Amazon Data Warehouse Interview Questions

The advent of cloud technology and automation represents the data warehouse’s promising future. Businesses value data more than any other resource, and they view their data as valuable. Essentially, the data warehouse sits at the center of the business intelligence system, analyzing and reporting data. With a solid understanding of data warehousing concepts, you can easily land a job as Big Data Architect, SQL Developer, Data Warehouse Developer, Data Analyst, and more.

A large volume of data is generated daily. Storing this data and ensuring that various departments can use it for analytical, reporting, and decision-making purposes is essential for reporting at various levels. Data warehousing is storing, collecting, and managing this data. This blog will discuss the top 66 data warehouse interview questions and answers you must learn in 2023.

1 What is Hybrid SCD?

Hybrid SCDs are a combination of both SCD1 and SCD2.

It may happen that in a table, some columns are important and we need to track changes for them, i.e., capture the historical data for them, whereas in some columns even if the data changes we do not have to bother. For such tables, we implement Hybrid SCDs, wherein some columns are Type 1 and some are Type 2.

Top Answers to Data Warehousing Interview Questions


A Data Warehouse allows you to collect and manage the data that later helps in providing significant business insights. Since it is an important Business Intelligence (BI) field, ‘Data Warehouse Analyst’ is among the most sought-after career options today. This Data Warehouse Interview Questions blog has a compiled list of some of the most important questions that companies generally ask during Data Warehouse job interviews. So, check out the following Data Warehouse interview questions and prepare them for your job interview:

The Data Warehouse Interview Questions blog is majorly classified into the parts listed below: 1. Basic

Watch this video on Data Warehouse Interview Questions and Answers:

2 What is the difference between E-R modeling and Dimensional modeling?

The basic difference is that E-R modeling has a logical and physical model while Dimensional modeling has only a physical model. E-R modeling is required to normalize the OLTP database design, whereas dimensional modeling is required to denormalize the ROLAP/MOLAP design.

2 What do you mean by dimensional modelling in the context of data warehousing?

Dimensional Modelling (DM) is a data structure technique that is specifically designed for data storage in a data warehouse. The goal of dimensional modelling is to optimise the database so that data can be retrieved more quickly. In a data warehouse, a dimensional model is used to read, summarise, and analyse numeric data such as values, balances, counts, weights, and so on. Relation models, on the other hand, are designed for adding, modifying, and deleting data in a real-time Online Transaction System.

Following are the steps that should be followed while creating a dimensional model:

  • Identifying the business process : The first step is to identify the specific business processes that a data warehouse should address. This might be Marketing, Sales, or Human Resources, depending on the organizations data analytic needs. The quality of data available for that process is also a factor in deciding which business process to use. It is the most crucial step in the Data Modeling process, and a failure here would result in a cascade of irreversible flaws.
  • Identifying the grain : The level of detail for the business problem/solution is described by the grain. Its the procedure for determining the lowest level of data in any table in your data warehouse. If a table contains sales data for each day, the granularity should be daily. Monthly granularity is defined as a table that contains total sales data for each month.
  • Identifying the dimension : Date, shop, inventory, and other nouns are examples of dimensions. All of the data should be saved in these dimensions. The date dimension, for example, could include information such as the year, month, and weekday.
  • Identifying the fact : This stage is linked to the systems business users because it is here that they gain access to data housed in the data warehouse. The majority of the rows in the fact table are numerical values such as price or cost per unit.
  • Building the schema : The Dimension Model is implemented in this step. The database structure is referred to as a schema (arrangement of tables).
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