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6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
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Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
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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
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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
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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
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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
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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

Creating and Managing DynamoDB Tables using AWS CLI

Ansaba
Ansaba Last Updated : 08 Dec, 2022
5 min read

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

DynamoDB
Source: https://rockset.com/blog/5-use-cases-for-dynamodb/

Introduction

The AWS Command Line Interface (CLI) is a centralized management tool for managing AWS services. Through CLI, users can control services manually or automate them with powerful scripts. With this one tool, it can handle multiple AWS services from the command line. Users can use the AWS CLI for ad hoc operations, like creating, deleting, describing tables, and other functions in DynamoDB using several commands. CLI gives access to the public AWS services APIs and offers customization for several services. This article covers how the AWS NoSQL database, DynamoDB, can be managed through AWS CLI, including creating and managing DynamoDB tables.

Overview of DynamoDB

Amazon DynamoDB is a fully managed AWS NoSQL database service best suited for high-performance queries on large datasets. It delivers consistent and fast performance for all applications without downtime or performance degradation.

Key features:

DynamoDB

Source: https://www.cloudinfonow.com/amazon-dynamodb-pricing/

Why DynamoDB?

  • Automatic partitioning and SSD-based storage.
  • No need to worry about patching and updating databases.
  • Supports cross-region replication, which helps to access data globally.
  • Offers on-demand backup features with long-term retention and archiving.

Use cases:

  • Analytics:
    It stores metrics from multiple data sources, like IoT Sensors, Web Applications, etc. The Time to Live (TTL) feature allows users to control data efficiently.
  • Media and Entertainment:
    It processes and performs complex analytics to identify the likes and dislikes of its customers and other users’ preferences in combination with big data. Also, it scales throughput and concurrency for media and entertainment workloads like real-time video streaming and interactive content,
  • Online Gaming:
    It helps to store and maintain Analytical Data, Player Data, Leaderboards, and Player Session Records with ease while providing better performance.

Steps for the Creation and Management of DynamoDB Tables using AWS CLI

To create and manage tables in DynamoDB, users have to follow the following steps:

1. AWS CLI Installation
2. Configure and Set CLI credentials
3. Creation of tables in the CLI using the create-table command
4. Management of Tables and Attributes

Step 1: AWS CLI Installation

AWS CLI can be installed on Windows, macOS, or Linux operating systems. Amazon Linux AMI does not need manual installation as it already includes AWS CLI in its OS distribution.
Linux OS :
Use the following commands to install the latest version of AWS CLI on Linux x86 (64-bit).
curl "https://awscli.amazonaws.com/awscli-exe-linux-x86_64.zip" -o "awscliv2.zip"
unzip awscliv2.zip
sudo ./aws/install
macOS:
First, Install Homebrew on macOS, and then use the following commands to install the latest version of AWS CLI.
brew install awscli
To verify credentials, the user can use the following command:
aws sts get-caller-identity
Windows:
The latest Windows installation package can be downloaded and installed from the following website:
Another way to install CLI is by running the following command:
msiexec.exe /i https://awscli.amazonaws.com/AWSCLIV2.msi
To confirm CLI installation, the user can run the following command:
aws --version

Step 2: Configure and Set CLI credentials

After installation, configure it with your credentials through the following command:
aws configure
This command asks you to enter details, including the Access Key, Secret Key, Region, etc.

