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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

AWS Redshift: Cloud Data Warehouse Service

Prateek Majumder
Prateek Majumder Last Updated : 27 Apr, 2022
5 min read

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

Introduction

Amazon’s Redshift Database is a cloud-based large data warehousing solution. Companies may store petabytes of data in easy-to-access “clusters” that can be searched in parallel using the platform’s storage system.

The datasets range in size from a few 100 megabytes to a petabyte. The first step in building a data warehouse is to launch a collection of computational resources known as nodes, which are arranged into clusters. We can then proceed to process our requests.

How does Redshift work?

Amazon Redshift analyses structured and semi-structured data across data warehouses, operational databases, and data lakes with SQL leveraging AWS-designed technology and machine learning to provide the best pricing performance at any scale.

AWS Redshift

Source: https://aws.amazon.com/redshift/

Administrative operations such as configuration, maintenance backups, and security are totally automated in every Redshift data warehouse. Because of its modular node design, Redshift is intended for massive data and can scale quickly. Redshift’s multi-layered structure allows numerous requests to be performed simultaneously, minimising wait times.

Furthermore, Redshift clusters may be further separated into slices, allowing for more detailed insights into data sets. Redshift databases also entirely use Amazon’s cloud server architecture, including access to Amazon S3 for data backup.

We can use Amazon Redshift to access data with a variety of conventional, cloud-native, and containerized serverless web services and event-driven applications. The Amazon Redshift Data API makes data access, intake, and egress easier from AWS SDK-supported programming languages and platforms like Python, Go, Java, Node.js, PHP, Ruby, and C++. The Data API eliminates the requirement for drivers to be configured and database connections to be managed. Instead, we can use the Data API to access a secured API endpoint to perform SQL queries on an Amazon Redshift cluster.

Features and Characteristics of Redshift

  • VPC support allows users to run Redshift within a VPC and control cluster access using the virtual networking environment.
  • Encryption Redshift data may be encrypted and customised when tables are being created.
  • SSL encryption is used to encrypt client-to-Redshift communications.
  • The number of nodes in the Redshift data warehouse may be quickly scaled as needed with a few easy clicks. It also enables the expansion of storage capacity without sacrificing performance.
  • Amazon Redshift is a less expensive alternative to standard data warehousing techniques. There are no upfront expenses, no long-term obligations, and a pricing system that is based on demand.

Why is Redshift unique?

Redshift is a column-oriented OLAP (Online Analytical Processing) database. It is built using the PostgreSQL 8.0.2 database. This implies that Redshift can be used with standard SQL queries. However, this is not what distinguishes it from other services. Redshift distinguishes itself by responding quickly to queries conducted on a big database containing exabytes of data.

Redshift, like any other clustered or distributed database model, operates in a clustered paradigm with a leader node and numerous working nodes. Because it is based on Postgres, it has many similarities with Postgres, notably the query language, which is nearly identical to Structured Query Language (SQL). This Redshift version allows you to create practically all important database objects, including databases, tables, views, and even stored procedures. In this post, we will look at how to set up and run your first Redshift cluster on AWS.

The Massively Parallel Processing architecture, or MPP, enables fast querying. ParAccel invented the technology. MPP uses a high number of computer processors working in parallel to do the necessary computations. Processes can sometimes be delivered by processors distributed across different servers.

Amazon Redshift analyses user workloads and use advanced algorithms to modify the physical structure of data in order to increase query performance. Automatic Table Optimization chooses the optimum sort and distribution keys to improve performance for the workload on the cluster. If Amazon Redshift believes that using a key will enhance cluster performance, tables will be changed automatically without the need for administrator interaction. The added capabilities Automatic Vacuum Delete, Automatic Table Sort, and Automatic Analyze minimise the need for human Redshift cluster maintenance and tuning to get the optimal performance for new clusters and production workloads.

Amazon offers data encryption for any aspect of the Redshift process. You, as the user, may choose which actions require encryption and which do not. Data encryption adds an extra degree of protection.

