This is going to be a bit different from our normal KNIME blog posts: instead of focusing on some interesting way of using KNIME or describing an example of doing data blending, I’m going to provide a personal perspective on why I think it’s useful to combine two particular tools: KNIME and Python. This came about because I keep getting questions like: “But you know Python really well, why would you use KNIME?” or “Now that you work at KNIME you aren’t really using Python anymore, right?”.

When to use one or the other?

So should you use Python or should you use KNIME?

Fortunately you don’t need to make this hard choice; it’s perfectly straightforward and, I think, quite productive to use both. It’s easy to take advantage of either tool from the other. I’ll spend most of the rest of this post looking at that. But there are areas where I think one tool or the other particularly shines.

Let’s start this post with a question. How many different algorithms do you know that can solve classification problems? There are lots! Decision Tree, Random Forest, Deep Learning, Logistic Regression, just to name a few options. How to choose? It is hard to say in advance. 

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: Finnish meets Italian and Portuguese through the Google Translate API. Preventing weather from getting lost in translation

Today we look at a dataset that supposedly is already clean, joined with the right additional information, and in the right shape and we want to use it to train a prediction model. Unfortunately, a quick glance at the dataset reveals that it still has tons of missing values, it is not normalized, and contains too many too similar features.

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: SugarCRM meets Salesforce. Crossing Accounts and Opportunities

The Challenge

Businesses use Customer Relationship Management (CRM) systems to keep track of all their customer related activities – creating leads and opportunities, managing contacts and accounts, sending quotes and invoices, etc. As long as it is somehow related to the stream of revenue, it is (or at least should be) stored in a CRM system.

Since there is more than one CRM solution on the market, there is a distinct chance that your organization uses multiple CRM platforms. While there might be sound reasons for this, it also poses a significant challenge: How do you combine data from several platforms? How do you generate a single, consolidated report that shows you how well the sales activities of your whole company are going?

One option is to export some tables, fire up your spreadsheet software of choice, and paste the stuff together. Then do the same thing next week. And the week after. And the week after that one (you get the point). Doesn’t sound too enticing? Fear not! This is KNIME, and one of our specialties is to save you the frustration of doing things manually. Fortunately, both SugarCRM and Salesforce allow their users to access their services via REST API, and that is exactly what we are going to do in this blog post.

There are a couple of prerequisites here. First of all, you obviously need accounts for SugarCRM and Salesforce. If you don’t have them but still want to try this yourself, you’ll be happy to see that both companies offer free trial licenses:

https://info.sugarcrm.com/trial-crm-software.html?utm_source=crmsoftware&utm_medium=referral&utm_campaign=crmsoftware-review

https://developer.salesforce.com/signup

You can learn more about how to use the REST APIs of SugarCRM and Salesforce here:

http://support.sugarcrm.com/Documentation/Sugar_Developer/Sugar_Developer_Guide_7.9/Integration/Web_Services/v10/

https://developer.salesforce.com/docs/atlas.en-us.api_rest.meta/api_rest/intro_what_is_rest_api.htm

We all know that just building a model is not the end of the line. However, deploying the model to put it into production is often also not the end of the story, although a complex one in itself (see our previous Blog Post on “The 7 Ways of Deployment”). Data scientists are increasingly often also tasked with the challenge to regularly monitor, fine tune, update, retrain, replace, and jump-start models - and sometimes even hundreds or thousands of models together.

The aim of this blog post is to highlight some of the key features of the KNIME Deeplearning4J (DL4J) integration, and help newcomers to either Deep Learning or KNIME to be able to take their first steps with Deep Learning in KNIME Analytics Platform.

Introduction

With a little bit of patience, you can run the example provided in this blog post on your laptop, since it uses a small dataset and only a few neural net layers. However, Deep Learning is a poster child for using GPUs to accelerate expensive computations. Fortunately DL4J includes GPU acceleration, which can be enabled within the KNIME Analytics Platform.

If you don’t happen to have a good GPU available, a particularly easy way to get access to one is to use a GPU-enabled KNIME Cloud Analytics Platform, which is the cloud version of KNIME Analytics Platform.

In the addendum at the end of this post we explain how to enable KNIME Analytics Platform to run deep learning on GPUs either on your machine or on the cloud for better performance.

The latest release of KNIME Analytics Platform 3.4 has produced many new features, nodes, integrations, and example workflows. This is all to give you a better all-round experience in data science, enterprise operations, usability, learning, and scalability.

Now, when we talk about scalability, the cloud often comes to mind. When we talk about the cloud, Microsoft Azure often comes to mind. That is the reason why KNIME has been integrating some of the Azure products and services.

The novelty of this latest release consists of the example material. If you currently access (or want to access in the future) some Microsoft products, on the cloud, in your KNIME workflow, you can start by having a look at the 11_Partners/01_Microsoft folder in the EXAMPLES server and at the following link on the KNIME Node Guide https://www.knime.com/nodeguide/partners/microsoft.

 A little note for the neophytes among us. The KNIME EXAMPLES server is a public KNIME server hosting a constantly growing number of example workflows (see YouTube video “KNIME EXAMPLES Server”). If you are new to a topic, let’s say “churn prediction”, and you are looking for a quick starting point, then you could access the EXAMPLES server in the top left corner inside the KNIME workbench, download the example workflow in 50_Applications/18_Churn_Prediction (50_Applications/18_Churn_Prediction/01_Training_a_Churn_Predictor50_Applications/18_Churn_Prediction/01_Training_a_Churn_Predictor^*), and update it to your data and specific business problem. It is very easy and one of the most loved features in the KNIME Analytics Platform.

We built a workflow to train a model. It works fast enough on our local, maybe not so powerful, machine. So far.

The data set is growing. Each month a considerable number of new records is added. Each month the training workflow becomes slower. Shall we start to think of scalability? Shall we consider big data platforms? Could my neat and elegant KNIME workflow be replicated on a big data platform? Indeed it can.

The KNIME Big Data Extensions offers nodes to build and configure workflows to run on the big data platform of choice. The cool feature of the KNIME Big Data Extensions consists in the nodes GUI. The configuration window for each Big Data node has been built as similar as possible to the configuration window of the corresponding KNIME node. The configuration window of a Spark Joiner node will look exactly the same as the configuration window of a Joiner node.

Thus, it is not only possible to replicate your original workflow on a Big Data Platform, it is also extremely easy, since you do not need to learn new scripts or tools instructions. The KNIME Big Data Extensions brings the ease of use of KNIME into the scalability of Big Data.

This video shows how we replicated an existing classical analytics workflow on a Big Data Platform.

The workflows used in the video can be found on the KNIME EXAMPLES server under 50_Applications/28_Predicting_Departure_Delays/02_Scaling_Analytics_w_BigData50_Applications/28_Predicting_Departure_Delays/02_Scaling_Analytics_w_BigData.knwf^*

Here's a familiar predicament: you have the data you want to analyze, and you have a trained model to analyze them. Now what? How do you deploy your model to analyze your data?

In this video we will look at seven ways of deploying a model with KNIME Analytics Platform and KNIME Server. This list has been prepared with an eye toward where the output of the deployment workflow goes:

• to a file or database
• to JSON via REST API
• to a dashboard via KNIME's WebPortal
• to a report and to email
• to SQL execution via SQL recoding
• to Java byte code execution via Java recoding
• to an external application

Once you know these options, you will also know which one best satisfies your needs.

The workflows used in the video can be found on the KNIME EXAMPLES server under 50_Applications/27_Deployment_Options50_Applications/27_Deployment_Options^*.