How The National Archives migrated its data store from SQL Server to Mongo DB and built Service-Oriented Platform to make available over 11m of historical records. http://discovery.nationalarchives.gov.uk

This talk will introduce the features of MongoDB by walking through how one can building a simple location-based checkin application using MongoDB. The talk will cover the basics of MongoDB's document model, query language, map-reduce framework and deployment architecture.

One of the challenges that comes with moving to MongoDB is figuring how to best model your data. While most developers have internalized the rules of thumb for designing schemas for RDBMSs, these rules don't always apply to MongoDB. The simple fact that documents can represent rich, schema-free data structures means that we have a lot of viable alternatives to the standard, normalized, relational model. Not only that, MongoDB has several unique features, such as atomic updates and indexed array keys, that greatly influence the kinds of schemas that make sense.

MongoDB and the Java programming language boast a deep affinity; but the places where MongoDB is proving strongest are on Java’s Virtual Machine (JVM) itself rather than the Java language. This talk explores the deeper relationship between the MongoDB database and various languages on the Java Virtual Machine such as Java, Scala, Clojure, JRuby and Python as well as the challenges posted getting MongoDB to play nice with these tools and their syntax. Also examined will be frameworks and integration points popular between MongoDB and the JVM such as Spring Data, Morphia and Lift’s MongoDB-Record component.

You've probably read all the blog posts about how MongoDB is awesome for real time analytics -- but is it? And how do you really do it? I've learnt how to do it the hard way, by making most of the mistakes and trying a few things that turned out to be not so great. You shouldn't have to make those mistakes too. In this talk I'll tell the story of how I built a system that processes tens of millions of ads per day and all the things I did wrong before I got it to work. The things you can't do with MongoDB once you scale up beyond a few hundred operations per second, signs of trouble to look out for and how the nice guys at 10gen can be really helpful when you're stuck.

B-Trees, Sparse and Covered indexes and how MongoDB uses them still confusing you? In this talk we will dive into the different indexes that are available, how query plans are executed and the internals of index evaluation including understanding of the explain output.

Telefonica is an integrated telco operator with services across voice, internet and IPTV, and presence in 25 countries. In order to exploit this data, Telefonica R&D has multiple projects that either are heavily data intensive or have not a clear data model solutions. As part of the R&D challenges, our focus is to provide a state of the art solution for these problems, such as data filtering and merging, real time analysis, distribution and processing of data. We currently use MongoDB as our data container due to speed, integration and scalable concurrent access that we need to address. In our project, MongoDB is the storage solution that interacts with several distinct technologies and solutions of our product. As part of this presentation, we will briefly talk about the migration between our current solution using Oracle towards a batch optimized processing, centralisation / orchestration architecture supported by MongoDB, including as well user identity matching using MongoDB MapReduce and real time geolocation index supported by MongoDB. As part of this integration with other innovative technologies, we will also present our work regarding "full" social network analysis data storage of processed data from Hadoop and Neo4j in order to establish a (spacial) proximity graph among users, using MongoDB and integrating it with Django for a ReST API.

The Guardian started developing with MongoDB more than a year ago and has been in production for the past 5 months. In this talk I'll discuss the good and bad choices we made in our implementation and how the various features of Mongo have turned out for us in practice.

With the release of 1.8, MongoDB supports write-ahead journaling of operations to facilitate fast crash recovery and durability in the storage engine. In this session, we'll give an overview of durability with MongoDB, demo journaling, and discuss journaling internals.

Schema design is a critical step in making sure an application scales well. There are considerations for reads and writes, both with and without sharding. We'll go through a few use cases and examine how difference schemas impact performance.

Introduction into SAP’s Java Platform as a ServiceIntroduction into the CMIS protocolOverview Document Service on JPaaS based on MongoDBDemo

That's right: you too can learn to read the omens and ensure that your MongoDB deployment stays in tip-top shape. We'll look at memory usage, file sizes, flushing, journaling, and all the special incantations that reveal MongoDB's true inner self. By the end of the talk, you'll have ten concrete steps you can take to address performance degradation before it happens. You'll also get a few tips on application design and pointers on remote monitoring.

Steve Francia, former VP of Engineering at OpenSky, a NYC based social commerce company, will present on how OpenSky augments using RDBMS with MongoDB to develop the next ecommerce platform. OpenSky utilizes both traditional SQL solutions and combines them with NoSQL to overcome the limitations of each, increase development speed and scale quickly.

We are one of the top social media monitoring platforms today and the volume of data is one of our biggest challenges on a daily basis. Let’s say we processes a large volume of data every minute. But making sense of all the social data available on Twitter, Facebook or blogs is a different challenge all-together. So we introduced filters. In real-time. We needed an intelligent way to index this stream, so that we’re able to perform fast searches over it using multiple filters, like language, location, time, etc. This session is about how we managed to do just that using MongoDB on AWS.

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.

Julian Brown of Equal Experts looks at the journey a major European mobile provider took, from its relational roots, through the understanding and selling of the benefits of NOSQL alternatives, to the selection and development of a strategic system in MongoDB.

How do I use MongoDB and Spring? How do deploy my MongoDB application into a PaaS? Cloud Foundry is a PaaS that supports many languages and frameworks as well as many services and data stores, one of which is MongoDB. Chris will talk about a new pattern enabled by this architecturewhere you can write different pieces of your application in multiple different languages or frameworks. This talk introduces the usage of Spring Data and the ability to dynamically bind your application to a MongoDB service running in a PaaS.

With MongoDB, you can distribute load across multiple servers using auto-sharding. This session will introduce MongoDB's auto-sharding concepts and how to use them. We'll discuss choosing a shard key, basic architecture concepts, and common usage patterns. We'll close out the session with a few example use cases, including real world sharded deployments.

Many organizations today, due to regulatory compliance or other needs, are finding it necessary to archive large volumes of data into long-term storage. Regulations in many industries (eg HIPAA, SOX, Basel 3, FATCA, LEI etc) are driving the need for data retention and for query processing across archives and operational data. Learn how MongoDB provides a flexible, efficient, scalable, long-term document storage that can adapt to your organization's changing needs over time. A case study from US federal government agency with 130 legacy applications that needed to be archived and integrated into a federated view of archive and real-time operational data. Case study will cover how MongoDB provided richer functionality compared to leading commercial RDBMS options for storage and query processing.

An overall introduction to GeoSpatial querying support in MongoDB, along with a tour of some of the new features introduced in 1.9.