Data Architect Interview Preparation Guide

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For a long time in the past and also even today, Data warehouses are built to facilitate reporting on different key business processes of an organization, known as KPI. Today we often call this whole process of reporting data from data warehouses as "Data Analytics". Data warehouses also help to integrate data from different sources and show a single-point-of-truth values about the business measures (e.g. enabling Master Data Management).

Data marts are for the most part intended for a solitary branch of business. They are designed for the individual departments. For example, I used to work for a health insurance provider company which had different departments in it like Finance, Reporting, Sales and so forth.

We had a data warehouse that was holding the information pertaining to all these departments and then we have few data marts built on top of this data warehouse. These DataMart were specific to each department. In simple words, you can say that a DataMart is a subset of a data warehouse.

A Dimension that is utilized as a part of different areas is called as conformed dimension. It might be utilized with different fact tables in a single database or over numerous data marts/warehouses. For example, if subscriber dimension is connected to two fact tables – billing and claim then the subscriber dimension would be treated as conformed dimension.

Facts represent quantitative data. For example – net amount due is a fact. A fact table contains numerical data and foreign keys from related dimensional tables.

The physical data model will be showing primary keys, foreign keys, table names, column names and column data types. This view actually elaborates how the model will be actually implemented in the database.

Data Modelling is the diagrammatic representation showing how the entities are related to each other. It is the initial step towards database design. We first create the conceptual model, then logical model and finally move to the physical model.

Generally, the data models are created in data analysis & design phase of software development life cycle.

A conformed dimension is the dimension that is shared across multiple subject area. Consider 'Customer' dimension. Both marketing and sales department may use the same customer dimension table in their reports. Similarly, a 'Time' or 'Date' dimension will be shared by different subject areas. These dimensions are conformed dimension.

Theoretically, two dimensions which are either identical or strict mathematical subsets of one another are said to be conformed.

Additive measures can be used with any aggregation function like Sum(), Avg() etc. Example is Sales Quantity etc.

ER model or entity-relationship model is a particular methodology of data modeling wherein the goal of modeling is to normalize the data by reducing redundancy. This is different than dimensional modeling where the main goal is to improve the data retrieval mechanism.

A data warehouse is a electronic storage of an Organization's historical data for the purpose of Data Analytics, such as reporting, analysis and other knowledge discovery activities.

Other than Data Analytics, a data warehouse can also be used for the purpose of data integration, master data management etc.