PostgreSQL is the foundation of Redshift. It is compatible with all SQL queries. We may also use any SQL, ETL (Extract, Transform, Load), and Business Intelligence (BI) tools that we are acquainted with. It is not necessary to use the tools offered by Amazon.

There would be several ways to query data with the same parameters for a huge data collection. The data usage levels of the various instructions will vary. AWS Redshift gives you the tools and data you need to optimise your searches. It will also automatically make suggestions for improving the database. These can be used for a more efficient procedure that uses fewer resources.

AWS Redshift

Source: https://aws.amazon.com/redshift/

Amazon Redshift data sharing enables us to extend Amazon Redshift’s ease of use, performance, and cost benefits from a single cluster to multi-cluster deployments while exchanging data. Data sharing allows Redshift clusters to access data instantly, granularly, and quickly without having to copy or relocate it. Data sharing allows the users to have real-time access to data, ensuring that they view the most up-to-date and consistent information as it is updated in the data warehouse.

Even with shifting workloads or concurrent user activity, Amazon Redshift’s advanced machine learning capabilities ensure great throughput and performance. To dynamically manage speed and concurrency while also helping you prioritise your business-critical workloads, Amazon Redshift employs advanced algorithms to forecast and classify incoming queries based on their run times and resource requirements. Brief query acceleration (SQA) routes short queries from dashboard apps to an express queue for quick processing, rather than being slowed down by big queries. Automatic workload management (WLM) uses machine learning to optimise query speed by dynamically managing memory and parallelism. Furthermore, even when hundreds of questions are filed, you can now quickly adjust the priority of your most critical requests.

Limitations of Redshift

Redshift is a great data warehousing platform. The entire service has been adjusted and optimised for one sort of workload and analytics processing. However, it has some limitations.

  • When Redshift is utilised for data warehousing, indexing becomes a challenge. To index and store data, Redshift employs distribution and sort keys. To work on the database, you’ll need to understand the ideas underlying the keys. AWS does not provide a way for changing or managing keys with only rudimentary expertise.
  • AWS Redshift does not include any tools or methods for ensuring data uniqueness. There will be duplicate data points when moving overlapping data from several sources to Redshift.
  • Redshift is utilised when a large amount of data has to be stored or processed. It will at the very least be in the petabyte range. Bandwidth becomes an issue at this point. Before you can begin the project, we’ll need to transfer these data to AWS locations.

Conclusion

Every day, more data is generated, stored and collected. Redshift is a safeguard against the growing volume of data and the increasing analytical complexity. It may be utilised to develop and build long-lasting infrastructure.

Some important highlights about Amazon Redshift are:

  • Amazon Redshift has a Massively Parallel Processing Architecture. The  MPP Architecture enables Redshift to distribute and parallelize queries across multiple nodes.
  • Apart from queries, the MPP architecture also enables parallel operations for data loads, backups and restores.
  • Database Constraints and Indexes are neither enforced nor supported by Redshift.
  • Redshift, like all other AWS services, comes with a variety of security protections.
  • Although Amazon’s tools lessen the demand for a full-time database administrator, it does not eliminate the necessity for one.
  • In an environment with frequent deletes, Amazon Redshift is known to struggle with storage management.

Furthermore, Redshift provides best-in-class performance for a fraction of the price of competitors. Using Redshift seems to be a very good value proposition for any small firm or startup who wants their web app up and running and get good results. The importance of Redshift lies in the fact that it is very easy to use and versatile.

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

Prateek Majumder
Prateek Majumder

Prateek is a dynamic professional with a strong foundation in Artificial Intelligence and Data Science, currently pursuing his PGP at Jio Institute. He holds a Bachelor's degree in Electrical Engineering and has hands-on experience as a System Engineer at TCS Digital, where he excelled in API management and data integration. Prateek also has a background in product marketing and analytics from his time with start-ups like AppleX and Milkie Way, Inc., where he was involved in growth campaigns and technical blog management. Recognized for his structured thinking and problem-solving abilities, he has received accolades like the Dr. Sudarshan Chakraborty Award for Best Student Performance. Fluent in multiple languages and passionate about technology, Prateek continues to expand his expertise in the rapidly evolving AI and tech landscape.

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