Building a Real-Time Data Processing Pipeline with AWS Kinesis, Lambda, S3, and Python: Hands-On

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Explore real-time data processing in the cloud! Learn how to build a robust pipeline using AWS Kinesis, Lambda, and S3, all coded in Python. This hands-on tutorial guides you through processing dynamic weather data, showcasing the power of serverless architecture for seamless data ingestion, transformation, and storage in the cloud. Elevate your AWS skills and dive into the world of real-time data engineering! Prepare for DEA-C01 certification.
Elevate your skills and readiness for an AWS Data Engineer role with practical insights and a hands-on approach to AWS technologies.

[@shaikhabubakarshaikhhabeeb8554]

superb video please make more videos on End to End Data engineering such real time data as well batch from Kinesis to Redshift . S3,Kinesis, Aws lambda,Glue,Redshift

[@wallstreetmindsetpro]

Please check this course ruclips.net/video/bx-fwTnrNQY/видео.html

[@learning7220]

Shouldnt we be using kinesis data fire hose?

[@wallstreetmindsetpro]

That is another option

[@believer0010]

Error writing to S3: An error occurred (AccessDenied) when calling the PutObject operation: Access Denied
I got this error msg among the other messeages like init info in the logs. How can I fix this exception

[@wallstreetmindsetpro]

Check IAM Role Permissions:
Ensure the IAM role associated with your Lambda function has s3:PutObject permission for the target bucket.
Verify S3 Bucket Policy:
Ensure the S3 bucket policy allows the Lambda function's IAM role to write to the bucket.
Check Environment Variables:
Ensure the Lambda function has the correct S3 bucket name and configuration set in its environment variables.
Test the Lambda Function:
After updating permissions, test the Lambda function to confirm it can write to the S3 bucket successfully.

[@sagargoyat5793]

Great content