The NoSQL Matters conference took place in Barcelona on November 30th, and now you can find slides from the presenters all collected in one place. The presentations collected cover a wide range of topics: Redis, MongoDB, DynamoDB, Riak, Neo4j, and more, including topics discussing NoSQL as a whole.
The author wanted to introduce the concept of a season into his graph so that he can have import matches for multiple years and then vary the time period which queries take into account. In this article, you'll find out how he did it.
At its core, Redis is an in-memory key-value datastore. Its simple data structures and intuitive API make Redis a true power-horse for solving various ‘Big Data’-related problems. To illustrate this point, I reimplemented my MongoDB-based molecular similarity search through Redis and its integrated Lua support.
This article, over a year old but reinvigorated by the recent "never use MongoDB" incident and its aftermath, takes an interesting look at last year's wave of MongoDB criticism by tracking the various arguments that had been made and responding to them individually.
Eventually consistent data is a buzzword nowadays, especially in NoSQL discussions. For those not versed in tech talk, having eventually consistent data means you’re willing to sacrifice data consistency in order to gain in other areas. But most projects don't need eventually consistent data from the beginning.
Anybody looking for an alternative NoSQL solution might be interested in ArangoDB, a not-quite-new but lesser-known NoSQL database that supports key-value documents, property graphs, and works with a query language called AQL based on the syntax of XQuery, among other things.
Neo4j is a powerful graph database that can be used for analytics, recommendation engines, social graphs and many more applications. In the following example we demonstrate in a few steps how you can load Neo4j from your legacy relations SQL source.
One of the first things the author needed to learn when he started using Neo4j was how to model his domain using nodes and relationships, and it wasn’t initially obvious to him what things should be nodes. In this article, you'll find some tips and tricks that helped him get started.
Last week, the author attended a talk at the Washington DC MongoDB User Group given by John Schulz, a chief architect at AOL. In the talk, he describes his experiments comparing TokuMX with MongoDB for his use case. The experiments show TokuMX favorably, but what the author found interesting was why.
Previously, the author talked about the Jepsen tester and about the various improvements made to it. In this post, the author will walk through a Jepsen run made with the code from their Github fork, explain the test setup, and then go through the output and explain the behavior that Jepsen is producing in NuoDB.
Document databases do a good job at storing a single representation of an aggregate entity, but struggle to handle use-cases that require multiple, different views of the domain. Fortunately, there is a data model that embraces rich connections between your domain entities: graph databases.
You may know that Kyle Kingsbury of Jepsen fame turned the baleful eye of his test framework against NuoDB 1.2. Unfortunately, the Jepsen tests ran into node instability issues while testing was underway. In this article, you'll find a new test of network failure using NuoDB and Jepsen.
When approaching a technology like Neo4J, if you’re as avid of a Twitter user as the author is, then you already have the best data set for to learn with: your own social network. This post will help you set up Neo4J and a Twitter app (for the Twitter API), and work with data from your social network and others.
Titan is a distributed graph database capable of supporting graphs on the order of 100 billion edges and sustaining on the order of 1 billion transactions a day. In this article, you'll learn more about graph representations, graph traversals, and performance in Titan 0.4.1+.
Make sure you didn't miss anything with this list of the Best of the Week in the NoSQL Zone. This week's best include more details on DB-Engines list of the top 10 most popular DB engines, Vlad Mihalcea on why he never blames open source projects, notes on the release of Redis 2.8.0, and more.
Christian Kvalheim from MongoDB gives some good advice based on past MongoDB use cases he's seen that have caused problems for the developers.
An Atlassian article takes an unorthodox (but very effective!) approach to introducing how Apache Cassandra works. A comic! It features a bunch of little nodes talking to each other sort of like normal people.
Sometimes it is more convenient to use a Ruby script than deal with the MongoDB JSON shell interface. This is a superquick tutorial to running Mongo MapReduce aggregations from Ruby.
Learn about these two technologies and how they can be used to help solve problems with the performance and scalability of your application.
This set of slides discusses how to migrate Magento - a popular open-source eCommerce platform that works with mySQL by default - to MongoDB.
You’ve probably been advised more than once to use longer passwords, perhaps required to include numbers and exotic characters — are “words” still okay? But even when such measures are used, passwords still fall victim to attacks. This is where multi-factor authentication comes in.
One of the breaking changes in Neo4j 2.0.0-RC1 compared to previous versions is that the -[?]-> syntax for matching optional relationships has been retired and replaced with the OPTIONAL MATCH construct. In this article, you'll find out how to work with OPTIONAL MATCH.
In the simplest terms, MarkLogic is a single product that combines features of a highly distributed NoSQL database, a search engine, all with application services layered over the top. In this article, you'll learn when and why one might use MarkLogic.