In the evolving landscape of online media, news organizations are in need of more sophisticated and agile tools for both managing content and gathering data — from the articles to photos to contacts. Newsrooms are building solutions to more effectively archive the stories we tell and share them with the world. Many of the largest new organizations in the world use MongoDB to power their analytics, content management and archiving systems. In this panel on Data in Online Publishing, we'll hear from technical leads from the New York Times, Forbes, Business Insider and Buzzfeed on how they use MongoDB's unique features within their larger architecture. We'll go through migrations, schema design decisions, production build-outs and tips and tricks for using MongoDB

We will build an IRC server based on MongoDB. In this first session we will review how the server works and how to manage the necessary data. We will look in detail at how to build a Message Bus that is the backbone of our IRC server.

You have seen the stuff that FourSquare has done with spatial and you want some of that hotness for your app. But, where to start? Have no fear - by the end of this session you will have all the pieces necessary to write your own location based app. The OpenShift platform already has MongoDB plus the spatial bits installed, so there is no need to find a VPS or convince your IT dude to install stuff. What's OpenShift? It's Red Hat's free auto-scaling Platform as a Service. This session will start with a quick intro on firing up an OpenShift instance with MongoDB. Then we will load some data into MongoDB, show you how to handle spatial data, do some command line spatial operations, and finally plugin in some code to build a simple "Find the National Park Application". When you go home you will be able to amaze your friends and supervisors with some spatial magic goodness you can control.

MongoDB has been designed for versatility, but the techniques you might use to build, say, an analytics engine or a hierarchical data store might not be obvious. In this talk, we'll learn about MongoDB in practice by looking at hypothetical application designs (based on real-world designs, of course). Topics to be covered include schema design, indexing, transactions (gasp!), trees, what's fast, and what's not. Sprinkled with tips, tricks, shoots, ladders, and trap doors, you're guaranteed to learn something new in this interdisciplinary talk.

Getting insights out of MongoDB is easy if you know how to program. But what if you don't? In this talk, we introduce Analytica, a software product with an Excel front-end that makes it easy to report, analyze, and graph your data directly from MongoDB. We will show you how to build reports, create graphs, and deploy Analytica without impacting your production deployment.

MongoDB supports write-ahead journaling (by default) to facilitate fast crash recovery and consistency in database files after that crash. In this session, we'll give an overview of on-disk persistence with MongoDB, journaling, and discuss the internals of journaling and the storage engine.

Now that we've built our app, we will look at how to deploy it in production. We will design and deploy a replica set to support a highly available backend for the server. We'll also show you how to monitor your server, both from the shell and using the MongoDB Monitoring Service (MMS). We'll also try to break our cluster and show how the service stays running throughout failures as well as how to recover from catastrophic failures.

MongoDB supports replication for failover and redundancy. In this session we will introduce the basic concepts around replica sets, which provide automated failover and recovery of nodes. We'll show you how to set up, configure, and initiate a replica set, and methods for using replication to scale reads. We'll also discuss proper architecture for durability.

Sailthru is a behavioral communication and analytics platform focusing on email, onsite, and mobile personalization. Sailthru stores terabytes of data for hundreds of millions of end users and billions of messages, all in MongoDB. Since we presented last year, we've had 10x growth and experienced lots of challenges scaling on AWS. We've learned lots of lessons about building and designing for MongoDB at scale, and we'll share them.

Understanding the concerns and goals of running multiple datacenter in a hot-failover or hot-hot-hot configuration can be challenging. Come to this talk to learn the basics while finish up with a much better understanding how to deploy MongoDB across the world. We will work through deploying multi data center for read/write locality, increased availability and improved systems isolation, for disaster preparedness with recovery. In addition we'll look at the MongoDB roadmap and planned enhancements around data center awareness.

You have a need for MongoDB, and you’d like to run it in the cloud…but can it be done quickly? It’s much easier than you might think. In this presentation, Claudio Gentile from RightScale (Leaders in Cloud Management) will discuss real world use cases for social gaming applications using MongoDB, and will showcase how easy it is to deploy Mongo as part of a 3-tier cloud architecture in just minutes. The audience will learn:

How to deploy MongoDB alone, or as part of a multitier HA architecture in minutes A tour through the dashboard, available reporting, and show full server access What RightScalegives MongoDB users vs. AWS or other clouds alone Why MongoDB is a perfect fit for the cloud

In this session we’ll talk through a series of examples to distill some of our best operational practices. The format of this talk is an interactive and fun adventure through some real-world cases that come from real systems and large deployments. This session will touch on backups, network availability, performance pitfalls, indexing/schema-design, log management, monitoring and alerting along with some good examples of diagnostic techniques with a goal of finding good solutions.

