The combination of Jupyter Notebooks, Apache Hadoop and Apache Spark has become a killer app for data practitioners. It unlocks the ability to explore, visualize and experiment with both structured and unstructured data sets with great ease and efficiency. We spoke recently with Chris Snow at IBM
Whether organizations want to extract customer data beyond names and addresses from unstructured data sources; pull specific dates, times or monetary amounts; predict trends from sentiment data; or engage in many other uses, text analytics is the way to go. Learn the details of text analytics, and
Advanced analytics, reporting and aggregation software for social media is everywhere these days. As a social media manager, my job is to keep testing out these tools all the time. In all of this testing, seeing a disruptive product that makes you stop and think is rare. And yet I’m pleased to say
How can you make the most of Hadoop in your enterprise? Create a pattern of success in your organization by incorporating Hadoop in your broader data architecture as a way of providing meaningful insights to your company.
Text mining is the next step in data mining, offering advanced capabilities for extracting meaning from vast, amorphous masses of data. Despite its complexity, text mining has much to offer businesses—and the list is growing. Discover what text mining could mean for your organization today.
When customers or other key stakeholders expect to be able to connect with an organization instantaneously, extremely low latency, high throughput data and analytics flows and execution are absolutely essential. The advent of the Internet of Things is among several key drivers of the emergence of
Streaming analytics is becoming ubiquitous as data streams from a range of sources, including the Internet of Things, are now mainstream. Although streaming analytics is not a new technology, it is well suited for today’s real-time, low-latency business and consumer applications. And today’s data
To fulfill the promise of analytics, we must put a lot more effort into delivering these projects right, the first time. We must think through each of the traditional success criteria and ask ourselves the burning question: how is delivering analytics different? This starts from gaining executive