Foursquare

3 results

Top Big Data skills? MongoDB and Hadoop

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 with SiliconAngle 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. Tagged with: MongoDB, Hadoop, Big Data, open source, operational database, Foursquare, IT jobs, jobs

January 8, 2013

MongoDB Case Study: foursquare

Foursquare is a location-based social network which has grown rapidly since its inception in 2009, requiring efficient ways to scale with limited engineering resources. As its user profile and activity stream data increased, foursquare made the strategic decision to migrate storage of venues and check-ins to MongoDB as a long-term scalable solution to the company's continued expansion. Originally, the foursquare application relied on a single relational database. As the company experienced rapid growth, they split the data to two nodes: one for check-ins (the biggest data set) and one for everything else. However, it was clear that in time, check-ins alone would increase beyond what a single machine could handle. MongoDB not only solved the initial problem, but also provided the tools for agile development. Foursquare can now take advantage of MongoDB's built-in auto-sharding. MongoDB’s auto-sharding partitions the database, allowing foursquare to scale writes and spin up new nodes as their application grows. Instead of writing its own sharding layer, foursquare can rely on MongoDB’s automated scaling infrastructure, enabling engineers to focus on building their application. MongoDB has allowed foursquare it to dramatically simplify its data model. For instance, rather than storing tags (“has wifi”, “great for dates”, “hotspot”, etc) in a separate table, in MongoDB tags are embedded directly into the document representing a venue. This is both more efficient at run-time and easier for engineers to understand and manipulate. As Harry Heymann, Lead Server Engineer at foursquare, explained, ...MongoDB is a practical database for practical problems that engineers in the real world have…it's only going to continue to evolve into a database that just makes our jobs easier as application developers, which is fantastic.” To learn more, visit the case study or hear directly from Heymann about how MongoDB has made a difference at foursquare. Tagged with: mongodb, foursquare, agile development, big data, nosql, 10gen

January 6, 2012