Every week here and in our newsletter, we feature a new developer/blogger from the DZone community to catch up and find out what he or she is working on now and what's coming next. This week we're talking to Michael Hunger, Neo4j Developer Advocate and author of our upcoming Neo4j Refcard.
TL;DR: As the amount of unstructured data being collected by organizations skyrockets, their existing databases come up short: they're too slow, too inflexible, and too expensive.
With Couchbase Server 3.0, you get a great new option to change the way we use memory for caching your keys and metadata. The new option is called "full ejection." Here is how full ejection is different.
This year I’ve been giving an evolving live demonstration of coding a Fully Buzz Word Compliant, mobile-friendly web application. The aim of the demo is to show, via a real-world application rather than a toy example, where these popular technologies sit in your architecture, and how they interact with each other.
You might have data as CSV files to create nodes and relationships from in your Neo4j Graph Database. It might be a lot of data, like many tens of million lines. Too much for LOAD CSV to handle transactionally.
In this final post of the series we will cover a subtle, but important distinction in terms of balancing a sharded cluster as well as an interesting limitation that can be worked around relatively easily, but is nonetheless surprising when it comes up.
Make sure you didn't miss anything with this list of the Best of the Week in the NoSQL Zone (October 17 - October 24). This week's best include MongoDB incremental migration scripts, how to connect to MongoDB form a Java EE stateless application, solutions for Redis running slowly, and more.
TL; DR: Google Analytics stores a massive amount of statistical data from web sites across the globe. Retrieving reports quickly from such a large amount of data requires Google to use a custom solution that is easily scalable whenever more data needs to be stored.
Over the last 6 months my colleagues and I have been running hands on Neo4j based sessions every few weeks and I was recently asked if I could write up the lessons we’ve learned. So in no particular order, here are some of the things that we’ve learnt.
We consistently hear that getting started with MongoDB is easy, but scaling to large configurations that include replication and sharding can be challenging. With MMS, it is now much easier.
Shindig is a mobile app (iOS, Android) that helps you explore new drinks and share them with the world. Take a picture of what you’re drinking, tag it with taste tags, share it, earn rewards and gameification points, follow famous mixologists and drink aficionados and search for the best drinks nearby.
Many enterprises are turning to us to help add a cache to an existing application or evolve applications to next generation technologies. For these level two cache implementations we’ve helped develop a data access layer for applications in the Spring project.
In this post we will go through some recommendations when running a sharded cluster at scale. Scalability is one of the core benefits of sharding in MongoDB but this can give you a false sense of security; even with that flexibility, you still have to make smart decisions about how and when you deploy resources.
An incremental software development process requires an incremental database migration strategy.
We’ve started running some sessions on graph modelling in London and during the first session it was pointed out that the process I’d described was very similar to that when modelling for a relational database.
In this post I will present how to connect to MongoDB from a stateless Java EE application, to take advantage of the built-in pool of connections to the database offered by the MongoDB Java Driver. This might be the case if you develop a REST API, that executes operations against a MongoDB.
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 part 1 of a series on MongoDB sharding pitfalls, the release of Redis Cluster as a minimum viable product, a new source for MEAN stack resources, and more.
Redis is blazing fast and can easily handle hundreds of thousands to millions of operations per second (of course, YMMV depending on your setup), but there are cases in which you may feel that it is underperforming.
An approach to modeling that I often see while working with Neo4j users is creating very generic relationships (e.g. HAS, CONTAINS, IS) and filtering on a relationship property or on a property/label at the end node.
The MongoDB Aggregation pipeline is a framework for data aggregation. Documents enter a multi-stage pipeline that transforms the documents into an aggregated results. It was introduced in MongoDB 2.2 to do aggregation operations without needing to use map-reduce.
One of the important roles operations has is going to an existing server and checking if everything is fine. This is routine maintenance stuff. It can be things like checking if we have enough disk space for our expected growth, or if we don’t have too many indexes.
Recently, I had the pleasure of doing a talk at the Brussels Data Science meetup. Some really cool people there, with interesting things to say. My talk was about how graph databases like Neo4j can contribute to HR Analytics. Here are the slides of the talk.
Unit testing requires isolating individual components from their dependencies. Dependencies are replaced with mocks, which simulate certain use cases.
This week, DZone released its latest Refcard. If you're interested in learning more about MongoDB or sharpening your skills, we decided to dig into the DZone archives and find some of the most popular posts we've had on the topic.
Many clients don’t quite realize how much powerful ad-hoc query capability they’re losing by leaving SQL. But how can we possibly have the best of both worlds? Well, luckily for us, Postgres is working on a very handy solution.