What is Lambda Function in AWS FULL DEMO in Hindi | AWS Beginner to Advanced |

What is Lambda Function in AWS FULL DEMO in Hindi | AWS Beginner to Advanced | @cloudtechburner
What is Lambda Function in AWS FULL DEMO in Hindi | AWS Beginner to Advanced | @cloudtechburner
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[@user-wy9zi5op9l]

bro how can you connect from frontend to data base directly? without backend(server) I don't think, it is possible to connect frontend to database directly.

[@cloudtechburner]

while it's technically possible to connect a frontend directly to a database in AWS Lambda, it's not a recommended practice due to security, scalability, and performance concerns. Using a backend server as an intermediary is a more secure and scalable approach.

[@maheshg82]

I was expecting to see the output which is missing from the video

[@shivamverma-ps1gj]

I have a x cloud and I want to download x data in my local
With parallel processing with aws lambda
Because the data are in very huge quantity
Any idea about that ?

[@cloudtechburner]

To download large quantities of data from X Cloud to your local environment using AWS Lambda with parallel processing, you can follow these general steps:
Setup AWS Lambda: First, ensure you have an AWS account and set up AWS Lambda. Create a Lambda function with the appropriate permissions to access the X Cloud service.
Break Down Data: Split the data into smaller chunks that can be processed in parallel. This step is crucial for parallel processing as it allows Lambda functions to work on different parts of the data simultaneously.
Implement Parallel Processing Logic: Write code within your Lambda function to download data in parallel. You can use Python's concurrent.futures module or other parallel processing libraries to achieve this. Divide the data into manageable chunks and assign each chunk to a separate Lambda invocation.
Handle Errors and Retries: Implement error handling and retries within your Lambda function to ensure robustness, especially when dealing with large quantities of data and network issues.
Monitor and Scale: Monitor the performance of your Lambda function and scale it accordingly to handle the workload efficiently. AWS provides monitoring tools like CloudWatch for this purpose.
Download Data: Once your Lambda function is set up, trigger it to start downloading the data from X Cloud to your local environment. Monitor the progress and ensure all data is successfully downloaded.
Here's a simple example using Python and concurrent.futures for parallel processing within a Lambda function:
import concurrent.futures
import boto3
def download_chunk(chunk):
# Download data from X Cloud for the given chunk
# Replace this with your actual download logic
pass
def lambda_handler(event, context):
# Split data into chunks
chunks = [chunk1, chunk2, ...] # Define your chunks here
# Process chunks in parallel
with concurrent.futures.ThreadPoolExecutor() as executor:
# Submit each chunk to the executor
futures = [executor.submit(download_chunk, chunk) for chunk in chunks]
# Wait for all futures to complete
for future in concurrent.futures.as_completed(futures):
try:
future.result() # Get the result of each future
except Exception as e:
print(f"Error downloading chunk: {e}")
return "Download complete"
Remember to replace download_chunk with your actual download logic for each chunk of data from X Cloud. Additionally, adjust the chunking logic based on the size and nature of your data.

[@abhishekbhatt948]

Most common error getting in lambda is about "lambda handler" ,why? Expecting talking about errors. And layers.

[@cloudtechburner]

Common AWS Lambda Handler Errors
1. Incorrect Handler Name: Ensure the handler in the Lambda config matches your code (file_name.method_name).
2. Handler Not Found: Verify the module and function names exist in your code.
3. Syntax Errors: Test code locally to fix syntax issues.
4. Incorrect Permissions: Ensure the execution role has necessary permissions.
5. Runtime Errors: Add error handling and logging in your code.
6. Timeouts: Optimize code or increase the function's timeout setting.
Common Lambda Layer Issues
1. Layer Permissions: Set correct policies and permissions for layer access.
2. Layer Versioning: Use the latest layer version.
3. Layer Dependencies: Include all necessary dependencies in the layer.
4. Compatibility Issues: Ensure the layer is compatible with the Lambda runtime.
Best Practices
1. Local Testing: Use AWS SAM for local testing.
2. Logging: Utilize CloudWatch Logs for debugging.
3. Automated Deployment: Use AWS CloudFormation or Terraform.
4. Version Control: Maintain versioning for functions and layers.
5. Documentation: Clearly document handler formats and dependencies.

[@sabkaBaap007]

​@@cloudtechburnerChat gpt ka copy paste reply 🤣🤣🤣

[@piyushraut3715]

Please provide code link

[@cloudtechburner]

Sample.json file: drive.google.com/file/d/1ABV2HRW4bSTCvEpC_nj8fT5LWYvz0SMp/view?usp=sharing

[@loverstatus288]

Can you give me notes.

[@cloudtechburner]

Come to telegram and pay the Charges you will get the notes

[@ankitkamble8673]

Done!

[@cloudtechburner]

I hope you may have get the notes 😊