Systems | Development | Analytics | API | Testing

Traditional tools for managing data integrity, such as data quality, governance and stewardship tools, were targeted at the most skilled data experts. With the advent of social networks, machine learning and smart pattern recognition technologies, these tools are getting simpler at every release. They now allow anyone with market or customer knowledge to contribute and collaborate in a data governance effort.

The answer to creating an inclusive data culture is in your hands. At Yellowfin, we firmly believe that organisations are far more successful when all their people engage with data. And whilst this has always been the goal of organisations on their journey to being “data-led”, the reality is most are still a long way off. For this to happen, all decision makers need access to insights, in a way that they can understand, not just the data analysts.

We've got a brand new mobile application coming now out and lots of people have been asking me what’s in it. To be honest, it's much easier to tell you what's not in it - and that’s dashboards and reports. I can hear you saying, ‘How can an analytics vendor have a mobile app with no dashboards or reports?’ But the truth is no one really uses a mobile app to view dashboards or reports. It's the wrong format for looking at a clunky dashboard or detailed report.

Kraken is a load testing solution currently deployed on Docker. In order to use several injectors (Gatling) while running a load test, its next version might rely on Kubernetes. This blog post belongs to a series that describe how to use Minikube, declarative configuration files and the kubectl command-line tool to deploy Docker micro-services on Kubernetes. It focuses on the installation of an Angular 8 frontend application served by an NGinx Ingress controller.

We are super excited to announce our support for Azure Databricks! We continue to build out the capabilities of the Unravel Data Operations platform and specifically support for the Microsoft Azure data and AI ecosystem teams. The business and technical imperative to strategically and tactically architect the journey to cloud for your organization has never been stronger.

Ever stopped to think how accurate your data is? If you are making business decisions on data, it's obvious that you will want that data to be correct. With businesses losing an estimated $100 of revenue for each dirty record, data collection and accuracy becomes increasingly important to business success.

Talend provides a number of Machine Learning components that can be used for a variety of purposes. I have previously described some of these various components, some in more detail than others, as well as outlining what they can do. However, one question remains, what use cases can be solved by using these Machine Learning components?