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Building a Beautiful Data Lakehouse

CIO Business Intelligence

But the data repository options that have been around for a while tend to fall short in their ability to serve as the foundation for big data analytics powered by AI. Traditional data warehouses, for example, support datasets from multiple sources but require a consistent data structure.

Data Lake 119
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Your 5-Step Journey from Analytics to AI

CIO Business Intelligence

One option is a data lake—on-premises or in the cloud—that stores unprocessed data in any type of format, structured or unstructured, and can be queried in aggregate. Another option is a data warehouse, which stores processed and refined data. Just starting out with analytics?

Analytics 115
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WEBCAST: Analytics Through the Lens of Conversational AI

bridgei2i

Enterprises can drive next-level transformational outcomes using intelligent chatbots that integrate with their data warehouses and dashboards, to provide actionable, easy to consume insights. Technologies like Natural Language Processing (NLP) are making analytics insights easier to consume through conversational AI.

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The Reason Many AI and Analytics Projects Fail—and How to Make Sure Yours Doesn’t

CIO Business Intelligence

It provides rapid, direct access to trusted data for data scientists, business analysts, and others who need data to drive business value. Focus on Outcomes Analytics and AI hold the promise of driving better business insights from data warehouses, streams, and lakes. Just starting out with analytics?

Analytics 137
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Become More Data-Driven by Evolving Analytics Workloads

CIO Business Intelligence

Some examples include: Customer 360 analytics, retail inventory and sales analysis, manufacturing operational analysis, eCommerce fraud prevention, network security intelligence, data warehouse consolidation and discount pricing optimization. Just starting out with analytics?

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Use AWS Glue to streamline SFTP data processing

AWS Big Data

About the Authors Sean Bjurstrom is a Technical Account Manager in ISV accounts at Amazon Web Services, where he specializes in Analytics technologies and draws on his background in consulting to support customers on their analytics and cloud journeys.

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Addressing the Three Scalability Challenges in Modern Data Platforms

Cloudera

In legacy analytical systems such as enterprise data warehouses, the scalability challenges of a system were primarily associated with computational scalability, i.e., the ability of a data platform to handle larger volumes of data in an agile and cost-efficient way. Introduction.