Location: New York, NY
10gen
Location: New York, NY
In this talk, we’ll hear from Sam Helman, Software Engineer at MongoDB (formerly 10gen), on how MongoDB is integrating Go into their new and existing cloud tools. Some of the tools leveraging Go include the backup capabilities in MongoDB Management Service and a continuous integration tool. They see using Go as an opportunity to experiment with new technologies and create a better product for end users. This talk was recorded at the MongoDB User Group meetup at MongoDB.
MongoDB found using the Go language to be extremely satisfying. Between the lightweight syntax, the first-class concurrency and the well documented, idiomatic libraries such as mgo, Go is a great choice for writing anything from small scripts to large distributed applications. In this talk, Sam will go through how the team has integrated Go and why Go and MongoDB are a great match for cloud services.
MongoNYC 2013 is coming in 2 days!
http://www.10gen.com/events/mongonyc-2013
MongoNYC brings together developers, IT professionals and executive decision makers across the MongoDB community for a one-day conference dedicated to the leading NoSQL database. At MongoNYC, you learn development and operations best practices, discover how other businesses are benefiting from MongoDB and network with MongoDB users and ecosystem partners.
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Apr 122013
(Contributor article by 10gen, originally appeared on 10gen Blog)
According to new research from the UK’s Sector Skills Council for Business and Information Technology, the organization responsible for managing IT standards and qualifications, Big Data is a big deal in the UK, and MongoDB is one of the top Big Data skills in demand. This meshes withSiliconAngle Wikibon research I highlighted earlier, detailing Hadoop and MongoDB as the top-two Big Data technologies.
It also jibes with JasperSoft data that shows MongoDB as one of its top Big Data connectors:
MongoDB is a fantastic operational data store. As soon as one remembers that Big Data is a question of both storage and processing, it makes sense that the top operational data store would be MongoDB, given its flexibility and scalability. Foursquare is a great example of a customer using MongoDB in this way.
On the data processing side, a growing number of enterprises use MongoDB both to store and process log data, among other data analytics workloads. Some use MongoDB with its built-in MapReduce functionality, while others choose to use the Hadoop connector or MongoDB’s Aggregation Framework to avoid MapReduce.
Whatever the method or use case, the great thing about Big Data technologies like MongoDB and Hadoop is that they’re open source, so the barriers to download, learn, and adopt them are negligible. Given the huge demand for Big Data skills, both in the UK and globally, according to data from Dice and Indeed.com, it’s time to download MongoDB and get started on your next Big Data project.
More on big data here
More from 10gen here
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Apr 042013
(Contributor article by 10gen, originally appeared on 10gen Blog.)
So much is written about Big Data that we tend to overlook a simple fact: most data isn’t big at all. As Bruno Aziza writes in Forbes, “it isn’t so” that “you have to be Big to be in the Big Data game,” echoing a similar sentiment from ReadWrite’s Brian Proffitt. Large enterprise adoption of Big Data technologies may steal the headlines, but it’s the “middle class” of enterprise data where the vast majority of data, and money, is.
There’s a lot of talk about zettabytes and petabytes of data, but as EMA Research highlights in a new study, “Big Data’s sweet spot starts at 110GB and the most common customer data situation is between 10 to 30TB.”
Small? Not exactly But Big? No, not really.
Continue reading »
(Contributor article by 10gen, author . originally appeared on MongoDB.org)
This is part 3 in a series, which will focus on the data modeling aspect of working with document databases. The previous parts are also available for reading: Part 1: Getting Started, and Part 2: Queries and Indexes.
The Usual Suspects
Although there are plenty of existing articles, presentations and webcasts about modeling your data to take advantage of a document database, this post is taking a slightly PHP-centric position as a part of this series. While this information is useful to anyone though, regardless of their chosen programming language, this talk specifically focuses on MongoDB with PHP.
We’re going to use two different scenarios to look at data modeling in the document world, chosen as common examples to illustrate differences in implementation between relational and document databases:
- Blog. Your garden variety of data, covering posts, comments and tags
- Private Sale / E-Commerce. Taking a look at needs for orders, users and products
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Scenario 1: Getting All Bloggy
I’m kicking off with the tried-but-true blog example as it is a common frame of reference. Part of the biggest challenges of assessing MongoDB is the ability to quickly understand document data modeling, and stepping outside the constraints of relational data modeling. A common scenario helps illustrate that.
Continue reading »
Mar 212013
(Contributor article by 10gen, author . originally appeared on MongoDB.org.)
This is part 2 of a series, with part 1 covering the bare essentials to get you going. In this post we are going to take a closer look at queries and how indexes work in MongoDB.
Introduction
I’d like to kick off this post with a thanks to the folks behind the PHP extension for MongoDB, who have done a fantastic job of matching the functionality of the Mongo shell client. This is important when you start to see how similarly the two function, and you might find that you can tweak your logic using the shell and quickly implement the same logic from within PHP.
Mar 152013
( Contributor article by 10gen, author . originally appeared on MongoDB.org.)
We have covered a lot on the blog about MongoDB features, as well as many ways to utilize MongoDB from different languages. This is the first in a series of posts from the perspective of a PHP developer; and covers the gamut from getting started to advanced concepts.
I’m not going to waste the first blog post getting you up and running with MongoDB and the PHP extension, as that whole process is documented quite beautifully:
While we’re at it, you should also take advantage of the online documentation for MongoDB, as well as the reference for the MongoDB extension for PHP.
Mar 072013
(Original Article by Ross Lawley, initially appeared on the MongoDB blog)
Recently, I attended both Pycon UK and to talk about the lessons I have learnt while maintaining mongoengine. The conferences were both excellent and surprisingly different. Pycon UK had quite an “unconference” feel, with some exciting sprint rooms – I wish I had more time as by all reports the educational jam was inspirational. Pycon Ireland in contrast felt more slick with booths from DemonWare, Amazon and Facebook. If you can, I’d advise going to both conferences as they really complement each other. Continue reading »
(Original post with audio and slides is here)
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Greg Brockman: So, I’m Greg Brockman. I work at Stripe. So, Stripe is an online payment processor to make it really easy to add payments to your website. So, if you haven’t heard of us before, you should definitely go check us out and use us for your data processing needs.
So, the thing I’m going to be talking about today is Monster, which is our event processing system that we’ve been using for a little over a year now. So, I wrote the first generation Monster, and over time, a lot of different people at Stripe have contributed to it. And the reason that I’m here is that we use MongoDB pretty heavily in order to make Monster work. There are a lot of reasons that we chose MongoDB and stuck with MongoDB over the past year, and I’ll get into that.
Today we have a talk from a recent SF MongoDB user group meetup by MongoDB expert Greg Brockman. Greg has been leading development at Stripe where they use MongoDB to power their event processing system.
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