Typically, ingesting streaming event data, persisting with low latency and analyzing it along with historical event data requires integrating multiple analytic systems. IBM EventStore is purpose built to simplify the complexity of harnessing event data with a single system. Its unique architecture
Data already is the new currency and is at the heart of everything digital. I like to repeat the adage, “Data becomes Information, becomes Knowledge, becomes Wisdom”. And “It’s all about the data”. So why do we send up probes, sensors or satellites — for the data?
On June 13th 2017, Hortonworks and IBM announced an extension of our partnership. A key part of this partnership is the collaboration on IBM Data Science Experience (DSX). This collaboration is win-win in that it brings a production-ready full-cycle data science experience to Hortonworks Data
Universal connectivity is fueling streams of event data from a variety of event sources. Increasingly, organizations are developing and deploying event driven applications to harness the growing volumes of event data. IBM EventStore offers a scalable integrated system for enterprises to ingest,
In the connected world of today’s digital economy, apps, IoT devices, vehicles, appliances and servers are generating endless stream of event data. The stream of events describes what is happening over time and offers the opportunity to track and analyze things as they happen.
The latest executive report published by IBM Institute for Business Value puts the estimated cost of cyber crime to the global economy in a range of USD 375–575 billion per year. Reputational damage, which is hard to calculate, comes on top of all this. No industry and geography has remained
Data is a potent business resource and the key to gaining and maintaining competitive advantage. Last month, IBM and Hortonworks announced a partnership to bring data science to the world on an open platform, offering Hortonworks Data Platform (HDP) along with IBM Data Science Experience (DSX) and
Recently, I had the honor of speaking with a number of the world’s most influential thought-leaders in the fields of data science, data analytics, machine learning and digital transformation. This group of prominent data technologists was more than happy to answer a wide variety of question on
Perhaps one the single most significant changes to the analytics landscape in recent years had been the emergence of the data scientist. This role is continuing to evolve, with many organizations still in the process of establishing how best to incorporate this relatively new discipline into their
Many of today’s top business performers successfully leverage a discipline – data science. Machine learning is one major way to apply data science and with machine learning, the more data we feed in, the better it performs. However, much of the world’s value data cannot be found on the Internet. It
The data lake can be considered the consolidation point for all of the data which is of value for use across different aspects of the enterprise. There is a significant range of the different types of potential data repositories that are likely to be part of a typical data lake.
It’s easy to be blinded (and impressed) with the rapid innovation and evolution in the arena of big data. Today’s most technically sophisticated companies have the opportunity to exploit big data tools to address mind-numbingly cool use cases and produce very enticing results. However, so many
This is the fourth in a series of blogs on analytics and the cloud. Read our introduction to the series. This blog concerns itself with the rise of open source software and how it is used for a whole host of analytical purposes. However, as will be seen in this blog, there are significant gaps in