ETL | AWS Glue | AWS S3 | ETL Job | Detect and remediate personal identifiable information PII

Рет қаралды 542

5 ай бұрын

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In this insightful KZfaq video, we delve into the world of Extract, Transform, Load (ETL) using AWS Glue and S3 to manage data seamlessly and efficiently. The focus of this tutorial is on detecting and remediating Personal Identifiable Information (PII), ensuring compliance with data privacy regulations.
Key Highlights:
Introduction to ETL with AWS Glue: Gain a comprehensive understanding of AWS Glue, Amazon's fully-managed ETL service, and its capabilities in processing and transforming vast amounts of data.
Integration with AWS S3: Explore the integration between AWS Glue and AWS S3, a scalable object storage service, to optimize storage and facilitate seamless data movement.
Detecting PII in Data: Learn how to implement advanced techniques to identify and locate Personal Identifiable Information within your datasets, safeguarding sensitive information from unauthorized access.
Remediation Strategies: Discover effective strategies for remediating PII, including data masking, encryption, and other privacy-enhancing techniques, to ensure compliance with data protection regulations.
Automation with ETL Job: Walk through the process of creating an ETL job using AWS Glue, automating the extraction, transformation, and loading of data while implementing PII detection and remediation measures.
Best Practices and Tips: Receive valuable insights into best practices for designing robust ETL workflows, optimizing performance, and ensuring data security.
Whether you are a data engineer, analyst, or a business professional looking to enhance your knowledge of ETL processes and data privacy, this video provides a step-by-step guide to implementing a secure and efficient data pipeline with AWS Glue and S3. Don't miss out on this opportunity to elevate your AWS skills and ensure the protection of sensitive information in your datasets. Watch now and take your data management to the next level!
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#datamanagement
#automation
#awscloud
#datalake
#compliance
#security
#bestpractices
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#dataengineering
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#informationsecurity

Пікірлер: 7

@rahulpanda9256 5 ай бұрын

This is great demonstration. Thanks a lot. I have some of the below follow up queries wrt this. 1) I see in most of the cases you are using Visual ETL. In the actual Production scenarios, is Visual ETL sufficient all the times for complex logics? What do you prefer based on your experience? 2) In most of your demos (the ones that I have see so far), you are dealing with structured data in csv format as a source. Have you covered any demo video with semi structured data as source (such as json)? If yes, can you help me with that video link? If no, would be great if you can come up with one of such demo. Thanks a lot!

@cloudquicklabs 5 ай бұрын

Thank you for watching my videos. Please find my response sequentially. 1. In production I do suggest to keep the data pipeline definition with Infrastructure as Code ( IaC) with either Terraform or CFN. I shall make video on this soon. 2. Indeed this very good suggestion to make JSON as source data. I shall create new videos on it. Thank you very much for your suggestions , I shall work on it.