09 Jan 2017bjoern.lohrmann

The ever-increasing application of big data technology brings with it the need to secure access to the data in your Hadoop cluster. Security in earlier Hadoop days only offered protection against accidental misuse as not even user credentials were properly enforced. Before Hadoop could be a true multi-user platform, where organizations can put all of their data and host many different applications, the security issue obviously had to be tackled. The Hadoop community addressed this problem by adopting the venerable Kerberos protocol.

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20 Dec 2016knime_admin

In this blog series we’ll be experimenting with the most interesting blends of data and tools. Whether it’s mixing traditional sources with modern data lakes, open-source devops on the cloud with protected internal legacy tools, SQL with noSQL, web-wisdom-of-the-crowd with in-house handwritten notes, or IoT sensor data with idle chatting, we’re curious to find out: will they blend? Want to find out what happens when IBM Watson meets Google News, Hadoop Hive meets Excel, R meets Python, or MS Word meets MongoDB?

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13 Dec 2016Vincenzo

In this blog series we’ll be experimenting with the most interesting blends of data and tools. Whether it’s mixing traditional sources with modern data lakes, open-source devops on the cloud with protected internal legacy tools, SQL with noSQL, web-wisdom-of-the-crowd with in-house handwritten notes, or IoT sensor data with idle chatting, we’re curious to find out: will they blend? Want to find out what happens when IBM Watson meets Google News, Hadoop Hive meets Excel, R meets Python, or MS Word meets MongoDB?

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05 Dec 2016oyasnev

Did you ever ask yourself, while using KNIME Analytics Platform, “What should I do next?” Or “How can I use this node?” Or “What on earth is this parameter for?!” No matter if you are new to  KNIME or already an expert, I’m sure you have asked these questions sometimes and might still be wondering about them.

There are many ways already to find the answers.

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28 Nov 2016rs

In this blog series we’ll be experimenting with the most interesting blends of data and tools. Whether it’s mixing traditional sources with modern data lakes, open-source devops on the cloud with protected internal legacy tools, SQL with noSQL, web-wisdom-of-the-crowd with in-house handwritten notes, or IoT sensor data with idle chatting, we’re curious to find out: will they blend? Want to find out what happens when IBM Watson meets Google News, Hadoop Hive meets Excel, R meets Python, or MS Word meets MongoDB?

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22 Nov 2016Vincenzo

In this blog series we’ll be experimenting with the most interesting blends of data and tools. Whether it’s mixing traditional sources with modern data lakes, open-source devops on the cloud with protected internal legacy tools, SQL with noSQL, web-wisdom-of-the-crowd with in-house handwritten notes, or IoT sensor data with idle chatting, we’re curious to find out: will they blend? Want to find out what happens when IBM Watson meets Google News, Hadoop Hive meets Excel, R meets Python, or MS Word meets MongoDB?

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14 Nov 2016rs

In this blog series we’ll be experimenting with the most interesting blends of data and tools. Whether it’s mixing traditional sources with modern data lakes, open-source devops on the cloud with protected internal legacy tools, SQL with noSQL, web-wisdom-of-the-crowd with in-house handwritten notes, or IoT sensor data with idle chatting, we’re curious to find out: will they blend? Want to find out what happens when IBM Watson meets Google News, Hadoop Hive meets Excel, R meets Python, or MS Word meets MongoDB?

Read more


07 Nov 2016rs

In this blog series we’ll be experimenting with the most interesting blends of data and tools. Whether it’s mixing traditional sources with modern data lakes, open-source devops on the cloud with protected internal legacy tools, SQL with noSQL, web-wisdom-of-the-crowd with in-house handwritten notes, or IoT sensor data with idle chatting, we’re curious to find out: will they blend? Want to find out what happens when IBM Watson meets Google News, Hadoop Hive meets Excel, R meets Python, or MS Word meets MongoDB?

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21 Oct 2016rs

A short description of data analytics project formalization
through some of the whitepapers developed over time here at KNIME AG

It is not hard nowadays to find talks from conferences and blog posts on the web claiming that data analytics, or data science as it is now called, can do wonders for your company. Sure! However, identification of the relevant problems and their formalization into available data vs. the desired output remain the biggest obstacles to a realistic implementation of any data-driven project.

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30 Sep 2016Vincenzo

The Semantic Web

According to the W3C Linked Data page, the Semantic Web refers to a technology stack to support the “Web of data”. Semantic Web technologies enable people to create data stores on the Web, build vocabularies, and write rules for handling data. Linked data are empowered by technologies such as RDF, SPARQL, OWL, and SKOS.

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