We will extend the IRC server to log the full chat history. This requires a scalable backend to store an infinitely growing volume of messages. We'll look at several ways of designing the message storage and the limitations and tradeoffs of each. We will then deploy our updated IRC server and upgrade from a replica set to a sharded cluster without any downtime.

This is a follow up talk to Jake Porway's talk at MongoNYC 2011 where he presented Project Cascade as one of R&D Labs Projects backed by MongoDB. Since then Project Cascade has moved to a scalable production level system. The talk will cover challenges and lessons learnt in moving MongoDB from a research project to a production environment.

Adoption of MongoDB has accelerated tremendously among developers in the past 18 months, and many large enterprises have now deployed MongoDB in reliable and large scale production environments. However, for many developers, it remains a challenge to convince production teams and business stakeholders to adopt an open source technology that has not been certified yet by their IT teams. This session will provide you with the compelling arguments to reassure business and production teams such as:

• Public customer references and real-world case studies (migration, and adoption stories)
• Deployment support and practices for robustness
• How MongoDB contributes to your company’s business value

We will explore the various options for backups, go through the rich information available through the monitoring service (MMS), and observe the automated chunk split and automated chunk migration across shards. We will also provoke several failures in the cluster and see how to recover.

In this talk we will explain the elements of a successful MongoDB deployment and focus on the where, how and why they fit into the development process.

At foursquare we store almost all our live data within sharded mongodb clusters. We handle thousands of requests per second via our web site and api and each of those may generate several dozen mongo queries. This talk will address the practical details of our configuration, optimizations we've found useful, and the operational techniques we use to keep things running smoothly.

MongoDB supports a wide range of indexing options to enable fast querying of your data. In this talk we’ll cover how indexing works, the various indexing options, and cover use cases where each might be useful.

MongoDB is helping Next Big Sound change how the music industry uses big data. By marrying social media data with traditional sales data, Next Big Sound creates a central platform for all of the music industries needs with MongoDB working hard behind the scenes. This use case analysis will hit key points such as cluster architecture, document schema, some growing pains, and how Next Big Sound uses MongoDB to store rich event data as well as index hundreds of thousands of artists by their network distribution and geolocation.

A brief overview of building and deploying applications using MongoDB Replica Sets on Windows Azure followed by an interactive session to discuss recommended configurations and tips (and tricks)

We're working on a new aggregation framework for MongoDB that will introduce a new aggregation system that will make it a lot easier to do simple tasks like counting, averaging, and finding minima or maxima while grouping by keys in a collection. The new aggregation features are not a replacement for map-reduce, but will make it possible to do a number of things much more easily, without having to resort to the big hammer that is map-reduce. After introducing the syntax and usage patterns for the new aggregation system, we will give some demonstrations of aggregation using the new system.

One of the most awesome benefits of Mongo is that its schemaless. This means you can do some really interesting things in your models such as dynamically adding attributes to the model without requiring a migration. The simplest example is adding createdat or updatedat timestamps. We'll go over some cool examples and discuss best practices. We'll cover best practices to avoid downtime in production while migrating to a different data representation.

At 5ivePoints, we have a MongoDB database with 85+million US voters, every one of them geocoded. Our application allows supporters of political campaigns across the country to pull up nearby voters on their mobile phone to canvas. Moreover, campaigns can create custom filters using any combination of voter attributes, and we provide data-driven filters that update in near real-time based on campaign results. But with a collection of this size, each index is on the order of GBs, and un-indexed queries aren't feasible. I'll discuss how we are able to support flexible user queries - integrating both geo-spatial and other attributes - with only 4 indexes, minimizing page faults caused by pulling rarely-used indexes into memory.

Gain valuable insights on running MongoDB on Amazon EC2. EC2 provides a simple and flexible deployment model for your application and databases. However, you still need to manage, maintain, and monitor your system. So how do you do this with EC2? We'll explore these questions and more in this session.

