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Better together? Why AWS is unifying data analytics and AI services in SageMaker

CIO

Data warehousing, business intelligence, data analytics, and AI services are all coming together under one roof at Amazon Web Services. It combines SQL analytics, data processing, AI development, data streaming, business intelligence, and search analytics.

Analytics 183
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Building Resilient Public Networking on AWS: Part 4

Xebia

Region Evacuation with static anycast IP approach Welcome back to our comprehensive "Building Resilient Public Networking on AWS" blog series, where we delve into advanced networking strategies for regional evacuation, failover, and robust disaster recovery. Find the detailed guide here.

AWS 130
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The future of data: A 5-pillar approach to modern data management

CIO

In todays economy, as the saying goes, data is the new gold a valuable asset from a financial standpoint. A similar transformation has occurred with data. More than 20 years ago, data within organizations was like scattered rocks on early Earth.

Data 167
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Build and deploy a UI for your generative AI applications with AWS and Python

AWS Machine Learning - AI

However, as exciting as these advancements are, data scientists often face challenges when it comes to developing UIs and to prototyping and interacting with their business users. Streamlit allows data scientists to create interactive web applications using Python, using their existing skills and knowledge.

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Build Your Open Data Lakehouse on Apache Iceberg

Speaker: Veena Vasudevan and Jason Hughes

With data stored in vendor-agnostic files and table formats like Apache Iceberg, the open lakehouse is the best architecture to enable data democratization. By moving analytic workloads to the data lakehouse you can save money, make more of your data accessible to consumers faster, and provide users a better experience.

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Accelerate AWS Well-Architected reviews with Generative AI

AWS Machine Learning - AI

To achieve these goals, the AWS Well-Architected Framework provides comprehensive guidance for building and improving cloud architectures. This allows teams to focus more on implementing improvements and optimizing AWS infrastructure. This systematic approach leads to more reliable and standardized evaluations.

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Implementing a Version Control System for AWS QuickSight

Xebia

Introduction In today’s data-driven world, business intelligence tools are indispensable for organizations aiming to make informed decisions. However, as with any data analytics platform, managing changes to reports, dashboards, and data sets is a critical concern.

AWS 130
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How to Operationalize Data From Multiple Sources to Deliver Actionable Insights

Speaker: Speakers from SafeGraph, Facteus, AWS Data Exchange, SimilarWeb, and AtScale

Data and analytics leaders across industries can benefit from leveraging multiple types of diverse external data for making smarter business decisions. Data and analytics specialists from AWS Data Exchange and AtScale will walk through exactly how to blend and operationalize these diverse data external and internal sources.

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Data Analytics in the Cloud for Developers and Founders

Speaker: Javier Ramírez, Senior AWS Developer Advocate, AWS

You have lots of data, and you are probably thinking of using the cloud to analyze it. But how will you move data into the cloud? How will you validate and prepare the data? What about streaming data? Can data scientists discover and use the data? Will the data lake scale when you have twice as much data?