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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

Top 10 AWS Redshift Interview Questions in 2022

S
Shikha Gupta Last Updated : 08 Aug, 2022
4 min read

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

Introduction

AWS Redshift is a powerful, petabyte-scale, highly managed cloud-based data warehousing solution. It processes and handles structured and unstructured data in exabytes (1018 bytes). The most common use cases of Redshift include large-scale data migration, log analysis, processing real-time analytics, joining multiple data sources, and many more.

Amazon Redshift - 11 Key Points to Remember

Source: https://www.blazeclan.com/blog/what-is-amazon-redshift-11-key-points-remember/

Redshift is an OLAP(Online Analytical Processing) database that gathers valuable insights from huge data. It is based on the PostgreSQL standard and can connect the JDBC client applications with ODBC connections. The properties like Massively Parallel Processing (MPP) technology, columnar data storage, and multiple encoding schemes for data compression provide Redshift with the strength of executing the humongous volume of data efficiently and at a faster speed. Like many other Amazon Web Services(AWS), Redshift can be deployed with just a few clicks and provides an easy-to-use interface.

This blog will discuss the frequently asked interview questions that might help you gain knowledge about Redshift and prepare you for the next interview.

RedShift Interview Questions

Q1: What is Redshift in AWS?

Amazon Web Service(AWS) Redshift is a fully managed big data warehouse service in the cloud that is rapid and potent enough to process and manage data in the range of exabytes. Redshift is built to handle large-scale data sets and database migrations by the company ParAccel (later acquired by Actian). It uses massive parallel processing (MPP) technology and provides a cost-effective and efficient data solution. The famous usage of Redshift is acquiring the latest insight for business and customers.

Q2: What are the benefits of using AWS Redshift?

The major benefits provided by AWS Redshift include:

  • In-built security with end-to-end encryption.

  • Multiple query support that provides significant query speed upgrades.

  • It provides an easy-to-use platform that is similar to MySQL and provides the usage of PostgreSQL, ODBC, and JDBC.

  • It offers Automated backup and fast scaling with fewer complications.

  • It is a cost-effective warehousing technique.

Q3: Why use an AWS Data Pipeline to load CSV into Redshift? And How?

AWS Data Pipeline facilitates the extraction and loading of CSV(Comma Separated Values) files. Using AWS Data Pipelines for CSV loading eliminates the stress of putting together a complex ETL system. It offers template activities to perform DML(data manipulation) tasks efficiently.

To load the CSV file, we must copy the CSV data from the host source and paste that into Redshift via RedshiftCopyActivity.

Q4: How to list tables in Amazon Redshift?

The ‘Show table’ keyword lists the tables in Amazon Redshift. It displays the table schema along with table and column constraints. Syntax:

SHOW TABLE [schema.]table_name

Q5: How are Amazon RDS, DynamoDB, and Redshift different?

Below are the major differences:

  1. Database Engine

The available Amazon RDS engines include Oracle, MySQL, SQL Server, PostgreSQL, etc., while the DynamoDB engine is NoSQL, and Amazon Redshift supports the Redshift(adapted PostgreSQL) as a database engine.

  1. Data Storage

RDS facilitates 6 terabytes per instance, Redshift supports 16 terabytes per instance, and DynamoDB provides unlimited storage.

  1. Major Usage

RDS is used for traditional databases, while Redshift is famous for data warehousing DynamoDB is the database for dynamically modified data.

  1. Multi-Availability Zone Replication

RDS acts as an additional service while Multi-AZ replication for Redshift is Manual and for DynamoDB is Built-in.

Q6: How far Redshift is better in performance as compared to other data warehouse technologies?

Amazon Redshift is the easiest and fastest cloud data warehouse which facilitates 3 times better price-performance than other data warehouses. Redshift offers fast query performance at a comparatively modest cost to firms where datasets ranging in size from gigabytes to exabytes.

Q7: How do we load data into Redshift?

Several methods are available to load data into Redshift, but the commonly used 3 methods are:

  1. The Copy command is used to load data into AWS Redshift.

  2. Use AWS services to load data into Redshift.

  3. Use the Insert command to load data into Redshift.

Q8: What is Redshift Spectrum? What data formats does Redshift Spectrum support?

Redshift Spectrum is released by AWS(Amazon Web Services) as a companion to Amazon Redshift. It uses Amazon Simple Storage Service (Amazon S3) to run SQL queries against the data available in a data lake. Redshift Spectrum facilitates the query processing against gigabytes to exabytes of unstructured data in Amazon S3, and no ETL or loading is required in this process. Redshift Spectrum is used to produce and optimize a query plan. Redshift Spectrum supports various structured and semi-structured data formats, including AVRO, TEXTFILE, RCFILE, PARQUET, SEQUENCE FILE, RegexSerDe, JSON, Geok, Ion, and ORC. Amazon suggests using columnar data formats like Apache PARQUET to improve performance and reduce cost.

Q9: How will the price of Amazon Redshift vary?

The Amazon Redshift pricing depends upon the type of node chosen by the customer to build his cluster. It mainly offers two types of nodes that differ in terms of storage and computation:

  1. Dense Compute Nodes

These optimized computing nodes offer RAM up to 244GB and SSDs up to 2.5 terabytes. The lowest spec price for dc2.larges varies from 0.25$ to 0.37$ per hour, and the highest spec price for dc2.8x varies from 4.8$ to 7$ per hour.

  1. Dense Storage Nodes

These nodes provide high storage capacity in two versions- a basic version(ds2.xlarge) with up to 2 TB HDDs and a higher version(ds2.8xlarge) with up to 16 TB HDDs. The cost of the basic version varies from 0.85$ to1.4$ per hour, and for the higher version is 6$ to 11$.

Q10: What are the limitations of Amazon Redshift?

  1. It cannot be used as a live app database due to the slow processing speed of web apps.

  2. There is no way to enforce uniqueness in AWS Redshift on inserted data.

  3. It supports the parallel loading only for Amazon EMR, relational DynamoDB, and Amazon S3.

Conclusion

In this blog, we have seen some of the important interview questions that can be asked in AWS Redshift interviews. We had discussed a basic combination of theoretical and practical questions, but that’s not it. This blog will give you a basic understanding of what type of questions you expect. However, it’s recommended apart from these Redshift interview questions; you also practice the SQL commands to develop more understanding of data processing and transformations. The key takeaways from the above AWS Redshift questions are:

  • We learned about what is Redshift in AWS and how it is beneficial for the user.

  • We have seen how can we load CSV in Redshift using the data pipeline.

  • We understand how Redshift differs from RDS and DynamoDB.

  • We got an understanding of how we can show tables.

  • We have also discussed the basics of Redshift Spectrum and the limitations of Redshift.

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

S
Shikha Gupta

My full name is Shikha Gupta , pursuing B.tech in computer science from Banasthali vidhyapeeth Rajasthan.
I am from East Champaran in Bihar.
My area of interest ,Deep learning,NLP,Java,Data Structure,DBMS and many more.

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