, and Satellite) for banking customer accounts with primary keys (PK) and foreign keys (FK).

SQL

-- 1. Create Hub Table (Stores unique business keys)
CREATE TABLE HUB_CUSTOMER (
    Customer_HK CHAR(32) NOT NULL,          -- Hash key (PK)
    Customer_ID VARCHAR(50) NOT NULL,       -- Natural business key
    Load_Timestamp DATETIME NOT NULL,       -- Audit metadata
    Record_Source VARCHAR(100) NOT NULL,    -- Audit metadata
    CONSTRAINT PK_HUB_CUSTOMER PRIMARY KEY (Customer_HK)
);

-- 2. Create another Hub Table for Accounts
CREATE TABLE HUB_ACCOUNT (
    Account_HK CHAR(32) NOT NULL,           -- Hash key (PK)
    Account_Number VARCHAR(50) NOT NULL,    -- Natural business key
    Load_Timestamp DATETIME NOT NULL,       -- Audit metadata
    Record_Source VARCHAR(100) NOT NULL,    -- Audit metadata
    CONSTRAINT PK_HUB_ACCOUNT PRIMARY KEY (Account_HK)
);

-- 3. Create Link Table (Stores relationships between Hubs)
CREATE TABLE LINK_CUSTOMER_ACCOUNT (
    Customer_Account_HK CHAR(32) NOT NULL,  -- Hash key of the link itself (PK)
    Customer_HK CHAR(32) NOT NULL,          -- FK pointing to HUB_CUSTOMER
    Account_HK CHAR(32) NOT NULL,           -- FK pointing to HUB_ACCOUNT
    Load_Timestamp DATETIME NOT NULL,       -- Audit metadata
    Record_Source VARCHAR(100) NOT NULL,    -- Audit metadata
    CONSTRAINT PK_LINK_CUSTOMER_ACCOUNT PRIMARY KEY (Customer_Account_HK),
    CONSTRAINT FK_LINK_CUST_ACCT_CUSTOMER FOREIGN KEY (Customer_HK) 
        REFERENCES HUB_CUSTOMER (Customer_HK),
    CONSTRAINT FK_LINK_CUST_ACCT_ACCOUNT FOREIGN KEY (Account_HK) 
        REFERENCES HUB_ACCOUNT (Account_HK)
);

-- 4. Create Satellite Table (Stores descriptive attributes and history for a Hub)
CREATE TABLE SAT_CUSTOMER_DETAILS (
    Customer_HK CHAR(32) NOT NULL,          -- FK pointing to HUB_CUSTOMER
    Load_Timestamp DATETIME NOT NULL,       -- Part of Composite PK (tracks version start time)
    Hash_Diff CHAR(32) NOT NULL,            -- Detects changes in descriptive columns
    First_Name VARCHAR(100),
    Last_Name VARCHAR(100),
    Credit_Score INT,
    Record_Source VARCHAR(100) NOT NULL,
    CONSTRAINT PK_SAT_CUSTOMER_DETAILS PRIMARY KEY (Customer_HK, Load_Timestamp),
    CONSTRAINT FK_SAT_CUST_HUB_CUSTOMER FOREIGN KEY (Customer_HK) 
        REFERENCES HUB_CUSTOMER (Customer_HK)
);

Handling Schema Changes in Data Vault Modeling (Adding / Deleting Columns & Tables)

Step-by-Step Process for Schema Changes in Data Vault

Unlike traditional relational or dimensional models (where altering tables can break fact/dimension joins or require expensive historical rewrites), Data Vault is designed specifically for additive and non-destructive change management.

1. Adding a New Column (Attribute)

• How it works: You never modify an existing Satellite table's structure if you can avoid it, but standard Data Vault allows adding columns to existing Satellites, or better yet, creating a new Satellite table attached to the same Hub.

• The Steps:

  1. Identify which business entity (Hub) the new attribute belongs to (e.g., adding Customer_Email to HUB_CUSTOMER).

  2. Create a brand-new Satellite table specifically for this attribute (e.g., SAT_CUSTOMER_CONTACT) linked to the existing Hub key.

