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Enhance your Amazon Redshift cloud data warehouse with easier, simpler, and faster machine learning using Amazon SageMaker Canvas

AWS Machine Learning Blog

Built into Data Wrangler, is the Chat for data prep option, which allows you to use natural language to explore, visualize, and transform your data in a conversational interface. Amazon QuickSight powers data-driven organizations with unified (BI) at hyperscale. A provisioned or serverless Amazon Redshift data warehouse.

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Build ML features at scale with Amazon SageMaker Feature Store using data from Amazon Redshift

Flipboard

Amazon Redshift is the most popular cloud data warehouse that is used by tens of thousands of customers to analyze exabytes of data every day. AWS Glue is a serverless data integration service that makes it easy to discover, prepare, and combine data for analytics, ML, and application development. yaml locally.

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How does Tableau power Salesforce Genie Customer Data Cloud?

Tableau

But good data—and actionable insights—are hard to get. Traditionally, organizations built complex data pipelines to replicate data. Those data architectures were brittle, complex, and time intensive to build and maintain, requiring data duplication and bloated data warehouse investments.

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How does Tableau power Salesforce Genie Customer Data Cloud?

Tableau

But good data—and actionable insights—are hard to get. Traditionally, organizations built complex data pipelines to replicate data. Those data architectures were brittle, complex, and time intensive to build and maintain, requiring data duplication and bloated data warehouse investments.

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Fivetran Modern Data Stack Conference 2023: Key Takeaways

Alation

In “The modern data stack is dead, long live the modern data stack!” the presenters elaborated on the common pain points of the cloud data warehouse today and predicted what it may look like in the future. So, how can a data catalog support the critical project of building data pipelines?

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Apache Kafka and Apache Flink: An open-source match made in heaven

IBM Journey to AI blog

When you make it easier to work with events, other users like analysts and data engineers can start gaining real-time insights and work with datasets when it matters most. As a result, you reduce the skills barrier and increase your speed of data processing by preventing important information from getting stuck in a data warehouse.

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Implementing GenAI in Practice

Iguazio

Demo: How to Build a Smart GenAI Call Center App How we used LLMs to turn call center conversation audio files of customers and agents into valuable data in a single workflow orchestrated by MLRun. The data pipeline - Takes the data from different sources (document, databases, online, data warehouses, etc.),