If you'll recall from several posts ago, the author had been attempting to write a web application based around the concept of a simple recommendation engine and a software retailer. The implementation was being done using a Spring-based Java stack with Neo4j as the data store.
Starting with an overview of relational databases and the history of NoSQL as a concept, the author breaks NoSQL down into its core components and analyzes each, exploring history, purpose, advantages, disadvantages, and specific examples for each type of database.
The author was pointed to Sarah Mei's blog post titled "Why You Should Never Use MongoDB," and thought he would comment from a RavenDB perspective. His summary: If you don't know how to tie your shoes, don't run.
Don’t let anybody fool you; a good bit of what a data scientist does is a glorified form of counting. In my recent work, I’m finding that Cassandra is quite good at counting. As a matter of fact, you can treat Cassandra as a giant, distributed, redundant, “infinitely” scalable counting framework.
Every week here and in our newsletter, we feature a new developer/blogger from the DZone community. This week we're talking to Mark Needham, developer at Neo Technology working on Neo4j, and (European) football enthusiast.
You may be curious: "Why not, exactly?" Answering that question is the central idea of Sarah Mei's recent blog post. She argues against the open-source document database - or at least the one-size-fits-all attitude some take with it - through the story of Diaspora, a social network to which she contributed.
In this article, the author is talking to Dr. Jim Webber about Neo4j and the value of graph databases, and how the major players' adoption of graphs have meant more attention for graph databases.
NoSQL was held up to the be the way to horizontally scale Big Data and Web applications and to bring a new level of simplicity to data storage and retrieval. So what happened to the buzz around NoSQL?
The author has been playing around with Neo4j unmanaged extensions recently and wanted to be able to check that it worked properly without having to deploy it to a real Neo4j server. CommunityServerBuilder allows users to do so.
Optimistic locking is very handy for preventing lost updates, and you can use it even if you've chosen to move away from RDBMS to MongoDB storage. In this article, you'll learn how.
How do you objectively measure the popularity of a DB engine? Good question! There's an Austrian company who claims to have the answer. Among the top 10 DB engines, MongoDB is the only non-RDBMS representative, and some other ratings are surprising as well.
MongoDB users interested in working with Node.js (or Node.js users looking at MongoDB) may find this tutorial to be particularly useful. It covers all the steps to connect Node.js to a MongoDB database on a VPS, and provides all the resources one might need to quickly catch up on either one.
Make sure you didn't miss anything with this list of the Best of the Week in the Mobile Zone. This week's best include a presentation on Marvel's use of graph theory and NoSQL for the Marvel Universe, an announcement about iterable collections in Redis, and first entry in a series on modeling data in Neo4j.
The main benefit of this conference for me was meeting most of the usual suspects from the London Java Community.
Your app is booming, you need more web servers, and you need to serve users and keep their user experience. When you had a single server you used session for that, but now how do you keep sessions across multiple web servers? Offloading web servers sessions to MongoDB looks like a great solution
Anyone can browse the database at MusicBrainz. With an account, you can contribute new data or fix existing record's details, track lengths, send in cover art scans of your favorite albums, etc. In this post (part 1) you will learn how to import the MusicBrainz data into Neo4j for some further analysis.
A recent blog post suggests that distributed database systems may not be as universally useful as many believe them to be. Is there too much hype surrounding distributed databases, or is there more to the story?
Today the author would like to introduce you to Spring Data Neo4j. To this end he implemented a little showcase application. The context of the showcase is a shop system in which it would be useful to calculate what other users also viewed – as known from many popular shopping e-commerce websites.
The overall idea behind this app is that Users can post their own Vine videos, to be voted on by the public to see who has the Funniest Vine videos globally. So without further hesitation, let's dive in and see how we can build a gamified application atop-of Couchbase using Ruby and Rails.
Through examples such as Hawkeye and his various identities, traits, and so on, Peter Olson describes a market in which story arcs and relationships between characters are not only valuable data, but a dataset so expansive that it requires emerging techniques to manage and analyze.
In this article - the first in the "Modelling Data in Neo4j for Beginners" series - we look at a common mistake made when modeling bidirectional relationships.
One graph database vendor decided to divide the graph database space into non-native (i.e. square) and native (i.e. diamond) graph databases. Obviously, non-native is boring, or slow, or simply bad, and native is exciting, or fast, or simply good. Problem is: There is no such thing as a native graph database.
One problem for online retailers is working out whether there is a suitable substitute product if an ordered item isn’t in stock. Since this problem brings together three types of data – order history, stock levels and products – it should be a nice fit for Neo4j, so the author ‘graphed up’ a quick example.
There are situations where your documents have many different fields and you want to be able to query efficiently on any of them. Say you have a document describing a person. Then you may want to find all people with blue eyes, or any other property. How can you use indexing to quickly resolve these queries?
You've probably read about the new features in Blackbirds Release 2.0. The big ticket items include geo-distribution, automation, and java stored procedures. In addition to these awesome new features, NuoDB slipped in support for ZFS, specifically Native ZFS on Linux.