  3. Update the ETL pipeline to populate the new Satellite using hash-diff tracking.

  • Advantage: Existing tables and historical loads remain completely untouched and unbroken.

2. Adding a New Table (Entity or Source)

• How it works: Adding a completely new business entity or integrating a new source system requires zero changes to existing structures.

• The Steps:

  1. Create a new Hub for the new business key.

  2. Create a new Link if it connects to existing Hubs.

  3. Create a new Satellite for its descriptive attributes.

  4. Route the new source stream into these new tables. Because Data Vault uses hash keys, it integrates seamlessly without foreign key dependency conflicts on existing tables.

3. Deleting or Soft-Deleting a Column / Record

• How it works: Data Vault enforces a strict non-deletion policy for audit compliance and historical accuracy. You never drop rows or columns physically.

• The Steps:

  1. For a deleted source column: Mark the old column as deprecated in documentation. In the ETL pipeline, map it to NULL for future loads, while preserving all historical values in past satellite rows.

  2. For a deleted/inactive source record: Do not delete rows from Hubs, Links, or Satellites. Instead, generate a new record in the Satellite table with a status flag set to inactive (e.g., Is_Active = 0 or Record_Status = 'DELETED'), driven by the hash-diff change capture.

SQL DDL: Implementing Schema Changes

Below is the SQL script demonstrating how we handle these changes in a Data Vault environment—specifically showing how to add a new Satellite table for a new column and how to handle soft-deletes via a status flag.

SQL

-- 1. SCENARIO: ADDING A NEW COLUMN
-- Instead of altering an old table, we create a new Satellite table linked to the existing Hub 
-- to store new attributes (e.g., Customer Email and Phone Number).

CREATE TABLE SAT_CUSTOMER_CONTACT (
    Customer_HK CHAR(32) NOT NULL,          -- FK pointing to HUB_CUSTOMER
    Load_Timestamp DATETIME NOT NULL,       -- Part of Composite PK (version start time)
    Hash_Diff CHAR(32) NOT NULL,            -- Detects changes in contact info
    Email_Address VARCHAR(150),
    Phone_Number VARCHAR(50),
    Record_Source VARCHAR(100) NOT NULL,
    CONSTRAINT PK_SAT_CUSTOMER_CONTACT PRIMARY KEY (Customer_HK, Load_Timestamp),
    CONSTRAINT FK_SAT_CONTACT_HUB FOREIGN KEY (Customer_HK) 
        REFERENCES HUB_CUSTOMER (Customer_HK)
);


-- 2. SCENARIO: HANDLING SOURCE DELETES (SOFT DELETE / STATUS FLAG)
-- When a record is deleted or deactivated in the source system, we insert a new row 
-- in the existing Satellite with an explicit deletion/inactive flag. No rows are ever physically deleted.

CREATE TABLE SAT_CUSTOMER_STATUS (
    Customer_HK CHAR(32) NOT NULL,          -- FK pointing to HUB_CUSTOMER
    Load_Timestamp DATETIME NOT NULL,       -- Part of Composite PK
    Hash_Diff CHAR(32) NOT NULL,            -- Detects status changes
    Is_Active INT NOT NULL,                 -- 1 = Active, 0 = Soft Deleted / Inactive
    Deletion_Reason VARCHAR(255),           -- Optional context from source
    Record_Source VARCHAR(100) NOT NULL,
    CONSTRAINT PK_SAT_CUSTOMER_STATUS PRIMARY KEY (Customer_HK, Load_Timestamp),
    CONSTRAINT FK_SAT_STATUS_HUB FOREIGN KEY (Customer_HK) 
        REFERENCES HUB_CUSTOMER (Customer_HK)
);

Deep Dive: Resolving "Same-As" Customer Entities in Data Vault

Step 1: The Problem Scenario (How it Was Previously)

The Issue

When our client bank merged with a regional financial institution, customers interacted with both retail and corporate banking systems. Because these legacy systems operated on separate databases without a shared identifier, a single person (e.g., John Doe) was assigned two completely different business keys:

• Retail System: Assigned CUST-RETAIL-1001

• Corporate System: Assigned HUB-CORP-9988

Previous Data Architecture (Siloed Approach)

Our raw transactional tables and initial Data Vault models treated these as two distinct human beings.

