ETL | Incremental JSON Dataset Load From Amazon S3 Bucket to Amazon Redshift Using AWS Glue

===================================================================
1. SUBSCRIBE FOR MORE LEARNING :
/ @cloudquicklabs
===================================================================
2. CLOUD QUICK LABS - CHANNEL MEMBERSHIP FOR MORE BENEFITS :
/ @cloudquicklabs
===================================================================
3. BUY ME A COFFEE AS A TOKEN OF APPRECIATION :
www.buymeacoff...
===================================================================
🚀 Dive into the world of Extract, Transform, Load (ETL) with this comprehensive tutorial! Learn how to efficiently load JSON datasets incrementally from an Amazon S3 bucket to Amazon Redshift using AWS Glue.
🔗 In this step-by-step guide, we'll walk you through the entire process, from setting up your AWS Glue environment to configuring incremental data loads. Discover best practices for optimizing performance and reducing costs while seamlessly integrating AWS Glue, Amazon S3, and Amazon Redshift.
🛠️ Key Topics Covered:
AWS Glue setup and configuration
Creating an AWS Glue ETL job for JSON data
Configuring incremental data loading strategies
Efficiently managing data changes in your S3 bucket
Mapping JSON schema to Redshift tables
Monitoring and troubleshooting your ETL workflow
💻 Whether you're a beginner exploring ETL processes or an experienced developer looking to enhance your skills, this tutorial offers valuable insights and hands-on demonstrations to help you master the art of loading JSON datasets incrementally with AWS Glue and Amazon Redshift.
👩‍💻 Don't miss out on this opportunity to streamline your data pipeline and boost the efficiency of your data warehouse. Watch now and empower your data integration workflows with AWS Glue!
#awsglue #s3 #amazonredshift #json #etl #dataengineering #etlload #cloudquicklabs

[@rahulpanda9256]

Thank you so much for bringing this up. This is really helpful.

[@cloudquicklabs]

Thank you for watching my videos. Glad that it helped you.

[@rahulpanda9256]

@@cloudquicklabs , For one time ETL initial load, do you think ETL is expensive to use? Glue is expensive in nature. But in case there is incremental load, the pipeline may be invoked multiple times. That way cost will increase. But for 1 time ETL, what do you suggest? In case I want to save cost, what alternate tools do you propose for actual production scenario for this kind of use cases?

[@ManojKumar-cp5kk]

Hello Sir, How I can do hands on in house.. As any way we can take AWS glue free or in less amount?

[@cloudquicklabs]

Thank you for watching my videos. As it's pay as you go service you can still use it for hands on experience. While careful using spark job as that would costly .

[@unknown_writter007]

How does it handle nested json. Exam I have json with ec2 describes information

[@cloudquicklabs]

Thank you for watching my videos. It should be handled in etl job pipelines that you create here.

[@prabhajayashetty2297]

Great info !! I was trying same, however I got An error occurred while calling o114.pyWriteDynamicFrame. : java.sql.SQLException: Exception thrown in awaitResult: Can you please suggest some solution here

[@cloudquicklabs]

Thank you for watching my videos. Check if source data is in required format at defined in the job. Whenever source data misses the required data format it throws Below error. Please check the same.

[@siddharthmishra6502]

But the glue is on the expensive side . Can you create one pipeline using nifi or airflow

[@cloudquicklabs]

Thank you for watching my videos. And thank you for providing the suggestions. I shall create video on this.