08 May 2017knime_admin

Authors: Iris Adä & Phil Winters

The benefits of using predictive analytics is now a given. In addition, the Data Scientist who does that is highly regarded but our daily work is full of contrasts. On the one hand, you can work with data, tools and techniques to really dive in and understand data and what it can do for you. On the other hand, there is usually quite a bit of administrative work around accessing data, massaging data and then putting that new insight into production - and keeping it there.

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24 Apr 2017knime_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?

Follow us here and send us your ideas for the next data blending challenge you’d like to see at willtheyblend@knime.com.

Today: Teradata Aster meets KNIME Table. What is that chest pain?

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10 Apr 2017rs

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?

Follow us here and send us your ideas for the next data blending challenge you’d like to see at willtheyblend@knime.com.

Today: Blending Databases. A Database Jam Session

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27 Mar 2017knime_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?

Follow us here and send us your ideas for the next data blending challenge you’d like to see at willtheyblend@knime.com.

Today: YouTube Metadata meet WebLog Files. What will it be tonight – a movie or a book?

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13 Mar 2017knime_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?

Follow us here and send us your ideas for the next data blending challenge you’d like to see at willtheyblend@knime.com.

Today: Kindle epub meets image JPEG: Will KNIME make peace between the Capulets and the Montagues?

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06 Mar 2017greglandrum

We’ve had a couple of posts in the past about creating RESTful services with the KNIME Analytics Platform and using the REST API provided by the KNIME Server. But since we keep adding functionality and making things easier, it’s worthwhile to occasionally come back and revisit the topic. This post will demonstrate a couple of changes since Jon’s last update.

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27 Feb 2017phil

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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20 Feb 2017rs

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 Feb 2017greglandrum

One of the cool new features of KNIME Analytics Platform 3.3 is the ability to use the Java Snippet node with objects and functions that are defined in KNIME extensions. This allows an interesting and powerful new way to work with extensions that are compatible with the new functionality (more on the small amount of work required for that in a separate post, but for those who want to get a head start, here’s a link to the commit that added the functionality to the RDKit nodes).

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06 Feb 2017Vincenzo

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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