Step 3: Creation of tables in the CLI using the create-table command

To create a table in DynamoDB, use the create-table command with four arguments:
  • –table-name – It denotes the table name to be created by the user.
  • –attribute-definitions – Denotes a list of attributes and their types that the user will use in the table and indexes.
  • –key-schema – Denotes the attributes that make up the Primary Key for the table or an index.
  • –billing-mode – Controls how users are charged for read and write throughput, and its value can be either PROVISIONED or PAY_PER_REQUEST.
Let’s create a table with the following details:
  • Table Name: Employee_Data
  • AttributeNames: Emp_Id, Name, Salary, Department, Position
  • Range Key: Department
  • Partition Key: Emp_Id
  • Billing Mode: PAY_PER_REQUEST

CLI command to create a table based on the above details will be:

aws dynamodb create-table 
    --table-name Employee_Data 
    --attribute-definitions 
           AttributeName=Emp_Id,AttributeType=S 
           AttributeName=Name,AttributeType=S 
           AttributeName=Salary,AttributeType=N 
           AttributeName=Department,AttributeType=S 
           AttributeName=Position,AttributeType=S 
    --key-schema 
           AttributeName=Emp_Id,KeyType=HASH 
           AttributeName=Department,KeyType=RANGE 
    --billing-mode PAY_PER_REQUEST
To confirm successful table creation, the user can check by clicking “Database” under the AWS console, then selecting “DynamoDB”, and finally clicking on “Tables” to view the created table.

Step 4: Management of Tables and Attributes

The tables and associated attributes can be managed through specific AWS CLI commands. Such commands, along with their functions, include the following :
List Tables
To list all the created DynamoDB tables using CLI, use the following command:
aws dynamodb list-tables
Describe Table
To describe the DynamoDB table using CLI, use the aws dynamodb describe-table command. It returns detailed information about the table, including table key schema, provisioned WCU, RCU, table status, etc.
aws dynamodb describe-table 
    --table-name Employee_Data
Delete Table
To delete the DynamoDB table using CLI, use the aws dynamodb delete-table command given below.
aws dynamodb delete-table 
    --table-name Employee_Data
Backup Table
To backup all the DynamoDB table data on demand, use the following command:
aws dynamodb create-backup 
    --table-name Employee_Data 
    --backup-name Employee_Data -backup-$(date +"%m-%d-%Y")
Put Item
To insert a new record to the DynamoDB table, use the following command:
aws dynamodb put-item 
    --table-name Employee_Data 
    --item '{"Emp_Id": {"S": "110"}, "Name": {"S": "Teena"},"Salary": {"N": "65000"},"Department": {"S": "Sales"},"Position": {"S": "Manager"}}'
Get Item
To get a particular Table Item by its key or combination of keys, use the following command:
aws dynamodb get-item 
    --table-name Employee_Data 
    --key '{"Emp_Id": {"S": "115"}}' 
    --consistent-read
Get All Items / Scan
To run a scan operation or get all Items from the DynamoDB table using CLI, use the following command:
aws dynamodb scan 
    --table-name Employee_Data
Delete Item
Table Item deletion can be only made using its Primary Key by using the following command:
aws dynamodb delete-item 
    --table-name Employee_Data 
    --key '{"Emp_Id": {"S": "111"}}'

Conclusion

Users can use the AWS Management Console or the AWS CLI to manage the AWS services. A developer prefers AWS CLI over other techniques because it provides more accurate control over other services and helps in script automation. Hence, table creation and management in DynamoDB are made easy using a particular set of CLI commands.
Key Takeaways:
  • AWS Command Line Interface (CLI) is a centralized management tool for managing AWS services.
  • Amazon DynamoDB is a fully managed AWS NoSQL database service best suited for high-performance queries on large datasets.
  • Creating a table is done through the aws dynamodb create-table command.
  • To get general information about the table, use the aws dynamodb describe-table command.
  • To insert a new record, use the aws dynamodb put-item command.
  • The aws dynamodb scan command will get the entire Table Items or perform a scan operation.
  • Table Items can be deleted using the aws dynamodb delete-item command.
The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion.
Ansaba
Ansaba

I'm a cloud enthusiast and have done graduation in computer science. My skills include C, C++, Php, Python, Cloud services(AWS, GCP), shell scripting, MySQL, etc. I have hands-on experience and have done several projects and certifications in areas of Python, Php, MySQL, Cloud (GCP, AWS), and C++

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