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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

Top 6 Amazon Athena Interview Questions

Hari Bhutanadhu
Hari Bhutanadhu Last Updated : 09 Mar, 2023
8 min read

Introduction

Amazon Athena is an interactive query tool supplied by Amazon Web Services (AWS) that allows you to use conventional SQL queries to evaluate data stored in Amazon S3. Athena is a serverless service. Thus there are no servers to operate, and you pay for the queries you perform. Athena is built on Presto, an open-source distributed SQL query engine, and supports various data formats such as CSV, JSON, ORC, and Parquet. Athena allows you to instantly query and analyze massive datasets stored in S3 without having to set up costly ETL procedures or manage infrastructure, making it an efficient and cost-effective data analysis solution.

Athena uses the Amazon Glue Data Catalog, a managed metadata catalog that holds table definitions and schema information, allowing data to be queried without the need to set up or administer a database. Athena may be used for ad-hoc querying, data analysis, and BI reporting, and it can be integrated with other AWS services, such as Amazon QuickSight and AWS Glue. Overall, Amazon Athena provides a simple and powerful approach to analyzing data in S3 without sophisticated data infrastructure setup and management.

Source: webscraper.io
                                                                               Source: webscraper.io

Learning Objectives

  1. We will go through the fundamentals of Amazon Athena and how it works.
  2. Next, we’ll review the advantages of utilizing Amazon Athena versus typical relational databases.
  3. We will learn several data types offered by Amazon Athena.
  4. We’ll examine how AWS Glue Data Catalog works and relates to Amazon Athena.
  5. Finally, we will cover how to optimize query performance in Amazon Athena and secure data stored in Amazon S3 and queried using Amazon Athena.

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

Table of Contents

Q1. What Exactly is Athena, and How does it Function?

Amazon Athena is an Amazon Web Services (AWS) query service that allows you to evaluate data stored in Amazon S3 using regular SQL queries. Athena is a serverless service. Thus there are no servers to operate, and you simply pay for the queries you perform. To use Amazon Athena, create tables in Athena that refer to data in Amazon S3. You can construct tables in Athena that point directly to your S3 data or utilize the Amazon Glue Data Catalog to define external tables that indicate your S3 data. When you’ve defined your tables, you can use the Athena Query Editor or any other standard SQL client to perform SQL queries against them.

When you perform a query in Amazon Athena, the service scales up the resources required to conduct the query and provides the results to you. Athena utilizes Presto, an open-source distributed SQL query engine, to perform your requests. Presto breaks down your query into small jobs spread across a cluster of Amazon EC2 servers. Each instance executes a subset of the query, and the results are merged to get the final output. CSV, JSON, ORC, and Parquet are among the data formats supported by Amazon Athena. You can also use Athena to analyze structured data in relational databases by crawling your database using Amazon Glue and creating a table definition that refers to your data.

Overall, Amazon Athena provides a simple and powerful approach to analyzing data in S3 without sophisticated data infrastructure setup and management. Users may evaluate data stored in various formats using standard SQL queries, and the serverless aspect of the service makes it simple to expand and improve query performance.

Source: aws.amazon.com

Source: aws.amazon.com

Q2. What Disadvantages does Athena have Over Standard Relational Databases?

Amazon Athena has various advantages over standard relational databases:

Serverless:  It is a serverless service requiring no servers or infrastructure. This removes the need for complex database maintenance duties like scalability, patching, and backups, allowing you to concentrate on data analysis.

Cost-effective:  It charges you only for the queries you perform, with no setup fees or minimum fees. Because you pay for the resources you use, it is a cost-effective alternative for ad-hoc data analysis. Because you pay for the resources you use, it is a cost-effective alternative for ad-hoc data analysis.

Scalability:  It grows automatically to accommodate massive datasets and high query volumes. This means you can examine petabytes of data without requiring or managing new resources.

Flexibility: It supports various data formats, including CSV, JSON, ORC, and Parquet. This enables simple data analysis from multiple sources without pre-processing or transformation.

Easy Integration: It interfaces easily with other AWS services, such as AWS Glue and Amazon QuickSight, making constructing end-to-end data analytics solutions simple.

It provides a versatile, scalable, and cost-effective approach to analyzing data stored in Amazon S3 using standard SQL queries without requiring complicated database administration or infrastructure management.

Source: www.slideshare.net
                                                                                      Source: www.slideshare.net

Q3.What are the Many Data Formats that Athena Supports?

It supports several data formats, including:

  1. CSV (Comma Separated Values): A basic text-based file format for storing tabular data.
  2. JSON (JavaScript Object Notation): A simple, easy-to-read data transfer format.
  3. ORC (Optimized Row Columnar): A high-performance columnar storage format for Hadoop data processing.
  4. Parquet: A columnar storage format developed to increase query speed for huge collections.
  5. Avro: A binary data format that is small and quick and is intended for efficient data serialization and deserialization.
  6. Apache HBase: A NoSQL database designed for fast read/write access to massive datasets.
  7. Amazon CloudFront logs: Amazon CloudFront logs include extensive information on user content requests.

It also supports data saved in Amazon S3 in compressed forms like gzip and Snappy. You may write your custom SerDe (Serializer/Deserializer) to read data in additional formats. Overall, the vast range of supported data formats makes it simple to evaluate data saved in diverse forms in Amazon Athena using typical SQL queries.

