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Modern Data Architectures | Data Mesh, Data Fabric, & Data Lakehouse

For years, companies have viewed data the wrong way. They see it as the byproduct of a business interaction and this data often ends up collecting dust in centralized silos governed by data teams who lack the expertize to understand its true value. Cloudera is ushering in a new era of data architecture by allowing experts to organize and manage their own data at the source. Data mesh brings all your domains together so each team can benefit from each other’s data.

Future of Data Meetup: Enrich Your Data Inline with Apache NiFi

In this meetup, we’ll look at the different options for enriching your data using Apache NiFi. When and why would we prefer using NiFi for enrichment over a potentially more holistic solution, like Flink or Spark? What are the limitations? And how can we get the best of both worlds, performing data enrichment with NiFi when it makes sense and using our CEP engine when that makes the most sense? Join John Kuchmek and Mark Payne to find out!

Universal Data Distribution with Cloudera DataFlow for the Public Cloud

The speed at which you move data throughout your organization can be your next competitive advantage. Cloudera DataFlow greatly simplifies your data flow infrastructure facilitating complex data collection and movement through a unified process that seamlessly transfers data throughout your organization. Even as you scale. With Cloudera DataFlow for Public Cloud you can collect and move any data (structured, unstructured, and semi-structured) from any source to any destination with any frequency (real-time streaming, batch, and micro-batch).

Cloudera DataFlow Functions for Public Cloud powered by Apache NiFi

Since its initial release in 2021, Cloudera DataFlow for Public Cloud (CDF-PC) has been helping customers solve their data distribution use cases that need high throughput and low latency requiring always-running clusters. CDF-PC’s DataFlow Deployments provides a cloud-native runtime to run your Apache NiFi flows through auto scaling Kubernetes clusters as well as centralized monitoring and alerting and improved SDLC for developers.

Get to anomaly detection faster with Cloudera's Applied Machine Learning Prototypes

The Applied Machine Learning Prototype (AMP) for anomaly detection reduces implementation time by providing a reference model that you can build from. Built by Fast Forward Labs, and tested on AMD EYPC™ CPUs with Dell Technologies, this AMP enables data scientists across industries to truly practice predictive maintenance.