Systems | Development | Analytics | API | Testing

DataOps Observability: The Missing Link for Data Teams

As organizations invest ever more heavily in modernizing their data stacks, data teams—the people who actually deliver the value of data to the business—are finding it increasingly difficult to manage the performance, cost, and quality of these complex systems. Data teams today find themselves in much the same boat as software teams were 10+ years ago. Software teams have dug themselves out the hole with DevOps best practices and tools—chief among them full-stack observability.

Adverity is Powered by Snowflake-and Moving into New Markets with Confidence

What’s harder than finding the right data architecture? Finding the right dedicated partner. Adverity gets both with Snowflake. Learn how the two organizations are moving into new markets and supplying even more reliable marketing data to Adverity customers. When a fast-growing SaaS business looks to expand its client base, it normally encounters two major challenges: In many cases, an external data solution provider can only help solve the scalability challenge.

Demystifying Modern Data Platforms

July brings summer vacations, holiday gatherings, and for the first time in two years, the return of the Massachusetts Institute of Technology (MIT) Chief Data Officer symposium as an in-person event. The gathering in 2022 marked the sixteenth year for top data and analytics professionals to come to the MIT campus to explore current and future trends. A key area of focus for the symposium this year was the design and deployment of modern data platforms.

Chose Both: Data Fabric and Data Lakehouse

A key part of business is the drive for continual improvement, to always do better. “Better” can mean different things to different organizations. It could be about offering better products, better services, or the same product or service for a better price or any number of things. Fundamentally, to be “better” requires ongoing analysis of the current state and comparison to the previous or next one. It sounds straightforward: you just need data and the means to analyze it.

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.

The Modern Data Lakehouse: An Architectural Innovation

Imagine having self-service access to all business data, anywhere it may be, and being able to explore it all at once. Imagine quickly answering burning business questions nearly instantly, without waiting for data to be found, shared, and ingested. Imagine independently discovering rich new business insights from both structured and unstructured data working together, without having to beg for data sets to be made available.

Kubernetes Logs Collection with MiNiFi C++

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.