The term Data Estate cropped up a little over a year ago, but what exactly does it mean?

Is it marketing-speak or something more profound?

In this DataPoint, I elaborate on the term “Data Estate” in front of an actual estate in Potomac, MD.

Press the play button below to listen here or visit the show page at DataDriven.tv

In this DataPoint, Frank talks about the term “Data Estate” in front of an actual estate.

With the rapid emergence of digital devices, an unstoppable, invisible force is changing human lives in incredible ways. That force is data. We generated more data in 2017 than in all the previous 5,000 years of human history.

The massive gathering and analyzing of data in real time is allowing us to address some of humanity’s biggest challenges but as Edward Snowden and the release of NSA documents have shown, the accessibility of all this data comes at a steep price.

This documentary captures the promise and peril of this extraordinary knowledge revolution.

Spark is gaining momentum in the big data space. Watch this video for a demonstration of how you can use your favorite developer tools to debug Spark applications.

Product info: azure.microsoft.com/en-us/services/hdinsight/apache-spark/
Learn more: docs.microsoft.com/en-us/azure/hdinsight/spark/apache-spark-load-data-run-query
Documentation: docs.microsoft.com/en-us/azure/hdinsight/spark/apache-spark-intellij-tool-debug-remotely-through-ssh

Raghav Mohan joins Scott Hanselman to talk about Apache Kafka on HDInsight, which added the open-source distributed streaming platform last year to complete a scalable, big data streaming scenario on Azure.

Kafka is capable of processing millions of events/sec, petabytes of data/day to power scenarios like Toyota’s connected car, Office 365’s clickstream analytics, fraud detection for large banks, etc.

Find out how to deploy managed, cost-effective Kafka clusters on Azure HDInsight with a 99.9% SLA with just 4 clicks or pre-created ARM templates.

For more information, see:

Deep learning and AI are fundamentally changing the way data is used in computation. They enable computing capabilities that will transform almost every industry, scientific domain, and public usage of data and compute.

The recent success of deep learning algorithms can be seen as the culmination of decades of progress in three areas: research in DL algorithms, broad availability of big data infrastructure, and the massive growth of computation power produced by Moore’s law and the advent of parallel compute architectures.

Deep learning has been employed successfully in such diverse areas as healthcare, transportation, industrial IoT, finance, entertainment, and retail, in addition to high-performance computing.

Examples shown in this video illustrate how the approach works and how it complements high-performance data analytics and traditional business intelligence.