By 2025, there will be 180 trillion gigabytes of data in the world, compared to only 10 trillion gigabytes in 2015. Of this, 90 percent will be unstructured, which is why many organizations are adopting open source data lake technologies such as Apache Hadoop to handle this expanding volume and
Human beings tend to filter out events they deem unimportant. They can only process so much at any given time. Computer systems, however, must be able to handle a massive number of events in real time or near-real time to help support a wide range of applications.
The data lake may be all about Apache Hadoop, but integrating operational data can be a challenge. Learn how to deliver real-time feeds of transactional data from mainframes and distributed environments directly into Hadoop clusters and make constantly changing data more available.
The new Gartner Magic Quadrant (MQ) for Master Data Management has been published, and what you might not notice at first glance is that this year, IBM chose not to participate. Gartner still included IBM in the MQ. However, we did decline to engage in the process and provide detailed data for
Managing enterprise information has always been a good idea, however with the potential for looming penalties from the General Data Protection Regulation (GDPR) non-compliance, companies are waking up and some organizations are even seeing GDPR as an opportunity to establish strengthened
Organizations everywhere, from massive governments to the smallest start-ups, are in a race for the best-possible data expertise and tools. To help your team understand the data science journey, IBM created the Data Science for All webcast.
Although there are many new and emerging classes of data integration, quality and governance software tools available in the market, many large organizations are coming to the conclusion that they're best served by a single unified enterprise data integration, quality and governance platform that
Learn how the IBM Integrated Analytics System, a unified data platform built on the IBM Common SQL Engine, helps do data science faster with high performance, embedded machine learning capabilities and built-in tools for data scientists to deliver analytics critical to increasing your organization’
Typically, ingesting streaming event data, persisting with low latency and analyzing it along with historical event data requires integrating multiple analytic systems. IBM Db2 EventStore is purpose built to simplify the complexity of harnessing event data with a single system. Its unique
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 Db2 EventStore offers a scalable integrated system for enterprises to ingest