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: A Recipe for Delicious Data: Mashing Google and Excel Sheets

The Challenge

Don’t be confused! This is not one of the data chef battles, but  a “Will they blend?” experiment - which, just by chance, happens to be on a restaurant theme again.

A local restaurant has been running its business relatively successfully for a few years now. It is a small business. An Excel Sheet was enough for the full accounting in 2016. To simplify collaboration, the restaurant owner decided to start using Google Sheets at the beginning of 2017. Now (2017 with Google Sheets) she faces the same task every month of calculating the monthly and YTD revenues and comparing them with the corresponding prior-year values (2016 with Microsoft Excel). 

The technical challenge at the center of this experiment is definitely not a trivial matter: mashing the data from the Excel and Google spreadsheets into something delicious… and digestible. Will they blend?

Continental, a leading automotive supplier, recently won the Digital Leader Award 2017 in the category “Empower People” for bringing big data and analytics closer to its employees with KNIME. Arne Beckhaus is the man behind this project. We are lucky enough today to welcome him for an interview in our KNIME blog.

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?

In a social networking era where a massive amount of unstructured data is generated every day, unsupervised topic modeling has became a very important task in the field of text mining. Topic modeling allows you to quickly summarize a set of documents to see which topics appear often; at that point, human input can be helpful to make sense of the topic content. As in any other unsupervised-learning approach, determining the optimal number of topics in a dataset is also a frequent problem in the topic modeling field.

Everyone who has heard of KNIME Analytics Platform knows that KNIME has nodes. Thousands of them! The resources under the Learning Hub as well as the hundreds of public examples within KNIME Analytics Platform are all designed to get you up to speed with KNIME and its nodes. But those that know best how to use KNIME nodes are KNIME users themselves. What if we could capture all their insight and experience in understanding which nodes to use when and in what order and give you a recommendation? Well that is exactly what the KNIME Workflow Coach does.

Do you remember the Iron Chef battles? 

It was a televised series of cook-offs in which famous chefs rolled up their sleeves to compete in making the perfect dish. Based on a set theme, this involved using all their experience, creativity and imagination to transform sometimes questionable ingredients into the ultimate meal.

Hey, isn’t that just like data transformation? Or data blending, or data manipulation, or ETL, or whatever new name is trending now? In this new blog series requested by popular vote, we will ask two data chefs to use all their knowledge and creativity to compete in extracting a given data set's most useful “flavors” via reductions, aggregations, measures, KPIs, and coordinate transformations. Delicious!

Want to find out how to prepare the ingredients for a delicious data dish by aggregating financial transactions, filtering out uninformative features or extracting the essence of the customer journey? Follow us here and send us your own ideas for the “Data Chef Battles” at datachef@knime.com.

Ingredient Theme: Customer Transactions. Money vs. Loyalty.

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.

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?

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

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?