This week in Neo4j - 21st October 2017

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Discover what's new in the Neo4j community for the week of 21 October 2017, including Neo4j Python driver 1.5.0, GraphConnect, and GraphQL summit.

Technology

The week's graph database news
in one handy slide deck!

It's GraphConnect NYC time!
graphconnect.com

Featured community member: Tim Ward
@jerrong

Version 1.5.0 of the Neo4j Python Driver was released this
week. The 1.5 series uses a least connections load
balancing strategy when interacting with clusters and adds
configuration options to give the
application developer more
control.
Python Driver 1.5.0 released
github.com/neo4j/neo4j-python-driver/releases/tag/1.5.0

App Developer Magazine have an interview with Dr Jim
Webber in which he explains how graph databases fit in
the NoSQL landscape, why native graph databases such as
Neo4j are most efficient for querying graph
data, and how the Panama Papers helped
project graphs into the mainstream.
An interview with Dr Jim Webber
appdevelopermagazine.com/5604/2017/10/17/Explaining-graph-databases-to-a-developer

The call for participation for the GraphDevRoom at
FOSDEM 2018 is now open. This is the 6th edition of the
GraphDevRoom and suggested topics for this year range
from graph query languages to knowledge graphs,
and from graph processing frameworks to
large scale graph visualisation
FOSDEM 2018 GraphDevRoom: Call for Participation
https://github.com/yantisj/netgrph

Analysing Debian packages with Neo4j
youtube.com/watch?v=lpqvv36SBQw
In this week's online meetup Norbert Preining showed us
how to load the Debian UDD into Neo4j
and write Cypher queries to explore
dependencies between packages.

If you liked this check out the blog post
https://neo4j.com/blog/this-week-neo4j-21-october-2017

Neo4j is a high performance, open-source graph database written in Java and Scala. But of course, you can use and extend it from other JVM Languages, like Kotlin. I've been using Kotlin on and off since 2012, that's when I wrote my first article about this pragmatic and clean programming language. Today I want to demonstrate in 4 examples how you can use Neo4j with Kotlin. In preparation for KotlinConf 2017 we started to gather community activity of the Kotlin community in a Neo4j graph. You can see tweets, GitHub projects, StackOverflow questions and answers and meetup events in this database of developer activity. After showing some examples we will explore how to query that database using a Kotlin driver for Neo4j. Then we'll look at the Kotlin + Spring (Data) demo app, that shows community members who get a lot of praise on a Twitter wall. My main Kotlin project over the last year has been the GraphQL extension for Neo4j. So we'll have a look at how we can extend Neo4j with a custom HTTP API and how we integrate the GraphQL-Java library using Kotlin. This extension manages a GraphQL schema to translate GraphQL queries to Neo4js native query language Cypher. Another way to extend Neo4j is with user-defined procedures and functions which (with a small trick) are really easy to implement with Kotlin too. We'll take a look at the procedures and functions we expose as part of the neo4j-graphql extension.

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Data Con LA 2020 Description Coming from a grand belief of data democratization, I believe that in order for any team to be successful collaborators, it has to be data centric and data should be accessible to all. *To ensure that your non software or software engineering centric team has maximum efficiency, data should be visible, data lake should be accessible. *Form a database for analytics summaries, talk about the different technologies(SQL, NoSQL) cost of deployment, need, team driven structure. Build an API for this database for external/inter team crosstalk. *Build analytics and visual layer on top of it. Flask/Django/Node, etc.., to enable the team to have high visibility in their analysis, and to ensure a higher turnaround of data. *Talk about an easy way of enabling the team to run code, could be local/cloud, JupyterHub is a great way of doing so, talk about the tremendous value added in that and the potential it enables *Talk about the common tools user for version control/CICD/Coding technologies, etc.. *Finally summarize the value of the mixture of all these tools and technologies in order to ensure the maximum efficiency. Speaker Nawar Khabbaz, Rivian, Data Engineer

Introduction to the IBM Watson Data Platform

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We've all been there before... you hear the announcement that your flight is canceled. Fellow passengers race to the gate agent to rebook on the next available flight. How do they quickly determine the best route from Berlin to San Francisco? Ultimately the flight route network is best solved as a graph problem. We will discuss our lessons learned from working with a major airline to solve this problem using JanusGraph database. JanusGraph is an open source graph database designed for massive scale. It is compatible with several pieces of the open source big data stack: Apache TinkerPop (graph computing framework), HBase, Cassandra, and Solr. We will go into depth about our approach to benchmarking graph performance and discuss the utilities we developed. We will share our comparison results for evaluating which storage backend use with JanusGraph. Whether you are productizing a new database or you are a frustrated traveler, a fast resolution is needed to satisfy everybody involved. Presented at DataWorks Summit Berlin on April 18, 2018

Time travel and time series analysis with pandas + statsmodels

Alexander Hendorf

Most data is allocated to a period or to some point in time. We can gain a lot of insight by analysing what happened when. The better the quality and accuracy of our data, the better our predictions can become. Unfortunately the data we have to deal with is often aggregated for example on a monthly basis, but not all months are the same, they may have 28 days, 31 days, have four or five weekends,… It’s made fit to our calendar that was made fit to deal with the earth surrounding the sun, not to please Data Scientists. Dealing with periodical data can be a challenge. Pandas is a powerful framework for working with time series data and can make your life a lot easier. This talks will feature: how to analyse periodical data with pandas read and write data in various formats how to mangle, reshape and pivot gain insights with statsmodels (e.g. seasonality) caveats when working with timed data visualize your data on the fly

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Start Flying with Python & Apache TinkerPop

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Gremlin, the graph traversal language from Apache TinkerPop, continues to evolve in support of the growing graph ecosystem. In this session, we'll take a deep dive into Gremlin Language Variants (GLV) to see how TinkerPop enables modern programming languages to leverage Gremlin natively. By converting Gremlin into bytecode, the same instructions can be transmitted and interpreted by graph systems from different vendors. We'll uncover the benefits of this approach by demonstrating a Python-based graph architecture built to empower your application developers and data scientists. By using popular packages from Python open source, like Flask microframework and Jupyter notebooks, we'll see how you can easily transition your app development from your machine to the IBM Cloud. Presented at Graph Day SF on June 17, 2017.

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Powers of Ten Redux

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One of the first problems a developer encounters when evaluating a graph database is how to construct a graph efficiently. Recognizing this need in 2014, TinkerPop's Stephen Mallette penned a series of blog posts titled "Powers of Ten" which addressed several bulkload techniques for Titan. Since then Titan has gone away, and the open source graph database landscape has evolved significantly. Do the same approaches stand the test of time? In this session, we will take a deep dive into strategies for loading data of various sizes into modern Apache TinkerPop graph systems. We will discuss bulkloading with JanusGraph, the scalable graph database forked from Titan, to better understand how its architecture can be optimized for ingestion. Presented at Data Day Texas on January 27, 2018.

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