Cloudera Flow Management, based on Apache NiFi and part of the Cloudera DataFlow platform, is used by some of the largest organizations in the world to facilitate an easy-to-use, powerful, and reliable way to distribute and process data at high velocity in the modern big data ecosystem. Increasingly, customers are adopting CFM to accelerate their enterprise streaming data processing from concept to implementation.

The Snowflake Data Cloud provides the unique ability for anyone to join their own data sets with thousands of live third-party data sets near-instantly, securely, and without moving data. Businesses operating in the Data Cloud gain a huge advantage over their competitors who are stuck in data silos and struggling with stale data sets downloaded from their legacy data providers weeks, months, or years ago.

Before the end of the year, I met with Christina McCoy, a Customer Success Manager for our federal region. We discussed some of her observations and proven practices for driving adoption in her accounts. Christina joined Qlik two years ago; she has over five years of CSM experience and is a graduate of Howard University, where she was a pitcher for the Bison’s softball program.

Data science has become an important capability for enterprises looking to solve complex, real-world problems, and generate operational models that deliver business value across all domains. More and more businesses are investing in ML capabilities, putting together data science teams to develop innovative, predictive models that provide the enterprise with a competitive edge — be it providing better customer service or optimizing logistics and maintenance of systems or machinery.

One particular feature requested by our customers is 3D body mapping, and whether exporting 3D models and utilizing its visualization and filtering can be applied easily. This technical walkthrough shows you how to leverage Yellowfin to integrate 3D models within Yellowfin and then use them to create a fully interactive display in your dashboard.

Data is produced and consumed at volumes and speeds which were unimaginable just a decade ago.Top players have taken advantage of this growth. Tapping into data resources for actionable insights - aptly called the new oil - makes data-driven companies dominate their competition. But the proliferation of data can lead to growing pains. Companies find themselves increasingly incapacitated by the vast and messy nature of their in-house data.

Building machine learning (ML) and deep learning (DL) models obviously require plenty of data as a training-set and a test-set on which the model is tested against and evaluated. Best practices related to the setup of train-sets and test-sets have evolved in academic circles, however, within the context of applied data science, organizations need to take into consideration a very different set of requirements and goals. Ultimately, any model that a company builds aims to address a business problem.

Most blogs in my history are very focused on Industry 4.0’s digital transformation of the manufacturing industry, which in itself is pretty remarkable. By 2025, Industry 4.0 is expected to generate greater than $11 trillion in economic value as connected manufacturing processes, operations and their supply chains become more streamlined, efficient, agile and realize improved productivity, improved uptime and product quality.