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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
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

Database Errors: Dark Side and Lessons Learned

Swapnil Vishwakarma
Swapnil Vishwakarma Last Updated : 29 Dec, 2022
6 min read

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

Introduction

Database errors
Source: Image by Pexels from Pixabay

Have you ever wondered about the dark side of databases? We, software developers, rely on databases to store and manage important data for our applications. But as we all know, things can go wrong with databases – and when they do, the consequences can be catastrophic.

In this blog, we will share some horror stories of real-life database disasters and the lessons that can be learned from them. We will also discuss common pitfalls that can lead to database problems and provide best practices for avoiding them.

By the end of the article, you will better understand the potential risks associated with databases and how to mitigate them. You will also learn valuable strategies for maintaining your database systems’ integrity, performance, and security.

Horror stories Relating to Database Errors

Database errors
Source: https://www.pexels.com/photo/greyscale-photo-of-day-of-the-dead-corpse-bride-604694/

1. Database security breaches occur when unauthorized individuals gain access to a database and steal or manipulate its data. This can have serious consequences, such as the loss of sensitive information, financial loss, and damage to a company’s reputation. One example of a database security breach occurred at Yahoo in 2013 when hackers accessed the company’s database and stole the personal information of all 3 billion Yahoo users. This resulted in a massive data breach and significant financial losses for the company.

  • The lesson to be learned from this disaster is the importance of securing your database. This includes implementing strong passwords, regularly updating software, and implementing security measures such as firewalls and encryption. Taking these precautions can help prevent unauthorized access to your database.

2. In 2021, a ransomware attack on Colonial Pipeline, a major US fuel pipeline operator, caused the company to shut down its operations and suffer millions of dollars in losses. The attack targeted the company’s database, which was used to control fuel flow through the pipeline. This disaster highlights the importance of protecting against ransomware attacks, which can seriously affect businesses and organizations.

  • The ransomware attack on Colonial Pipeline highlights the importance of protecting against ransomware attacks. This includes implementing strong passwords, regularly updating software, and implementing security measures such as firewalls and encryption. It’s also important to have robust backups in place to recover your data quickly if an attack occurs.

3. In 2020, a database error at the UK’s National Health Service (NHS) caused the cancellation of thousands of appointments and procedures, leading to significant disruption for patients and healthcare providers. The error occurred when a database that stored patient information was incorrectly configured, resulting in data loss. This disaster highlights the importance of properly configuring and maintaining databases to avoid errors and disruptions.

  • The database error at the UK’s National Health Service (NHS) highlights the importance of properly configuring and maintaining databases. This includes regularly checking for and fixing any issues and implementing measures to prevent corruption, such as using redundant hardware and implementing backup power systems. By paying attention to the health and performance of your databases, you can help avoid errors and disruptions.

4. In 2018, a software bug at Delta Air Lines caused a system-wide outage, resulting in hundreds of flight cancellations and delays. The bug affected the company’s database, which stores flight schedules and other important information. This disaster highlights the importance of testing software thoroughly to ensure that it is reliable and free of bugs.

  • The software bug at Delta Air Lines highlights the importance of testing software thoroughly to ensure that it is reliable and free of bugs. This includes conducting both unit and integration testing and implementing a robust software development process that includes thorough quality assurance. By thoroughly testing your software, you can help prevent disruptions and ensure that it performs as expected.

These are just a few examples of database disasters that can occur and the lessons that can be learned from them. By taking steps to prevent data loss, corruption, and security breaches, you can help ensure the integrity and security of your database systems.

Common Pitfalls

Database errors
Source: Photo by Sigmund on Unsplash

Here are some common pitfalls that can lead to database problems:

  • Inadequate backups: One common pitfall is neglecting to back up your database regularly. This can lead to data loss if a disaster occurs, such as a hardware failure or a software bug. It’s important to regularly back up your database and test your backups regularly to ensure they are working properly.
  • Poor performance tuning: Another pitfall is neglecting to optimize the performance of your database. This can lead to slow query times, poor user experience, and increased load on your database server. It’s important to monitor your database’s performance regularly and optimize it as needed. This can include indexing data, optimizing queries, and properly sizing your database server.
  • Insecure configurations: A third pitfall is neglecting to secure your database. This can lead to security breaches, such as unauthorized access to sensitive data. Implementing security measures such as strong passwords, encryption, and firewalls is important to protect your database from unauthorized access. It’s also important to regularly update your database software to ensure that it includes the latest security patches.

