I will check and get back

I will update it . Thanks for the feedback

Thanks for the feedback. Will try and improve

Thanks siddb09

As of recent updates, Application Load Balancers do support AWS PrivateLink. You can create an endpoint service in your VPC and specify an ALB as the service provider. This allows you to offer the applications behind your ALB privately to other VPCs through AWS PrivateLink. If there are any specific documentation you can refer i can help you better where they have mentioned dropping support.

You can definitely send payloads directly to Amazon SNS without using AWS Lambda or Amazon SQS. The choice to use services like Lambda or SQS before SNS often depends on the specific requirements of your architecture and the nature of the data processing involved. Here are some reasons why you might use AWS Lambda or Amazon SQS before sending a message to SNS: 1. **Data Processing with Lambda:** - **Complex Transformations:** If the payload needs to be processed or transformed before sending the notification, using Lambda is a common approach. For example, you might need to extract specific information from a larger dataset or format the message in a particular way. - **Conditional Logic:** Lambda allows you to implement complex logic to determine whether a notification should be sent. For instance, you might only want to trigger an SNS notification under certain conditions after inspecting the incoming data. - **Integration Logic:** Sometimes, the decision to send a notification via SNS depends on the results of integrating with other services. Lambda can handle these integrations (e.g., querying a database or calling an external API) before deciding to send a message to SNS. 2. **Decoupling with SQS:** - **Rate Limiting and Throttling:** If your system produces notifications at a high rate, you might use SQS to buffer these messages. This helps in managing throughput to SNS, especially if there are many subscribers or if the subscribers have rate limits. - **Reliability and Durability:** SQS provides a reliable queueing system that ensures that messages are not lost between your application and the notification service. If your system requires a high degree of reliability, using SQS as a buffer can help protect against data loss in case of transient errors. - **Retry Logic and Dead Letter Queues:** SQS can be used to manage retries in a more controlled manner. If a message fails to be processed or sent to SNS, it can be retried or moved to a Dead Letter Queue (DLQ) for further investigation. This helps in isolating problematic messages and ensures that your system remains resilient. 3. **Direct Use of SNS:** - **Simplicity:** If your application simply needs to send a notification with minimal processing, sending the payload directly to SNS is the simplest approach. This reduces the complexity and operational overhead of your architecture. - **Real-Time Notifications:** For scenarios where you need immediate notification without any delay, sending direct messages to SNS is preferable. This ensures that there is minimal latency between the event occurrence and the notification being sent out. 4. **Cost Considerations:** - Using Lambda or SQS introduces additional costs based on their usage (invocations for Lambda and the number of messages for SQS). Directly using SNS might be more cost-effective if the additional features provided by Lambda or SQS are not required for your use case. In summary, while sending payloads directly to SNS is a common and straightforward approach, using Lambda or SQS as intermediaries can provide additional flexibility, processing capabilities, and reliability features based on your specific needs.

DynamoDB might start with 2 partitions instead of 1 for your requirements of 3000 RCUs and 1000 WCUs to better balance the load and improve performance consistency, even if one partition can technically handle the capacity. This approach also enhances fault tolerance and operational flexibility by distributing the workload across multiple partitions from the start.

@@Pythoholic I think, I have figured out why 2 partitions are needed and not 1. One partition can deliver either 3000 rcu or 1000 wcu or mix of both (i.e. if it has 500 wcu it can have 1500 rcu). It is not possible for a partition to deliver both 3000 rcu and 1000 wcu. By this logic, if one partition delivers all the required 3000 rcu, one more partiton is required to deliver 1000 wcu. Since they are distributed equally among partitons, each partition will deliver 1500 rcu and 500 wcu.

The thing which is missing from most documentation online, is its not 3000 rcu and 1000 wcu. Its 3000 rcu or 1000 wcu or a mix of both. By this logic, one partition won't be able to handle that load. You must need two. I got this information from an AWS Events video posted on YT about DynamoDB.

No sir not yet. Will make one soon

@@Pythoholic you are one of the best channel to learn AWS. Can you tell some simple projects for beginner and intermediate

