Welcome to the Data Vault interview questions blog! Data Vault plays an important role in today's modern data analytics and data warehousing. It is a database or data warehouse modeling framework that helps organizations to build trackable and scalable enterprise analytics.
I believe you are here to refresh your skills before giving your data engineering interview. Data Vault is one of the important skills when it comes to data engineering interviews and people with data modeling skills get hired easily. 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 designing process that also represents relationships between different 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 process to work with historical data and allows users to audit, track, and understand data changes, etc. Data Vault helps users to understand the source of each data in the database by recording attributes such as load date and source.
Apart from historical storage tracking, Data Vault helps organizations in building a strong 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 simpler than ever and offers the following benefits:
Yes, Data Vault comes with a highly scalable architecture and supports massive volumes of data. Its architecture has been designed to satisfy enterprise-grade big data requirements and even some users are running multi-petabyte 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 easily 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 to satisfy modern data warehousing requirements Data Vault has come up with a 2.0 version. The latest version offers modern features such as scalable architecture, agile project delivery, operational process, continuous improvement, integrations, automation, etc.
Majorly we can use two ways to load data into the Data Vault. The first method to load data is using the Data Vault loader feature; it has been built to meet any type of data loading requirements in the Data Vault. And the second option used for data loading is the ETL process. In this process, the data gets extracted from the source and applied required transformations, and finally gets 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.
Yes, it is possible to create more than one fact table from a database in Datavault. It can be done using hubs. Creating multiple hubs helps us to build separate fact tables.
Data Vault is being used by below top companies 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 biggest advantages of using a Data Vault is scalability, ease of maintenance, and highly flexible 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 when it comes to data and helps users uniquely identify each record in a table. It is also being used in Data Vault models and helps to identify records. Moreover, it is important 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 a period of 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 the process to understand 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 is growing over the years because of its data handling capabilities. Data engineering has become an important segment of IT and there will always be a good demand for skilled Data Vault professionals. I assume that this Data Vault interview questions blog would have helped you to gain some knowledge. Will keep updating this blog with new questions, stay connected!
By Tech Solidity
Last updated on August 1, 2023
