AWS DOCUMENTATION

Meenaumadevi M — Thu, 23 Jan 2025 12:15:21 +0000

1.Running react app in s3 bucket and configure to cloudfront

Running the code in command prompt:

After running it in chrome:

Creating bucket:

After uploading the files and folders in s3 bucket:

Enabling static webhosting:

Editing bucket policy:

Running the s3 url in chrome:

Creating distribution in cloudfront:

Running the Distribution Domain name in chrome:

2.Connecting ec2 instance

Creating instance

Edit the inbound rules in security group:

Updating the inbound rules:

Downloading putty:

Connected to instance and run using putty:

Saving the files in linux:

Creating role:

Attaching the role to instance:

Upload the files in s3 bucket:

AWS Glue DataBrew

Meenaumadevi M — Thu, 23 Jan 2025 03:17:02 +0000

Service Overview
Service Name: AWS Glue DataBrew
Logo:
Tagline or One-Line Description: "AWS Glue DataBrew: Clean, Prepare, and Transform Your Data Visually, Without Writing Code."
Key Features
Top Features:
Automated Data Profiling.
Secure and Reliable.
Scalable Data Handling.
Technical Specifications:
Supported Regions : Available in most AWS regions worldwide.
Dataset Profiling Limits: Up to 20 million rows or 10 GB per profiling session.
Security : Full integration with AWS IAM for role-based access and resource permissions.
Data Sources : Compatible with Amazon S3, Redshift, RDS, and other JDBC-supported data sources.
Use Cases
Real-Life Applications: AWS Glue DataBrew
Cleaning Customer Data : Fix issues like missing names or incorrect dates in customer data for better analysis.
Preparing Data for Reports : Format and clean data so it's ready to be used in business reports.
Combining Data from Different Sources: Merge data from Amazon S3 and RDS to create a single dataset for analysis.
Pricing Model
Pricing Overview: AWS Glue DataBrew uses a pay-as-you-go pricing model.Pricing based on
Data Processing Charges.
Compute Resource Usage.
Data Profiling Sessions.
Automated Job Scheduling.
Comparison with Similar Services
Competitors or Alternatives:
Google Cloud Dataprep (Non-AWS):Provides a no-code interface for data cleaning but focuses on Google Cloud integration.
Microsoft Power Query (Non-AWS):Ideal for small-scale, desktop-based data preparation, integrating seamlessly with Excel and Power BI.
Azure Data Factory (AWS Alternative):Offers data transformation within the Azure ecosystem but has a steeper learning curve, suited for advanced users.
Apache NiFi (Non-AWS):Open-source tool for data routing and transformation but needs complex configurations.
Benefits and Challenges
Advantages: No-Code Interface, Scalability, Over 250 Pre-Built Transformations, Automated Data Profiling, Security and Compliance.
Limitations or Challenges: Cost for Large Datasets, Complexity for Advanced Data Processing, Dependency on AWS Services, Limited Output Formats.
Real-World Example or Case Study
Case Study: Coca-Cola Company.
Example: Coca-Cola uses AWS Glue DataBrew to simplify and automate their data preparation processes, which are essential for generating business insights across various markets.

DEV Community: Meenaumadevi M

AWS DOCUMENTATION

AWS Glue DataBrew