Systems | Development | Analytics | API | Testing

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!

Paul Codding introduces Cloudera's Applied ML Prototypes to accelerate machine learning applications in business.

In this demo, we have shown how an analyst who knows only SQL can work independently to create sophisticated data transformation pipelines without the need for any engineering. Our CDP deployment simplifies all aspects of the software development lifecycle of dbt models.

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).

CDP Private Cloud Base 7.1.8 is here! This marks the next wave of Cloudera innovation on-premises for CDP. In this live stream, we’ll go through what’s in our latest release and highlight some of the exciting new features we’ve made available.

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.

The Cloudera Applied Machine Learning Prototype (AMP) for continuous model monitoring acts as a customizable template for your data science team to quickly build an accurate way to track drift.

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.

The MiNiFi C++ agent provides many features for collecting and processing data at the edge. All the strengths of MiNiFi C++ make it a perfect candidate for collecting logs of cloud native applications running on Kubernetes. This video explains how to use the MiNiFi C++ agent as a side-car pod or as a DaemonSet to collect logs from Kubernetes applications. It goes through many examples and demonstrations to get you started with your own deployments. Don’t hesitate to reach out to Cloudera to get more details and discuss further options and integrations with Edge Flow Manager.

This video explains the Agent Manager view introduced with the 1.4 release. The main goal of this view was to give the user better understanding and more control over the agents in the system. Monitoring individual agents’ health becomes easier as you can see rich details about them. From the Agent Details view, you can also request and download debug logs from the agents, so in case of any issues you don’t need to log in to the agent’s environment. The highly customizable main table and the different tabs (details, alerts, commands and properties) are explained in detail.