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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. To get started today, refer to Developing AWS Glue jobs with Notebooks and Interactive sessions.

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Build data integration jobs with AI companion on AWS Glue Studio notebook powered by Amazon CodeWhisperer

AWS Big Data

AWS Glue provides different authoring experiences for you to build data integration jobs. Data scientists tend to run queries interactively and retrieve results immediately to author data integration jobs. This interactive experience can accelerate building data integration pipelines.

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The Enduring Significance of Data Modeling in the Modern Data-Driven Enterprise

erwin

Q: Is data modeling cool again? In today’s fast-paced digital landscape, data reigns supreme. The data-driven enterprise relies on accurate, accessible, and actionable information to make strategic decisions and drive innovation. A: It always was and is getting cooler!!

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How To Interact With Power BI Data In A PowerPoint Presentation

Smart Data Collective

Microsoft Power BI is a Business Intelligence and Data Visualization tool which assists organizations to analyze data from multiple sources, convert it into an interactive dashboard and share insights. Data Processing, Data Integration, and Data Presenting form the nucleus of Power BI.

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End-to-end development lifecycle for data engineers to build a data integration pipeline using AWS Glue

AWS Big Data

Many AWS customers have integrated their data across multiple data sources using AWS Glue , a serverless data integration service, in order to make data-driven business decisions. Are there recommended approaches to provisioning components for data integration?

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GraphDB in Action: Putting the Most Reliable RDF Database to Work for Better Human-machine Interaction

Ontotext

In today’s world, we increasingly interact with the environment around us through data. For all these data operations to flow smoothly, data needs to be interoperable, of good quality and easy to integrate. As a result of these data quality issues, the need for integrity checks arises.

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Data Modeling 101: OLTP data modeling, design, and normalization for the cloud

erwin

How to create a solid foundation for data modeling of OLTP systems. As you undertake a cloud database migration , a best practice is to perform data modeling as the foundation for well-designed OLTP databases. This makes mastering basic data modeling techniques and avoiding common pitfalls imperative.