Here are some simple AWS project ideas 1. **Deploy a Static Website on S3:** - **Objective:** Learn how to use Amazon S3 to host static web resources like HTML, CSS, and JavaScript files. - **Key Services:** Amazon S3, AWS IAM, Route 53 (optional for custom domain). - **Steps:** 1. Create an S3 bucket and enable static website hosting. 2. Upload the static web files to the S3 bucket. 3. Update the bucket policy to make the content publicly accessible. 4. (Optional) Configure a custom domain using Route 53. 2. **Simple Web Application with DynamoDB:** - **Objective:** Develop a simple web application that uses DynamoDB to store data. - **Key Services:** AWS Lambda, Amazon API Gateway, Amazon DynamoDB, AWS IAM. - **Steps:** 1. Create a DynamoDB table to store data (e.g., a simple CRUD for tasks or notes). 2. Implement Lambda functions to handle CRUD operations on the DynamoDB table. 3. Use API Gateway to create RESTful endpoints for the Lambda functions. 4. Secure the application using appropriate IAM roles. 3. **Deploy a Python Flask App to Elastic Beanstalk:** - **Objective:** Learn how to deploy a simple Python Flask application using AWS Elastic Beanstalk. - **Key Services:** AWS Elastic Beanstalk, Amazon RDS (optional for a database). - **Steps:** 1. Develop a simple Flask application on your local machine. 2. Package the application with any dependencies and a configuration file (`requirements.txt` and `.ebextensions`). 3. Deploy the application to Elastic Beanstalk using the EB CLI or the AWS Management Console. 4. (Optional) Add an RDS database and connect it to your application. 4. **Serverless Image Resizer:** - **Objective:** Create a serverless application that automatically resizes images uploaded to S3. - **Key Services:** AWS Lambda, Amazon S3, Amazon SNS or SQS, AWS IAM. - **Steps:** 1. Create an S3 bucket to upload images. 2. Implement a Lambda function triggered by S3 events to resize images using a library like Pillow. 3. Store the resized images in a different S3 bucket or the same bucket with a different prefix. 4. Configure any necessary permissions and roles for Lambda to access S3. 5. **Basic CloudWatch Dashboard:** - **Objective:** Set up a CloudWatch dashboard to monitor the performance and health of AWS services. - **Key Services:** Amazon CloudWatch. - **Steps:** 1. Identify which metrics are important for your application or environment. 2. Create a CloudWatch dashboard and add widgets to visualize these metrics. 3. Set up CloudWatch alarms to notify you of any critical changes in the metrics. 6. **Simple Notification System:** - **Objective:** Implement a notification system using SNS to send messages to subscribed endpoints. - **Key Services:** Amazon SNS, AWS Lambda (optional), AWS IAM. - **Steps:** 1. Create an SNS topic. 2. Subscribe email addresses or phone numbers to the SNS topic. 3. Publish messages to the topic either manually from the AWS console or programmatically using AWS SDKs. 7. **Lambda Function to Process Logs:** - **Objective:** Develop a Lambda function to process log files uploaded to S3. - **Key Services:** AWS Lambda, Amazon S3, AWS IAM. - **Steps:** 1. Create an S3 bucket for log file uploads. 2. Write a Lambda function to parse log files (e.g., for error monitoring or usage statistics). 3. Set up an S3 event notification to trigger the Lambda function when new logs are uploaded.

Point taken. Will try and improve. Sorry for the inconvenience

Glad you enjoy it!

Thanks for the feedback I will surely keep this in mind

Thanks a lot Jagan for your wonderful story that you shared and its also so amazing that you are still going on after facing a lot of issues in your life as well. I am sure you will get all the things you want from life. I am really happy that i could help you in any way possible. May be my job as a content creator was just that. I will try my best to provide good content and share what i know with the best of my abilities. Thanks again :)

I got these steps on a website please check if it helps. 1. **Set up AWS SDK:** First, make sure you have the AWS SDK for JavaScript installed in your React project. You can install it using npm or yarn: ```bash npm install aws-sdk ``` 2. **Configure AWS Credentials:** You need to configure your AWS credentials to access Timestream from your React app. You can do this by setting up the `AWS.config` object: ```javascript import AWS from 'aws-sdk'; AWS.config.update({ region: 'us-east-1', // Replace with your region accessKeyId: 'YOUR_ACCESS_KEY_ID', secretAccessKey: 'YOUR_SECRET_ACCESS_KEY' }); ``` For security reasons, it's recommended to use environment variables or AWS IAM roles instead of hardcoding your credentials. 3. **Set up Timestream Query Client:** Create a Timestream Query client to execute queries on your Timestream table. ```javascript const timestreamQuery = new AWS.TimestreamQuery(); ``` 4. **Query Timestream Table:** Use the Timestream Query client to execute SQL queries on your Timestream table and fetch the data. ```javascript const params = { QueryString: 'SELECT * FROM your_database.your_table LIMIT 10' // Replace with your query }; timestreamQuery.query(params, (err, data) => { if (err) { console.error('Query Error:', err); } else { console.log('Query Results:', data); // Process and display the data in your React component } }); ``` 5. **Display Data in React Component:** Once you have the data, you can display it in your React components as needed. You might want to store the data in the component's state and render it in the JSX. ```javascript import React, { useState, useEffect } from 'react'; const MyComponent = () => { const [timestreamData, setTimestreamData] = useState([]); useEffect(() => { // Fetch Timestream data and set it to state timestreamQuery.query(params, (err, data) => { if (!err) { setTimestreamData(data.Rows); // Assuming data is in data.Rows } }); }, []); return ( <div> {timestreamData.map((row, index) => ( <div key={index}> {/* Render your data here */} </div> ))} </div> ); }; export default MyComponent; ``` Remember to replace placeholders like `YOUR_ACCESS_KEY_ID`, `YOUR_SECRET_ACCESS_KEY`, `your_database`, and `your_table` with your actual AWS credentials and Timestream table details. Also, make sure to handle error cases and secure your AWS credentials properly.

@@Pythoholic thank you bro its working now

The topics are basically cloud concepts please refer the doc for saa.c03 and follow the course for individual topics that u wish to learn. I hope that helps

Please refer to the latest syllabus doc for the current exam pattern. Follow the course for the content and the topic.

@@Pythoholic ok thanks for reply ..any extra service and knowledge we need to gain for latest one?? This is my question

You need to cover the topics in the new one as per the exam syllabus. These videos are a bit old so you can refer them for ur concept knowledge and read more about the service from the documentation. I might not be able to share the complete changes.

@@Pythoholic pls suggest certification how can I pass by following your playlist? I don't know what to study extra for clearing it.. pls help

Pls reply

Hello Uday, the concepts will be same but the domain weights of topics for exam will be different.

All you said about performance is a big lie.

Thanks for the feedback Yusuf. I will update it as per ur feedback