Data Warehouse Interview Questions & Answers

Data Warehouse Interview Questions & Answers

The Questions That You Must Prepare to Crack the Data Warehouse Interview

Is it precise to state that you are making yourself orchestrated for the Data warehouse Interview? If you are then the most imperative thing that you need to do is to make yourself masterminded with some data warehouse interview questions. A Data warehouse is the advancement that will oversee the world in not all that far off future so it is perfect to have such an occupation. The reason behind such arranging is that you will have the ability to move the examiner the most and you won’t be stunned by any question that they ask. You may think where to get such request which may be asked. We have taken the long to assemble such request here with the objective that you surmise that it’s more straightforward to encounter those and be prepared. Other than being set up for the meeting as for request there are alternate points of view that you need to consider. You’re visiting plan should be with the ultimate objective that you are not late in accomplishing the meeting scene. It is perfect to be spot on time than to be late. The accompanying thing is the dress that you wear; you ought to be stylish and should wear formal pieces of clothing. Endeavour to have direct eye contact with the examiner; this will help you to have a particular level of conviction. The exact opposite thing that you ought to be set up about is to make your standpoint up with the true objective that you don’t get startled by any condition. It may so happen that even resulting to taking such arranging you don’t know around an answer. Make an effort not to get unnerved, put aside your chance to recollect the proper reaction and a short time later give the correct one. It is perfect to tell the examiner that you can’t recall the suitable reaction than to give a wrong answer. By and by as you perceive what the game plans that you need to take give us a risk to go to the request that you may be asked.

What is Data Warehouse?

This may be the first question that you are asked while you seat in the hot seat. The questions that will come up, in the beginning, will be rudimental and basic as the interviewer will try to access your basic knowledge about the subject. The answer to this question is: It is a process by means of which any organization can store their data for the purpose of data analysis. The process of storage is certainly electronic in nature and facilitates reporting, the analysis and other nature of activities which helps in discovery of knowledge contained in the data. This very process can also be used for integration of data and management of data. The data warehouse is oriented according to the subject; it is non-volatile in nature, it is integrated and is time-variant.

After reading to the answer of this particular question from the data warehouse interview questions you may have understood the reason of preparation. The reasons of preparation are to refresh the knowledge that you already have and most importantly to have the ability to proper frame the answers so that you can impress the interviewer the most.

Explain Data analytics?

It is the science of examination of the available raw data with the purpose of having a conclusion as to what the data contains. The best way to have such analytics is through the proper usage of data warehouse.

Tell the benefits that can achieve by the usage of data warehouse?

There are various benefits which can be achieved by the proper usage of data warehouse. The benefits are:

  • It gives you a proper manner of integration of data.
  • It helps in historical storage of data.
  • The proper usage of data warehouse gives you the means so that you have the best data analytics.
  • This analysis can be done over the required time frame and the result can be used to improve the efficiency of the process that your business follows.

The answer is formatted into points so that it is easier for you to remember. It is not prudent that you memorize the entire answer but you need to remember the points and expand those according to the time that you get.

Why use data warehouse?

This may be the next question from the set of data warehouse interview questions that you are asked. Let us see what should be the answer to this question. When such basic questions are asked try to be more informative so that the interviewer understands that you know the subject like the palm of your hand. The answer to this question is: The reasons for the usage of the data warehouse are:

  • This helps to have means of proper reporting of various processes that are used by an organization. That is to say in technical term it is used for having the proper data analytics available to an organization.
  • It is also used for integration of different data that are received from different sources. This compilation of data helps to know the true value of any business measure.
  • Data mining is another aspect why this nature of data storage is used. Having such mining helps in the prediction of a trend that should be followed, forecast any implementation that has to be in place for business growth, you can also recognize the pattern that you must follow for having the desired growth of business.

Explain the difference between OLTP and OLAP?

As you can see the questions are happening to be going into the depth of the subject. This is basically the nature that you will find in real interviews. There are some differences between OLTP and OLAP. The differences are:

  • The system which is transaction in mode which collects data is OLTP. OLAP on the other hand is the system of reporting and analysing the data that is the warehouse.
  • The optimization of OLTP system can be for INSERT, UPDATE of operations and so they are highly normalized. If you think about OLAP they are intentionally de-normalized so the data retrieval can be done in a fast manner using the SELECT operation.

Can you explain data mart?

In an organization, there may be data which are relevant for different departments and have the source from various departments. Data mart is designed for each and every set of data that an organization has. They are generally built on top of the data warehouse.

Explain what you know about ER model?

If one wishes to reduce redundancy by normalizing the data then there must be the implementation of ER model or the entity-relationship model. This type of a model is entirely different from dimensional modelling as in this case the basic purpose is to enhance the mechanism of data retrieval.

Can you explain dimensional Modelling?

The answer to this question should definitely be yes but then also let us see how to answer so that the interviewer gets impressed. The answer is: Dimension and fact tables are what dimensional model contains. The fact table is built up with various measurements which are transactional in nature and also have foreign keys which qualify a data from the dimension table. The basic purpose of usage of such a model is to enhance the data retrieval process by making is faster and easier. Attaining higher degree of normalization is not the ultimate goal of this model.

