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Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
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Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Most Frequently Asked Data Warehouse Interview Questions

Prashant
Prashant Last Updated : 03 Aug, 2022
5 min read

This article was published as a part of the Data Science Blogathon.

Introduction

Organizations are turning to cloud-based technology for efficient data collecting, reporting, and analysis in today’s fast-changing business environment. Data and analytics have become critical for firms to remain competitive. Reports, dashboards, and analytics tools are used by business users to derive insights from data, monitor corporate performance, and support decision-making. This is where Data Warehousing comes in as a key component of business intelligence, allowing companies to improve their performance. These reports, dashboards, and analytics tools are powered by data warehouses, which store data effectively to reduce data input and output (I/O) and provide query answers swiftly to hundreds of thousands of users concurrently.

What exactly is a Data Warehouse?

A data warehouse is a centralized storage system that enables data storage, analysis, and interpretation to improve decision-making. Data warehouses receive data regularly from transactional systems, relational databases, and other sources.

Data Warehouse
Data Warehouse’s main Principle, Source:starship-knowledge.com

A data warehouse is a collection of organizational data and information derived from operational sources and external data sources. Periodically, data is extracted from various internal applications, such as sales, marketing, and finance, as well as customer-facing and external partner systems. The data is then made accessible and analyzable by decision-makers.

Interview Questions on Data Warehouse

1. What is the difference between Data Warehousing and Data Mining?

A Data warehouse stores data from several transactional databases via extraction, transformation, and loading. Data is periodically saved. It saves a vast quantity of data. Data warehouse applications include product management and development, marketing, finance, banking, etc. It is utilized for increasing operational efficiency and generating and analyzing MIS reports.
Whereas, Data Mining is the process of discovering patterns in huge datasets via the use of machine learning, statistics, and database management systems. Data is routinely evaluated here. It focuses mostly on analyzing data samples. Market Analysis and Management, Identifying Anomaly Transactions, Corporate Analysis, Risk Management, etc., are a few examples of application cases. It is used to improve corporate operations and make better judgments.

2. What are the significant features of a data warehouse?

Following are some of the significant features of a data warehouse: 

  • Subject-Oriented – A data warehouse is subject-oriented because it delivers information on a subject instead of the company’s activities. These may include products, customers, suppliers, sales, and income. Instead of focusing on actual operations, a data warehouse concentrates on modeling and analyzing data for decision-making.
  • Integrated − It is created by merging data from numerous sources, such as flat files and relational databases, allowing for more accurate data analysis.
  • Time Variant − The data gathered in a data warehouse are associated with a certain time frame. A data warehouse’s data gives information from a historical perspective.
  • Non-volatile − Non-volatile storage does not delete existing data when new data is added. A data warehouse is kept distinct from the operational database, so frequent modifications to the operational database are not reflected in the data warehouse.

3. Define OLTP and OLAP.

  • On-Line Transaction Processing (OLTP) is a program that alters data whenever received and has many concurrent users.
  • Online Analytical Processing (OLAP) is a system that collects, maintains, and analyses multidimensional data for analysis and management purposes.

4. What is metadata?

The definition of metadata is information about data. Metadata gives data a more comprehensive identification and acts as the basis for its interactions with other data. It may also be a valuable tool for saving time, remaining organized, and maximizing the effectiveness of your working files.

5. What distinguishes structured data from unstructured data?

Structured data is organized, has a well-defined format, and can fit into a predefined table. It employs the DBMS storage technique. Scaling schemas is quite challenging. Among the protocols listed below are ODBS, SQL, ADO.NET, etc. Unlike structured data, unstructured data lacks a schema or framework. It is mostly unmanaged, highly scalable at runtime, and capable of storing data. Among the protocols adopted are XML, CSV, SMSM, SMTP, and JASON, among others.

6. In the context of data warehousing, what do you understand about a data cube?

A data cube is a multidimensional data model that holds data that has been streamlined, summarised, or aggregated for fast and simple analysis utilizing OLAP technology. The data are kept in a data cube, which facilitates online analytical analysis. In data warehousing, an n-dimensional data cube can be built. A cube is typically thought of as a three-dimensional structure. A data cube holds information in the form of facts and dimensions.

Data Warehouse
Source: interviewbit

7. What is the purpose of an ODS?

A data store for operational systems is called an operational data store. For reporting and analysis, this data is frequently used.

8. What do you understand by the term data purging?

Data purging is a procedure that involves ways that can permanently delete data from storage. Several approaches and strategies can be employed for data purging. The process of data forging frequently contrasts with data deletion. Thus, they are not the same. Data deletion is more temporary, whereas data cleansing permanently eliminates the data, freeing up additional storage and memory space that may be used for other reasons. The purging process allows us to archive data even if it is permanently removed from the main source, giving us the option to recover that data if we purge it. On the other hand, the deleting process permanently removes data but does not always require keeping a backup; it usually involves insignificant amounts of data.

9. Obtain a list of industry data warehouse systems currently in use.

The following are some of the key data warehouse systems now in use in the industry:

  • Snowflakes
  • Apache Hadoop
  • Oracle Exadata
  • Microfocus Vertica
  • Teradata
  • GCP Big Query
  • SAP BW4HANA
  • AWS Redshift

10. What is virtual data warehousing?

A virtual data warehouse gives a collected picture of completed data. A virtual data warehouse does not include historical data. It is frequently regarded as a logical data model of the provided metadata. Virtual data warehousing is the standard data system method for facilitating analytical decision-making. It is one of the most straightforward methods for translating data and presenting it in decision-makers format. As a result of virtualizing the data, it produces a semantic map that the top user can view.

Conclusion

This brings us to the conclusion of the blog post on the Most Frequently Asked Data Warehouse Interview Questions. We hope you found this information helpful and are now more prepared to attend the upcoming interviews. Below are a few of the article’s most important takeaways:

  • A Data warehouse is used to store data from several transactional databases, Whereas; Data Mining is the process of discovering patterns in huge datasets.
  • (OLTP) stands for On-Line Transaction Processing, and (OLAP) stands for Online Analytical Processing
  • A data store for operational systems is called an operational data store
  • A virtual data warehouse provides a consolidated view of all of the data that has been gathered.

I hope you liked my article on the most common questions asked in data warehouse interviews. If you are preparing for interviews, check more articles on our blog.

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion.

Prashant
Prashant

Hello, my name is Prashant, and I'm currently pursuing my Bachelor of Technology (B.Tech) degree. I'm in my 3rd year of study, specializing in machine learning, and attending VIT University.

In addition to my academic pursuits, I enjoy traveling, blogging, and sports. I'm also a member of the sports club. I'm constantly looking for opportunities to learn and grow both inside and outside the classroom, and I'm excited about the possibilities that my B.Tech degree can offer me in terms of future career prospects.

Thank you for taking the time to get to know me, and I look forward to engaging with you further!

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