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Neo4j: Detecting rogue spaces in CSV headers with LOAD CSV


Last week I was helping someone load the data from a CSV file into Neo4j and we were having trouble filtering out rows which contained a null value in one of the columns. This is what the data looked like: load csv with headers from "file:///foo.csv" as row RETURN row ╒══════════════════════════════════╕ │row │ ╞══════════════════════════════════╡ │{key1: a, key2: (null), key3: c}│ ...

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The Value in Project Valhalla


I have been interested in the progress of Project Valhalla for quite a while, but Brian Goetz‘s recent message “Project Valhalla: Goals” has raised my level of interest. I have frequently enjoyed Goetz‘s writing because he combines two characteristics I want most in a technical author: he knows the subjects he writes about much more deeply than what he is ...

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Command Interfaces: Approaching Redis with dynamic APIs in Java


Redis is a data store supporting over 190 documented commands and over 450 command permutations. The community supports actively Redis development; each major Redis release comes with new commands. This year Redis was opened up for 3rd party vendors to develop modules that extend Redis functionality. Command growth and keeping track with upcoming modules are challenging for client developers and ...

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Windows Server 2016 Using VirtualBox – Getting Ready for Docker


Windows  Server 2016 was announced a few weeks ago.  Download Windows 2016 Server Evaluation version. This blog is the first part of a multi-part series. The first part will show how to set up Windows Server 2016 as a VirtualBox VM. This is an update to Windows Server 2016 using VirtualBox for Docker Containers. Download Windows 2016 Server Evaluation version.  Its ~5GB download so try on ...

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Getting Started With JHipster, Part 3


Welcome back to this JHipster tutorial series! In part one we covered creating a monolithic application. In part two, we walked through creating a microservice application (which was a bit more complicated). For those of you out there working to get things going with JHipster, there are some setup things and “gotchas” that I’d like to highlight. So this little ...

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Predicting Breast Cancer Using Apache Spark Machine Learning Logistic Regression


In this blog post, I’ll help you get started using Apache Spark’s Logistic Regression for predicting cancer malignancy. Spark’s library goal is to provide a set of APIs on top of DataFrames that help users create and tune machine learning workflows or pipelines. Using with DataFrames improves performance through intelligent optimizations. Classification Classification is a family of ...

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LAMP Stacks for IoT


The Eclipse IoT community has grown considerably over the last 12 months. More importantly we have expanded the breadth of technology to include software that runs on devices, at the edge and the cloud. We have also learnt a lot about the software functionality required to build an IoT solution. It has always been clear that for IoT to be successful ...

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Complex event processing (CEP) with Apache Storm and Apache Ignite


In this article, author of the book “High Performance in-memory computing with Apache Ignite”, will discuss the complex event processing with Apache Strom and the Apache Ignite. Part of this article taken from the complex event processing chapter of the book. There is no broadly or highly accepted definition of the term Complex Event Processing or CEP. What Complex Event Processing ...

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