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Reading list

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
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
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

Demystifying NoSQL: Your Complete Interview Guide

Neil D
Neil D Last Updated : 12 Oct, 2022
6 min read

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

Introduction

In data science, learning about databases is inevitable. In fact, as a data science expert, you have to learn how to work with databases, run queries quickly, and more. There is no way around it!

He has two things to know. Learn as much as possible about database administration and understand how to approach it efficiently. Trust me; you’ll come a long way in data science. Data engineers must work with all databases, especially SQL and NoSQL. But most of us already have a fair amount of experience with SQL databases. Where we stumble is when we need to move to a NoSQL database. It can be a little confusing at first. Getting started is always the hardest step.

(Source: src)

This article describes the essential differences between these two types of databases to clear this roadblock. This will give you two overviews and make it easier to start your journey. Let’s start

Interview Question on NoSQL

1. What do you understand and know about NoSQL in databases?

Contrary to the literal translation of the meaning, NoSQL stands for “Not Only SQL.” It is a new way of thinking about databases, which can handle many structured, semi-structured, and complicated data. It refers to various database technologies created in response to increased data being saved about individuals, things, and goods. Performance and processing requirements, as well as the frequency of access to this data. Contrarily, relational databases were not created to handle the size and agility issues that plague modern applications, nor were they meant to benefit from the affordable storage and processing power available today. So the main target of NoSQL is to create an alternate database in SQL -where textual data can be stored easily in a less-structured manner.

2. What are the features and advantages of NoSQL?

Unlike RDBMS, NoSQL is far more easily scalable and provides superior performance. Additionally, it helps address the issues that RDBMS failed to address:

  • NoSQL is capable of handling large amounts of structured, semi-structured, and unstructured data
  • OOPs can easily be used and integrated with NoSQL
  • Provides efficient scale-out architectures while RDBMS mainly operates on monolithic architectures
  • It provides agile sprints, and the iterations are quick because the in-memory caching option is available to increase the performance of queries.
  • It provides good support for Analytic tools on top of Bigdata
  • It is capable of being hosted on cheaper hardware machines
NoSQL database
(Source: src)

3. What are the different types of databases available under NoSQL?

NoSQL databases come in the following types:

  • Document Oriented DB – One of the characteristics of the NoSQL database is this. The data should be stored without schema. As a result, scalability will be higher, and JavaScript object notation will be used. The job will be completed more quickly and for less money. Example -MongoDB
  • Key-Value Stores – The data is often stored in tables in the RDBMS database, while hash tables are used in NoSQL to store data. Each of these tables has its own identity. Working with a key-value store is preferable to utilizing joins if you are looking for data. This key value will retrieve data from the hash table more quickly. Examples – Riak, Voldemort, and Redis.
  • Graph DB – A graph database is one of the most crucial databases in NoSQL. It is primarily tailored for navigating and storing data relationships. Edges will contain data relationships, and the idea is entity information. Banks, social media, new channels, etc., use this database. Example –  Neo4J and HyperGraphDB.
  • Column Oriented Stores- This gives NoSQL much more flexibility. Keyspace is a concept in column databases that functions somewhat similarly to a relational model’s schema. All column families are contained in this keyspace, which in turn comprises rows and columns. It takes a little while to get your head around, but it’s not too difficult. Example -Cassandra and HBase.
NoSQL
The different types of databases in NoSQL (Source: src)

4. What is the difference between Vertical and Horizontal Databases?

Vertical Database

Horizontal Database
The physical layout of data is column by column. Vertical ScalingScalingus be added, thereby adding more power to the PC. The physical layout of data is row by row. Thus horizontal scaling is achieved, thereby adding more equipment.
All data is stored in a single node. Only part data is stored in all nodes.
Multi-core scaling will be done. Single-core scaling done.
Example – Amazon Cloud Example – MongoDB

 5. What do you understand by Polyglot Persistence in NoSQL?

The term “Polyglot Persistence” is used to represent the notion that applications ought to be written in a variety of languages. As is common knowledge, difficulties can occur in every application. Therefore, when an application is written in various languages, those languages can be used to address or solve multiple issues. The term “polyglot persistence” describes this. Instead of encompassing all facets of a problem in a single language, choosing the appropriate language for that situation can be more beneficial. Therefore, this hybrid approach to Persistence is referred to as polyglot Persistence.
Polyglot Persistence suggests that database engineers/architects should determine how they want to manipulate the data and then choose the database technology that best suits their needs. This approach solves data storage efficiency problems, simplifies operations, and eliminates fragmentation.

Polygot persistence
Schematic representation of Polyglot Persistence. Note how data is fed from different sources (Source: src)

6. When should NoSQL be used over RDBMS?

You can utilize NoSQL if you seek key-value stores with extremely high-performance levels because ACID transactions are used in relational databases. The schema-based process will slow down the database performance once we employ this transaction.

Possible scenarios of potential usage of NoSQL are:

  • In situations of the need for multiple JOIN queries
  • For high-traffic websites
  • While using denormalized data

7. Explain the CAP theorem in NoSQL.

It is the most reliable of the three guarantees for a NoSQL database. CAP is the fundamental value of consistency, availability, and partition tolerance. The nodes will be working in tandem in the network. As a result, the entire functioning of the database will work faster.

8. What is understood by Database Sharding in NoSQL?

Database sharding in NoSQL refers to splitting the database according to NoSQL time-appropriate patterns. Data can be stored by sharding over numerous, possibly independent servers worldwide. A database administrator can readily retrieve this data from anywhere in the world with excellent data speed characteristics.

NoSQL
Schematic representation of Data Sharding in NoSQL (Source: src)

9. What are the ways to track data record relations in NoSQL?

The possible steps are as follows:

  • Embed all data into any user-object
  • Create the user-id credential
  • Using the login id, you will be needed to give the value of the comments with a list of comments.
  • Following these three steps will lead to the desired information retrieval.

10. Explain the BASE Characteristic of NoSQL.

The BASE model, which is a softer approach, is used by NoSQL. The BASE stands for Basically Available, Soft state, Eventual consistency.

  • Available: Assures the data’s accessibility. Any inquiry will receive a response (it can be a failure too).
  • Soft state: Over time, the system’s state might change.
  • Eventually Consistent – It assumes that once it stops accepting input, the system will finally achieve consistency.

NoSQL databases sacrifice the A, C, and D requirements for greater scalability.

Conclusion

Throughout the ten questions, we have covered the essential concepts of NoSQL as a DBMS. Key takeaways from today’s blog include –

  • The general idea of NoSQL and why it originated and came into popularity
  • The key features and different types of databases in NoSQL
  • Key concepts like Polyglot Persistence, CAP Theorem, Database Sharding
  • When you should be using NoSQL over the existing RDBMS
  • The BASE characteristics of NoSQL over ACID characteristics of RDMBS

If thoroughly well versed with the above ideas and questions will surely give you an edge in the interview. Hope you liked today’s topic of discussion and you managed to add new concepts to your existing knowledge. Wishing you great luck with your future goals and aspirations!

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

Neil D
Neil D

Neil is a research professional currently working on the development of AI agents. He has successfully contributed to various AI projects across different domains, with his works published in several high-impact, peer-reviewed journals. His research focuses on advancing the boundaries of artificial intelligence, and he is deeply committed to sharing knowledge through writing. Through his blogs, Neil strives to make complex AI concepts more accessible to professionals and enthusiasts alike.

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