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Introducing Apache Iceberg in Cloudera Data Platform

Cloudera

Over the past decade, the successful deployment of large scale data platforms at our customers has acted as a big data flywheel driving demand to bring in even more data, apply more sophisticated analytics, and on-board many new data practitioners from business analysts to data scientists.

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Orca Security’s journey to a petabyte-scale data lake with Apache Iceberg and AWS Analytics

AWS Big Data

With data becoming the driving force behind many industries today, having a modern data architecture is pivotal for organizations to be successful. This data is then used by various applications for streaming analytics, business intelligence, and reporting. This ensures that the data is suitable for training purposes.

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A Summary Of Gartner’s Recent Innovation Insight Into Data Observability

DataKitchen

Like an apartment blueprint, Data lineage provides a written document that is only marginally useful during a crisis. This is especially true regarding our one-to-many, producer-to-consumer relationships on our data architecture. Are problems with data tests? Which report tab is wrong? When did it last run? Did it fail?

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AI at Scale isn’t Magic, it’s Data – Hybrid Data

Cloudera

The takeaway – businesses need control over all their data in order to achieve AI at scale and digital business transformation. The challenge for AI is how to do data in all its complexity – volume, variety, velocity. Because that is how models learn. But it isn’t just aggregating data for models.

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5 Reasons to Use Apache Iceberg on Cloudera Data Platform (CDP)

Cloudera

In fact, we recently announced the integration with our cloud ecosystem bringing the benefits of Iceberg to enterprises as they make their journey to the public cloud, and as they adopt more converged architectures like the Lakehouse. Simplify data management . 1: Multi-function analytics . 2: Open formats. Reproducibility for ML Ops.

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Exploring real-time streaming for generative AI Applications

AWS Big Data

Foundation models (FMs) are large machine learning (ML) models trained on a broad spectrum of unlabeled and generalized datasets. This scale and general-purpose adaptability are what makes FMs different from traditional ML models. FMs are multimodal; they work with different data types such as text, video, audio, and images.

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Build a serverless transactional data lake with Apache Iceberg, Amazon EMR Serverless, and Amazon Athena

AWS Big Data

Tracking data changes and rollback Build your transactional data lake on AWS You can build your modern data architecture with a scalable data lake that integrates seamlessly with an Amazon Redshift powered cloud warehouse. Dimension-based models have been used extensively to build data warehouses.

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