How to do real-time analytics across historical and live data

Today’s analytical requirements are putting unprecedented pressures on existing data infrastructures. Performing real-time analytics across operational and stored data is typically critical to success but always challenging to implement. To read this article in full, please click here (Insider Story) from InfoWorld Big Data https://ift.tt/31CzJff via IFTTT

HPE plus MapR: Too much Hadoop, not enough cloud

Cloud killed the fortunes of the Hadoop trinity—Cloudera, Hortonworks, and MapR—and that same cloud likely won’t rain success down on HPE, which recently acquired the business assets of MapR. While the deal promises to marry “MapR’s technology, intellectual property, and domain expertise in artificial intelligence and machine learning (AI/ML) and analytics data management” with HPE’s… Continue reading HPE plus MapR: Too much Hadoop, not enough cloud

Julia vs. Python: Which is best for data science

Among the many use cases Python covers, data analytics has become perhaps the biggest and most significant. The Python ecosystem is loaded with libraries, tools, and applications that make the work of scientific computing and data analysis fast and convenient. But for the developers behind the Julia language — aimed specifically at “scientific computing, machine learning,… Continue reading Julia vs. Python: Which is best for data science

TensorFlow 2 review: Easier, end-to-end machine learning

The importance of machine learning and deep learning is no longer in doubt. After decades of promise, hype, and disappointment, both have led to practical applications. We haven’t gotten to the point where machine learning or deep learning applications are perfect, but many are very good indeed. To read this article in full, please click… Continue reading TensorFlow 2 review: Easier, end-to-end machine learning