• Previous SQL (Creating Separate Hubs):

SQL

-- Previously, separate hubs were created without knowing they belonged to the same person
CREATE TABLE HUB_CUSTOMER_RETAIL (
    Customer_Retail_HK CHAR(32) NOT NULL,
    Customer_ID VARCHAR(50) NOT NULL,
    Load_Timestamp DATETIME NOT NULL,
    Record_Source VARCHAR(100) NOT NULL,
    CONSTRAINT PK_HUB_CUST_RETAIL PRIMARY KEY (Customer_Retail_HK)
);

CREATE TABLE HUB_CUSTOMER_CORPORATE (
    Customer_Corp_HK CHAR(32) NOT NULL,
    Corporate_ID VARCHAR(50) NOT NULL,
    Load_Timestamp DATETIME NOT NULL,
    Record_Source VARCHAR(100) NOT NULL,
    CONSTRAINT PK_HUB_CUST_CORP PRIMARY KEY (Customer_Corp_HK)
);

• Previous Design Structure:

Plaintext

[Source System A: Retail] ---> [HUB_CUSTOMER_RETAIL] ---> [SAT_RETAIL_DETAILS]
[Source System B: Corp]   ---> [HUB_CUSTOMER_CORP]   ---> [SAT_CORP_DETAILS]
*(Completely disconnected silos leading to fragmented reporting and double-counting)*

Step 2: The Resolution (How it Is Now)

The Solution Design

To connect these isolated entities without altering the core immutable Hub records, we introduced a Same-As Link (LINK_CUSTOMER_SAME_AS).

1. Deterministic Matching Process: In our staging layer, a matching engine evaluated shared attributes (National ID, Tax ID, and Mobile Phone Number).

2. Same-As Link Creation: Once a match was confirmed, we captured the relationship in a specialized Link table that points two Hub keys back to each other.

3. Unified Downstream View: Downstream business data marts consumed this link to merge the profiles into a single master customer dimension.

Updated SQL Scripts (Current Architecture)

SQL

-- 1. Unified Hub Definition (or keeping separate Hubs bridged via a Same-As Link)
-- Here we maintain the Hubs, but link them explicitly.
CREATE TABLE HUB_CUSTOMER (
    Customer_HK CHAR(32) NOT NULL,
    Master_Customer_ID VARCHAR(50) NOT NULL,
    Load_Timestamp DATETIME NOT NULL,
    Record_Source VARCHAR(100) NOT NULL,
    CONSTRAINT PK_HUB_CUSTOMER PRIMARY KEY (Customer_HK)
);

-- 2. Updated Link Table: Same-As Link to bridge duplicate/merged identities
CREATE TABLE LINK_CUSTOMER_SAME_AS (
    Same_As_Link_HK CHAR(32) NOT NULL,      -- Hash key of the relationship (PK)
    Source_Customer_HK CHAR(32) NOT NULL,   -- FK pointing to HUB_CUSTOMER (e.g., Retail profile)
    Target_Customer_HK CHAR(32) NOT NULL,   -- FK pointing to HUB_CUSTOMER (e.g., Corporate profile)
    Load_Timestamp DATETIME NOT NULL,       -- Audit metadata
    Record_Source VARCHAR(100) NOT NULL,    -- Audit metadata
    CONSTRAINT PK_LINK_CUSTOMER_SAME_AS PRIMARY KEY (Same_As_Link_HK),
    CONSTRAINT FK_CUST_SAME_AS_SOURCE FOREIGN KEY (Source_Customer_HK) 
        REFERENCES HUB_CUSTOMER (Customer_HK),
    CONSTRAINT FK_CUST_SAME_AS_TARGET FOREIGN KEY (Target_Customer_HK) 
        REFERENCES HUB_CUSTOMER (Customer_HK)
);

Updated Design Structure

Plaintext

[HUB_CUSTOMER (Retail Key)]  <\
                                >--- [LINK_CUSTOMER_SAME_AS] ---> [Unified Business Data Vault / Mart View]
[HUB_CUSTOMER (Corp Key)]    </

==========================

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.