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Analytics

Experience the power of big data with Apache Spark and Cloud Pak for Data

Blog | by Shikha Garg

Experience the power of big data with Apache Spark and Cloud Pak for Data

In the latest release of IBM Cloud Pak for Data, v2.5 has three key themes: Red Hat integration, new key built-in capabilities like Watson tools and runtimes, and a heavy focus on open source .(https://www.ibmbigdatahub.com/blog/announcing-cloud-pak-for-data-2-5). Open source is widely adopted in
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Data Science

The cost of data warehouse appliance complexity: Comparing IAS and IntelliFlex

Blog | by Holly Vatter

The cost of data warehouse appliance complexity: Comparing IAS and IntelliFlex

In a previous blog, I explained how data science capabilities, massive parallel processing (MPP) and usability improvements in data warehouse appliances can help the bottom line—and why old-fashioned architectures might not cut it. But what does that look like in practice? Research firm Quark +
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Data Science

How a data warehouse appliance can help your data scientists deliver insights faster

Blog | by Holly Vatter

How a data warehouse appliance can help your data scientists deliver insights faster

Learn why you need a ata-science-focused appliance like the IBM Integrated Analytics System, offering a 45 percent lower 5-year TCO over the competition.
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Big Data

Ingest and Analyze Streaming Event Data at Scale with IBM EventStore

Blog | by Siva Anne

Ingest and analyze streaming event data at scale with IBM Db2 EventStore

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.
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Analytics

Propelling the Future of Big Data and Data Science

Blog | by Jessica Lee

Propelling the future of big data and data science

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
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Analytics

Blog | by Aylee Nielsen

9 experts answer your top data science & machine learning questions

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
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Hadoop

#ITIQSpotlight: Trends and challenges in hybrid cloud analytics

Blog | by Andrea Braida

#ITIQSpotlight: Trends and challenges in hybrid cloud analytics

In any successful modern organization, analytics is likely to play a central role in helping decision-makers design and execute effective business strategies. At IBM, as we work with clients across the globe, we’re seeing ever-increasing levels of maturity and confidence in data-driven business
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Big Data

Sparking change: How analytics is helping global communities improve water security

Blog | by Andrea Braida

Sparking change: How analytics is helping global communities improve water security

Most of us are privileged enough to live in societies where clean water is a given. When our physicians and the media advise us to stay hydrated, it’s a simple health consideration. But in many countries, access to water is a huge issue – approximately one third of the world’s population does not
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Big Data

Building a cognitive data lake with ODPi-compliant Hadoop

Blog | by Andrea Braida

Building a cognitive data lake with ODPi-compliant Hadoop

For today’s data scientists and data engineers, the data lake is a concept that is both intriguing and often misunderstood. While there are many good resources about data lakes on ibm.com and other websites, there is also a lot of hype and spin. As a result, it can be difficult to get a clear
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Use Cases

Incorporating machine learning in the data lake for robust business results

Blog | by Karan Sachdeva

Incorporating machine learning in the data lake for robust business results

Building a data lake is one of the stepping stones towards data monetization use cases and many other advance revenue generating and competitive edge use cases. What are the building blocks of a “cognitive trusted data lake” enabled by machine learning and data science?
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Data Science

How does machine learning work?

Blog | by Bob Yelland

How does machine learning work?

Machine learning—a branch of artificial intelligence—is changing not only how we interact with machines, but how we relate to the world around us.
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Data Science

PySpark: High-performance data processing without learning Scala

White papers & Reports

PySpark: High-performance data processing without learning Scala

This white paper discusses the advantages of using the PySpark API, which enables the use of Python to interact with the Spark programming model. It starts with a basic description of Spark and then describes PySpark, its benefits, and when it is appropriate to use instead of "pandas" open source
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Analytics

Video | 1:11:53

IBM Machine Learning Event: The dawn of continuous intelligence, part 2

Learn about IBM's breakthroughs in Machine Learning, data, and analytics. This is a replay of IBM's Machine Learning Event which took place in New York City on February 15th.
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Analytics

IBM Machine Learning Event: The dawn of continuous intelligence, part 1

Video | 1:52:28

IBM Machine Learning Event: The dawn of continuous intelligence, part 1

Learn about IBM's breakthroughs in Machine Learning, data, and analytics. This is a replay of IBM's Machine Learning Event which took place in New York City on February 15, 2017.
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Data Science

Internet of Things and the autonomous vehicle: Simultaneous localization and mapping (SLAM)

Podcast | 10:43

Internet of Things and the autonomous vehicle: Simultaneous localization and mapping (SLAM)

J White Bear is a data scientist and software engineer at IBM. In this podcast, White Bear discusses simultaneous localization and mapping, an ongoing research area in robotics for autonomous vehicles and well-recognized as a nontrivial problem space in both industry and research.

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