By avoiding these pitfalls and following best practices for database management, you can help prevent database problems and maintain your database systems’ integrity, performance, and security.

Best Practices to Avoid Database Errors

Databases errors
Source: https://www.pexels.com/photo/unrecognizable-man-showing-thumb-up-on-city-street-6140706/

Here are some best practices for avoiding database issues and maintaining the integrity, performance, and security of a database system:

  • Regularly back up your database: One of the most important best practices is to back up your database regularly. This will allow you to recover your data in a disaster, such as a hardware failure or a software bug. It’s important to test your backups regularly to ensure that they are working properly.
  • Optimize database performance: Another best practice is optimizing your database’s performance. This can include indexing data, optimizing queries, and properly sizing your database server. By paying attention to the performance of your database, you can ensure that it is fast and reliable, which will improve the user experience and reduce the load on your database server.
  • Secure your database: A third best practice is to secure your database. This includes implementing strong passwords, encryption, and firewalls to protect against unauthorized access. It’s also important to regularly update your database software to ensure that it includes the latest security patches.
  • Monitor your database: A fourth best practice is to regularly monitor your database. This includes monitoring the performance of your database, checking for errors and issues, and keeping track of any changes made to the database. By monitoring your database, you can quickly identify and fix any problems.

By following these best practices and being proactive in managing your database, you can help avoid database errors and maintain your database system’s integrity, performance, and security.

Conclusion

This blog explores the dark side of databases and the potential consequences of misusing or neglecting them. We shared some horror stories of real-life database disasters, such as data loss, corruption, and security breaches, and the lessons that can be learned from them. We also discussed common pitfalls that can lead to database problems, such as inadequate backups, poor performance tuning, and insecure configurations.

Overall, the key takeaways from this blog are the importance of following best practices for database management and being proactive in avoiding database errors. Some practical tips for readers to apply in their own work with databases include:

  • Regularly backing up your database
  • Optimizing the performance of your database
  • Securing your database
  • Monitoring your database for errors and issues

By following these tips and staying vigilant in your work with databases, you can help prevent database disasters and maintain your database systems’ integrity, performance, and security.

Thanks for Reading!🤗

If you liked this blog, consider following me on Analytics Vidhya, Medium, GitHub, and LinkedIn.

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

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Swapnil Vishwakarma
Swapnil Vishwakarma

Hello there! 👋🏻 My name is Swapnil Vishwakarma, and I'm delighted to meet you! 🏄‍♂️

I've had some fantastic experiences in my journey so far! I worked as a Data Science Intern at a start-up called Data Glacier, where I had the opportunity to delve into the fascinating world of data. I also had the chance to be a Python Developer Intern at Infigon Futures, where I honed my programming skills. Additionally, I worked as a research assistant at my college, focusing on exciting applications of Artificial Intelligence. ⚗️👨‍🔬

During the lockdown, I discovered my passion for Machine Learning, and I eagerly pursued a course on Machine Learning offered by Stanford University through Coursera. Completing that course empowered me to apply my newfound knowledge in real-world settings through internships. Currently, I'm proud to be an AWS Community Builder, where I actively engage with the AWS community, share knowledge, and stay up to date with the latest advancements in cloud computing.

Aside from my professional endeavors, I have a few hobbies that bring me joy. I love swaying to the beats of Punjabi songs, as they uplift my spirits and fill me with energy! 🎵 I also find solace in sketching and enjoy immersing myself in captivating books, although I wouldn't consider myself a bookworm. 🐛

Feel free to ask me anything or engage in a friendly conversation! I'm here to assist you in English. 😊

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