In today’s data-driven world, poor data quality can lead to missed opportunities, wrong decisions, and costly inefficiencies. This ebook demonstrates how AWS Glue DataBrew empowers organizations to automate data cleansing, normalization, and validation processes with a simple, visual interface.
Through a practical proof of concept (POC), you’ll explore how to integrate automated data quality checks into your data workflows—boosting accuracy, reliability, and trust in your data assets.
What’s inside:
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Understanding Data Quality Challenges: Learn why accuracy, completeness, and consistency are vital to informed business decisions.
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AWS Glue DataBrew Deep Dive: Explore the tool’s visual interface, automation capabilities, and integration with the AWS ecosystem.
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Step-by-Step Proof of Concept: Follow a real-world example of automating data quality checks in a human resources dataset using AWS Glue DataBrew.
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Implementation Guide: Configure datasets, define rulesets, clean data, and validate results — all without writing code.
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Business Benefits: See how organizations achieve better efficiency, reliability, and scalability through no-code data automation.
What You’ll Gain
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Practical Knowledge: Learn how to set up and manage automated data quality checks using AWS Glue DataBrew.
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Technical Confidence: Understand how to validate and cleanse large datasets without coding.
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Strategic Perspective: Recognize the business value of maintaining high-quality data for compliance, analytics, and decision-making.
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Implementation Blueprint: Access a reusable framework for automating data quality processes in your organization.
Take control of your data quality today.
Download the free ebook and see how AWS Glue DataBrew can transform your approach to data preparation and governance.