Learn how to integrate MongoDB with Hadoop for large-scale distributed data processing. Using Hadoop's MapReduce and Streaming you will learn how to do analytics and ETL on large datasets with the ability to load and save data against MongoDB. With support for Hadoop streaming support goes beyond the native Java enabling map reduce to be run in languages like Python and Ruby.

GameChanger is a giant data pipeline where high throughput on dynamic content is the top priority. We route live sports data in and back out to content generation systems, phones, dynamic web viewers, mobile apps, and email, peaking out at 10s of 1000s of constantly changing data streams. Our play-by-play collection will soon pass 1 billion 1k-sized, sport-variable documents- it's not something you can just query. We've had to re-learn how to lay out data in the document world: you must validate and maintain data differently, change access patterns, and cater to the strengths of the platform.

Sharding allows you to distribute load across multiple servers and keep your data balanced across those servers. This session will review MongoDB’s sharding support, including an architectural overview, design principles, and automation. Also will be presented varied scenarios of handling large amount of data, along with their best sharding strategies.

Art.sy started as a prototype built on top of a relational database, only to be quickly replaced with MongoDB. In this talk we'll walk along a year-long timeline of using MongoDB as our project matured and the number of developers and users grew significantly. We'll focus on failures, success stories, design patterns and lessons learned.

Ok, so you’ve launched your development sandbox, love MongoDB, and are now thinking about how you want to handle your production environment. Learn all sorts of tips and tricks in this practical session on MongoDB operations by leading cloud database hosting provider MongoLab. We at MongoLab provide database hosting on EC2, Rackspace and Joyent for thousands of applications powered by MongoDB. In this session we will share with you some of the best practices we have developed, and help you avoid some of the pitfalls common with running production MongoDB deployments. This talk will cover the basics, such as VM selection, OS and disk configuration as well as more advanced topics such as clustering, VM migrations/upgrades, backup strategies and monitoring, with special emphasis on running MongoDB in the cloud. Don’t miss this informative session that will help you operate MongoDB like a pro!

The combination of node.js and mongodb has emerged as the framework of choice for people building real-time applications, and is on the path to becoming the replacement for the common LAMP stack. This talk will suggest some reasons why and discuss some of the observed patterns in these types of applications.

MongoDB presents many benefits and holds great promise but it’s ultimate success will be based on mass adoption. This requires a whole product approach where data management and business analytic professionals that are used to working with relational sources can just as easily work with MongoDB. In this talk, I'll will present examples illustrating how one graphical design tool can be used for both environments with no coding involved:

Moving data into and out of MongoDB Instant Analytics and Reporting on top of MongoDB Job Orchestration – workflow, scheduling and error detection for MongoDB processes

All this will be done using a graphical design tool, without writing a single line of code. No prior knowledge or experience of MongoDB is required to follow this session. Everything show will be open source or 'free to use' software.

10gen CEO & Co-Founder Dwight Merriman will look "under the hood" at concurrency internals in the upcoming version of MongoDB.

Using MongoDB at UnderArmour, in 9 months from concept to launch, we built an e-commerce platform that within weeks of launch was handling millions of dollars a day in sales; replacing a huge portion of a large MSSQL+J2EE stack, in what is considered a "home run" by the technical team, the content teams, and management. Here's how we did it using an innovative CMS we built for MongoDB.

This talk will introduce the Spring Data MongoDB project. Spring Data MongoDB provides a feature rich library for writing MongoDB applications in Java. It builds upon the Spring Framework and as such promotes a POJO programming model with a strong emphasis on productivity. The Spring MongoDB Project provides a rich object mapper, a MongoTemplate helper class to simplify common document access operations using query, criteria, and update DSLs, automatic repository interface implementations, QueryDSL integration, and cross-store persistence.

There are many ways to build dashboards and reports with detailed aggregations. In this talk we will outline one way of using pre-aggregating as data is collected in order to provide real-time views. You may be familiar with options which include post data collection processing, like map/reduce or the new aggregation framework; we will investigate some of the downsides which we will need to be addressed when defining the use case for counters and pre-aggregation with MongoDB.

Come learn how to build these types of reports and see how easy it can be.

The Data Aggregation system is a next generation of data discovery service for CMS experiment at CERN LHC. Its primary goal is to help physicists to find desired information across distributed CMS data-services. The MongoDB was chosen as a primary back-end to carry this task. We will provide system overview and our experience with MognoDB in production environment.