Welcome to the Data Vault interview questions blog! A Data Vault is essential in modern data analytics and warehousing. It is a database or data warehouse modeling framework that helps organizations build trackable and scalable enterprise analytics.
You are here to refresh your skills before giving your data engineering interview. Data Vault is essential for data engineering interviews, and people with data modeling skills get hired quickly. This blog comprises frequently asked Data Vault interview questions and helps you with detailed explanations. Let's jump into the blog post!
Basic Data Vault Interview Questions
Data modeling is a conceptual data design process representing relationships between data objects. The typical data modeling process involves steps such as requirement collection, conceptual & logical design, final design, and deployment.
Below listed are some of the commonly followed data modeling types;
Below listed are the commonly used data modeling techniques:
Data modeling plays a crucial role in designing scalable databases. The following are the advantages of data modeling:
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.
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Data Vault comprises the following three entities:
Data Vault makes the analytics process far more straightforward than ever and offers the following benefits:
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.
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.
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.
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.
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.
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.
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.
The top companies are using Data Vault for their data warehouse and data lake requirements:
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.
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.
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.
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.
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.
Conclusion:
The implementation of Data vault architecture has grown over the years because of its data handling capabilities. Data engineering has become an essential segment of IT, and there will always be a demand for skilled Data Vault professionals. I assume this Data Vault interview questions blog would have helped you gain some knowledge. I will keep updating this blog with new questions; stay connected!
By Tech Solidity
Last updated on January 29, 2024