Q4. What is the AWS Glue Data Catalog, and How Does it Connect to Athena?

The Amazon Glue Data Catalog is a managed metadata repository that maintains data source and schema information. It is a common repository for storing and maintaining metadata for numerous AWS services, including Athena, such as table definitions, partition information, and schema versions. As you crawl your data sources with Amazon Glue, it automatically extracts information and builds table definitions in the AWS Glue Data Catalog. It may utilize these table definitions to construct external tables that allow you to query data stored in Amazon S3 using regular SQL queries.It manages metadata about data sources and schemas using the AWS Glue Data Catalog. When you execute a query in Athena, it leverages the AWS Glue Data Catalog table definitions to understand the structure of the data, allowing it to optimize query execution and increase performance. Data versioning is also supported by the Amazon Glue Data Catalog, allowing you to trace changes to data sources and schemas across time. This ensures that your queries always use the correct schema and data definitions.

Overall, the AWS Glue Data Catalog is an essential component of the AWS analytics stack, serving as a centralized repository for metadata management across different AWS services, including Amazon Athena.

Source: aws.amazon.com
                                                                                 Source: aws.amazon.com

Q5. How can Query Performance in Athena be Improved?

With Amazon Athena, there are numerous approaches to improve query performance:

  1. Partitioning: To decrease the quantity of data scanned by your queries, partition your data depending on one or more columns. You may dramatically increase query speed by splitting your data and limiting the amount of data examined by a query to only.
  2. Compression: You may compress your data on Amazon S3 using a supported compression format like Snappy or GZIP. The reduction can increase query speed by reducing the quantity of data scanned by your queries.
  3. Columnar storage: By lowering the quantity of data scanned and enhancing data compression, you may improve query speed by storing your data in a columnar format like ORC or Parquet.
  4. Query tuning: You may improve the performance of your queries by using suitable query syntaxes, such as choosing just the required columns and eliminating superfluous joins and subqueries. You may also improve query speed by utilizing appropriate data types, such as integer or date data types, and avoiding costly operations, such as regular expressions.
  5. Workgroup management: Using Amazon Athena’s workgroup management functionality, you may improve query speed by allocating various resources, like memory and CPU, to distinct workgroups based on their priority and workload. This can guarantee that queries from high-priority workloads get the help they need to finish fast.

To guarantee that queries run fast and efficiently, optimizing query performance in Amazon Athena needs a mix of data management approaches, query optimization, and workgroup management.

Source : www.upsolver.com
                                                                                    Source: www.upsolver.com

Q6. How can Data Stored in Amazon S3 and Queried Using Athena be Secured?

There are numerous methods for protecting data stored in Amazon S3 and queried using Amazon Athena:

  1. Encryption: You may encrypt your data at rest in Amazon S3 using server-side encryption. To help you safeguard your data, Amazon S3 offers multiple encryption solutions, including AWS KMS-managed keys and customer-managed keys. You may also encrypt your data before uploading it to Amazon S3 using client-side encryption.
  2. Access Control: You may manage who has access to your Amazon S3 data by using access control tools such as bucket policies and object ACLs. AWS Identity and Access Management (IAM) may also govern access to Amazon Athena, enabling you to designate who can perform queries and access query results.
  3. VPC Endpoints: AWS Identity and Access Management (IAM) may also govern access to Amazon Athena, enabling you to designate who can perform queries and access query results.  Amazon VPC endpoints allow you to securely access Amazon S3 and Athena through a private network connection without exposing your data to the public internet. This can assist in increasing data security and prevent illegal access.
  4. Encryption in Transit: Encrypt data as it travels between Amazon S3, Athena, and your application using encryption in transit. This is possible because the SSL/TLS protocols encrypt data as it travels over the network.
  5. Auditing and Logging: AWS CloudTrail can audit and log all API calls made to Amazon S3 and Athena. This allows you to monitor data access and identify unwanted access or activity.

Overall, safeguarding data stored in Amazon S3 and queried using Amazon Athena necessitates a mix of encryption, access control, network security, and auditing to secure your data from illegal access and exploitation.

Conclusion

To summarise, It is a serverless, interactive query tool that simplifies data analysis in Amazon S3 using standard SQL. It enables you to query your data fast and simply without the need for complicated ETL processes or data warehouse systems. Furthermore, it has advantages over typical relational databases, such as scalability, cost-effectiveness, and flexibility.

Key takeaways of this article:

  1. Initially, we examined it, a powerful and versatile tool for accessing data stored in Amazon S3 using conventional SQL.
  2. After that, we explored various advantages over traditional relational databases, including scalability, cost-effectiveness, and flexibility.
  3. It supports numerous data formats, including CSV, JSON, and Apache Parquet.
  4. Finally, We talked about optimizing query performance in Amazon Athena by partitioning your data, compressing it, and using columnar formats like Parquet, as well as how to secure data stored in Amazon S3 and queried using Amazon Athena by using encryption, access control, network security, and auditing.

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

Hari Bhutanadhu
Hari Bhutanadhu

My self Bhutanadhu Hari, 2023 Graduated from Indian Institute of Technology Jodhpur ( IITJ ) . I am interested in Web Development and Machine Learning and most passionate about exploring Artificial Intelligence.

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