Explain what you know about dimension?

Dimension is an element that is present in a data that can help categorization of each item in a data set into regions which are over-lapping. Let us make it clear with an example. If anyone says 40 kgs, does it make any sense? Definitely it does not. But on the other hand if one says that 40 kilos of rice are sold to Ram on 11th of April, 2017 then it makes complete sense. The name of the product, the customer name, and the date of sale are dimensions which give qualification to the data measure.

So, what do you feel like after reading the data warehouse interview questions that have been discussed so far? The questions are sure enough to be the ones that you will face while seating for an interview upon data warehouse. The preparation will give you the necessary confidence so that you can easily crack the interview and be the one chosen for the job.

Explain what is fact?

Facts are generally values which are numerical in nature and can be aggregated. It is something that is measured.

Explain the different nature of measures?

There are different natures of measures. They are additive, semi-additive and non-additive. Let us see what they are.

Non-additive: These are those types of measures that cannot be used in between any numeric aggregation function like those of SUM () or AVG (). Ratio and percentage are examples of this nature of the measure. Even when the data is non-numeric in nature but are stored in the fact table they can be non-additive. Certain varchar flags that are in fact table are examples of such nature.

Semi-additive: When only a subset of certain aggregation function can be bought into operation then it is called semi-additive. Like if we use the function of sum on balance then it is not so useful whereas the usage of max or min is beneficial. Again if we consider the rate of price or currency then sum is meaningless whereas the function of average is useful.

Additive measures: These measures can be used with any nature of aggregation functions.

Can you explain Star-schema?

This schema is utilized in models of data warehouse where there is one centralized fact table references number of dimension tables so as the primary key from all the available dimension tables run into the available fact table as foreign key where there is storage of measures. The diagram of this entity-relationship looks like a star, hence the name. If you consider a fact table where there is storage of sales quantity of every product and of all the customers in a time frame then, the measure will be the quantity of sales and the keys from the tables containing names of the customers, the product sold and the time of sales will be the one flowing into the fact table.

Explain snow-flake schema?

This is another nature of logical arrangement of tables. It will be easier to explain if you do it using an example. Let us consider a fact table where there is storage of sales quantity of different products that are sold to different customers. The measure over here is the quantity of sales and the various keys from the table containing the name of customers, the product sold and the time of sales will flow into the fact table. Additionally all the product can be further sub divided under various tables of the product family. This nature of construction is called snow-flaked as the table of product is further snow-flaked into tables of product family.

Name the different nature of dimensions?

There are various natures of dimensions that are used in data warehouse. They are:

  • Conformed Dimension
  • Junk
  • Degenerated
  • Role Playing

Depending on the frequency of changes in data inside a dimension, it can further be classified into:

  • Unchanging or static dimension
  • Slowly changing dimension
  • Rapidly changing dimension.

Explain Conformed Dimension?

This may be the next that you are asked from the set of data warehouse interview questions. The answer to this question is that when a dimension sharing occurs between different subject areas then that dimension is called Conformed dimension.

It will be clear if you explain it with an example. If we consider the dimension Customer then we will see that both the sales and the marketing department of any organization use that table for the reports that they produce.

Explain Junk Dimension?

As far as this question is concerned the answer is known to you. But as always it is better to know the most efficient formulation so as to impress the interviewer the most. The answer to this question is that this nature of dimensions is those which have low-cardinality attributes. This makes possible to remove them from a table and junk them into the abstract dimension table. The most frequent use of this dimension is for the implementation of RCD in data warehouse.

Can you explain role-playing dimension?

The answer is known to you but may not be for others so let us know the answer. There are certain dimensions which can be reused for various applications which are within the same data base but having different meaning such dimensions are called role-playing dimensions. For example, the dimension of Date can be used as Date of sales, Date of delivery or may be for Date of hire. So, the dimension Date is a role-playing dimension.

It is always prudent to explain with an example so that the explanation if proper and the interviewer understand that you know the subject well.

For the most part, these are the sorts of data warehouse interview questions that are asked amid a meeting relating to data warehouse. Be that as it may, all said and done it must be said that there is an element of shakiness connected with an interview. It is ideal to set up the above questions. The principle purpose of such planning is that so that the questioner does not have the capacity to discover a place where they can make you bow down. The questions that we have talked about above may now be appearing to be straightforward however when you situate yourself at the hot seat you would find many to be the one that becomes hard to remember if there is no proper preparation. In this way, get ready well before you take off for the meeting. Amid the meeting keep a collected mind. Keeping in mind the end goal to confound you, they might state the basic thing in a troublesome way. Along these lines, set aside your opportunity to dissect the question before you give the appropriate response. Make yourself clear concerning what they are attempting to ask than answer. Do not just answers a question as you hear it. You may be thinking that it will show how prepared you are on the subject but do that way you may have missed the catch that is there in the question. So, take your time to understand the question and even ask if you have any confusion and then answer. This nature of preparation will make you the one who will definitely crack the interview and will have the job. So, be prepared and do not miss the chance that you have got to have a secured job when many are trying to have such.

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