Systems | Development | Analytics | API | Testing

Businesses today have a growing demand for real-time data integration, analysis, and action. More often than not, the valuable data driving these actions—transactional and operational data—is stored either on-prem or in public clouds in traditional relational databases that aren’t suitable for continuous analytics.

Continuous evaluation—the process of ensuring a production machine learning model is still performing well on new data—is an essential part in any ML workflow. Performing continuous evaluation can help you catch model drift, a phenomenon that occurs when the data used to train your model no longer reflects the current environment.

The data lakes concept has come back into popular focus with Amazon Athena, an innovative, serverless solution. But does it fit into your organization’s data stack? This article covers Amazon Athena capabilities, pros and cons, competitors, and use cases.

COVID-19 vaccines from various manufacturers are being approved by more countries, but that doesn’t mean that they will be available at your local pharmacy or mass vaccination centers anytime soon. Creating, scaling-up and manufacturing the vaccine is just the first step, now the world needs to coordinate an incredible and complex supply chain system to deliver more vaccines to more places than ever before.

Innovative organizations need DataOps and new technologies because old-school data integration is no longer sufficient. The traditional approach creates monolithic, set-in-concrete data pipelines that can’t convert data into insights quickly enough to keep pace with business. The following trends are driving the adoption of Hitachi’s Lumada DataOps Suite.

Thanks for all those who enthusiastically responded to my first blog post on Qlik analytics with Peloton! Now, onward brave souls as we learn HOW I was able to create the analytics I wrote about earlier!