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Explore visualizations with AWS Glue interactive sessions

AWS Big Data

AWS Glue interactive sessions offer a powerful way to iteratively explore datasets and fine-tune transformations using Jupyter-compatible notebooks. This post is part of a series exploring the features of AWS Glue interactive sessions. Now, let’s run a few visualizations on the Iris and MNIST datasets. and later).

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Visualize data quality scores and metrics generated by AWS Glue Data Quality

AWS Big Data

It provides insights and metrics related to the performance and effectiveness of data quality processes. In this post, we highlight the seamless integration of Amazon Athena and Amazon QuickSight , which enables the visualization of operational metrics for AWS Glue Data Quality rule evaluation in an efficient and effective manner.

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Why the Data Journey Manifesto?

DataKitchen

They need to learn customers’ interactions with their brand and marketing touchpoints. The Customer Journey visually represents the total sum of experiences any given customer has with a brand. We continue to over-invest, as an industry, in the tools that run within our data estate.

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Data is essential: Building an effective generative AI marketing strategy

IBM Big Data Hub

The advanced capabilities of generative AI for text, visuals and video have the potential to create a more personalized and engaging experience. Generative AI needs human marketing teams Once deployed, your generative AI data journey isn’t over.

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Amazon DataZone now integrates with AWS Glue Data Quality and external data quality solutions

AWS Big Data

In the first part of this post, we walk through the integration between AWS Glue Data Quality and Amazon DataZone. We discuss how to visualize data quality scores in Amazon DataZone, enable AWS Glue Data Quality when creating a new Amazon DataZone data source, and enable data quality for an existing data asset.

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Accelerating generative AI requires the right storage

CIO Business Intelligence

In addition, managing the data created by generative AI models is becoming a crucial aspect of the AI lifecycle. That newly generated data, from AI interactions, simulations, or creative outputs, must be properly stored, organized and curated for various purposes like model improvement, analysis, and compliance with data governance standards.

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Erwin Data Intelligence: A Data Partner’s Perspective

erwin

While the essence of success in data governance is people and not technology, having the right tools at your fingertips is crucial. Technology is an enabler, and for data governance this is essentially having an excellent metadata management tool. Next to data governance, data architecture is really embedded in our DNA.