Systems | Development | Analytics | API | Testing

Enterprise data and analytics in the cloud with Microsoft Azure and Talend

The emergence of the cloud as a cost-effective solution to delivering compute power has caused a paradigm shift in how we approach designing, building, and delivering analytics to business users. Although forklifting an existing analytics environment into the cloud is possible, there’s substantial benefit for those that are willing to review and adjust their systems to capitalize on the strengths of the cloud.

Why a Real Device Testing Cloud is Good for Your Business: Mobile Test Automation Day Online

In this session, you will learn how cloud-based real device testing can reduce the total cost of ownership by 3X to 5X, eliminate the operational pain of maintenance and updates, and drive team productivity to deliver better and faster mobile app releases.

Scaling Kafka Brokers in Cloudera Data Hub

This blog post will provide guidance to administrators currently using or interested in using Kafka nodes to maintain cluster changes as they scale up or down to balance performance and cloud costs in production deployments. Kafka brokers contained within host groups enable the administrators to more easily add and remove nodes. This creates flexibility to handle real-time data feed volumes as they fluctuate.

Webinar: Unlocking the Value of Cloud Data and Analytics

From data lakes and data warehouses to data mesh and data fabric architectures, the world of analytics continues to evolve to meet the demand for fast, easy, wide-ranging data insights. Right now, nearly 50% of DBTA subscribers are using public cloud services, and many are investing further in staff, skills, and solutions to address key technical challenges. Even today, the amount of time and resources most organizations spend analyzing data pales in comparison to the effort expended in identifying, cleansing, rationalizing, consolidating, and transforming that data.

How to Do Data Labeling, Versioning, and Management for ML

It has been months ago when Toloka and ClearML met together to create this joint project. Our goal was to showcase to other ML practitioners how to first gather data and then version and manage data before it is fed to an ML model. We believe that following those best practices will help others build better and more robust AI solutions. If you are curious, have a look at the project we have created together.