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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

How is AWS Athena different from other databases?

Gitesh Dhore
Gitesh Dhore Last Updated : 25 Jul, 2022
6 min read

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

Introduction

Amazon Athena is an interactive query service based on open-source Apache Presto that allows you to analyze data stored in Amazon S3 using ANSI SQL directly. In addition, it is serverless, so there is no infrastructure to manage and maintain, and you only pay for the queries you run.

Image Source – openbridge.com

 

To start with Athena, you need to define the schema of your data stored in Amazon S3; you’re ready to start querying it with SQL. The schema is determined using the Amazon Glue Data Catalog, which allows you to create a unified metadata repository across multiple services.
It can be used alongside or instead of traditional databases depending on the specific business and technical scenario. But, first, it is essential to understand the differences and why you would choose one over the other.

 Differentiating Athena from Database and Warehouse

Athena works more like a query engine than a particular database. This means that:

  • Compute and storage are decoupled: Databases store data at rest and provide the resources needed to perform queries and calculations. Each of these comes with direct and indirect overhead costs. It does not store data – instead, storage is managed entirely on Amazon S3. The Athena query service is fully managed, so resources are automatically allocated by AWS as needed to execute a query.
  • No DML interface: No need to model data with Athena. I/O is the bottleneck of virtually every database, but it’s not a problem with Athena. And since you don’t have to waste I/O bandwidth on data modelling, you can focus all computing resources on query processing.
Anazon athena

 

Advantages of  Using Athena

Serverless Design Reduces IT Overhead: Amazon Athena is serverless, meaning there is no user-side infrastructure to manage or configure. Using Athena is as simple as defining a query, and you only pay for the questions you run. As a result, there are no additional IT costs and no clusters to manage.

  • Based on SQL: You can use Athena to run SQL queries against the desired table that is configured in the Glue data catalogue or data sources that you can connect to using the Athena Query Federation SDK. For users who already know SQL, there is no learning curve to get started.
  • Open architecture (no vendor lock-in): Athena enables open access to data rather than lock-in to a specific tool or technology. This manifests itself in various ways;
  • Ubiquitous Access: Because your data is stored in an S3 bucket and the schema is defined in the Glue Data Catalog, you can switch between query engines that can read from these sources without redefining the schema or creating a separate copy of the data.
  • Separated storage and computing resources: Athena has a complete separation of computing and memory resources. Data is stored in your Amazon S3 account, while Amazon Web Services provide Athena computation as a shared resource among all Athena users.
  • Open file formats: Unlike many high-performance databases, Athena does not use a proprietary file format but supports standard open source formats such as Apache Parquet, ORC, CSV, and JSON.
  • Low cost: Athena’s pricing model is based on terabytes of scanned data. You can control and keep costs down by checking only the data you need to answer a specific query (this can be done using data splitting – see below).
  • Access to all your data: Most organizations process only 30 to 35 percent of their data into a traditional data warehouse due to the high operational and infrastructure costs of constantly resizing database clusters. Because this storage costs a fraction of what you would pay to keep the same data in a data warehouse, you can handle larger volumes of data without worry.
  • Custom Connectors: Amazon Athena lets you run SQL queries across multiple data sources, which can drive various business intelligence and analytics processes. You can use JDBC to connect Athena with BI and machine learning tools.
data source

Image source – https://www.sqlshack.com/an-introduction-to-aws-athena/

Limitations of Athena

No built-in insert/update/delete operations: Because Athene is a query engine with no DML interface, upsets can be difficult.
  • The optimization is limited to queries: You can optimize your questions, not your data. However, your data is already stored in Amazon S3; performing transformations to use Athena may affect other users using the exact information for other purposes.
  • Multi-tenancy means pooled resources: All Athena users receive a similar SLA for queries at any time. In other words, the entire global user base is “competing” for the same resources – and although AWS provides more as needed, this could mean that query performance fluctuates depending on other people’s usage.
  • No indexing: Indexes are integrated into traditional databases but do not exist in Athena. This makes joining large tables a demanding operation that increases the load on Athena and negatively impacts performance. For example, running a query by key requires scanning all the data and searching for the desired key in the result list. This is solved using Upsolver lookup tables.
  • Partitioning: Efficient queries in Athena require partitioning of the data. Maintaining the number of partitions in the park that meet your performance needs is essential. Every 500 divisions scanned will add 1 second to your query.

Other Products Required with Athena

Athena is never a standalone product but rather always part of a package that includes:
metadata

 

  • Amazon S3: Athena queries run directly on Amazon S3, so this is where your data will be stored.
  • Glue Data Catalog: A centralized managed schema that allows you to replace or augment Athena with other services as needed (for example, with Amazon Redshift Spectrum).
  • ETL Tools: While Athena can run almost any query out of the box, reducing costs and improving performance requires following a set of performance tuning best practices. The traditional way is to use Spark, which can process large volumes of unstructured data; however, this option requires considerable coding knowledge. Some solutions offer managed Spark as a service that simplifies the infrastructure aspects but doesn’t remove the coding overhead.

Use Case

Athena helps analyze unstructured, semi-structured, and structured data stored in Amazon S3. Data can be stored in CSV, JSON or columnar formats such as Apache Parquet and Apache ORC. It can also be used to run queries using ANSI SQL, and this does not require the user to aggregate or load data into Athena.
It can be integrated with Amazon Quick Sight for data visualization purposes to help generate reports and explore data using business intelligence tools such as SQL clients that interface with JDBC or ODBC driver.
Athena can also be integrated with the AWS Glue Data Catalog. AWS Data Catalog provides persistent metadata storage for user data in Amazon S3. This way, tables can be created, and data can be queried in Athena, all based on a centralized metadata repository available throughout the user account. It can also be integrated with ETL (Extract, Transform, Load) and data discovery features included in the AWS glue catalog.

Conclusion

Athena is a service offered by Amazon that is an interactive query service. Athena makes it easy for the user to directly analyze data in Amazon S3 (Simple Storage Service) using standard SQL. For example, in the Amazon Management Console, it can be set to point to where data is stored in Amazon S3 with a few clicks of a button. SQL can then be used to run ad-hoc queries, bringing the result to the user in seconds.

  • It does not store data. Instead, storage is managed entirely on Amazon S3. The Athena query service is fully managed, so resources are automatically allocated by AWS as needed to execute a query.
  • Because your data is stored in an S3 bucket and the schema is defined in the Glue Data Catalog, you can switch between query engines that can read from these sources without redefining the schema or creating a separate copy of the data.
  • Indexes are integrated into traditional databases but do not exist in Athena. This makes joining large tables a demanding operation that increases its load and negatively impacts performance. For example, running a query by key requires scanning all the data and searching for the desired key in the result list. This is solved using Upsolver lookup tables.

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Gitesh Dhore
Gitesh Dhore

I am a Machine Learning Enthusiast. Done some Industry level projects on Data Science and Machine Learning. Have Certifications in Python and ML from trusted sources like data camp and Skills vertex. My Goal in life is to perceive a career in Data Industry.

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