Big Data

Big Data refers to technologies and initiatives that involve data that is too diverse, fast-changing or massive for conventional technologies, skills and infrastructure to address efficiently. These include a wide variety of apps such as genomics, clickstream analysis, customer Sentiment analysis, log data collection and ad technology.

Business Outcome

Leading organizations are leveraging Big Data to:

  • Build new applications that were not possible before, like the City of Chicago’s WindyGrid initiative;
  • Understand customer sentiment, like’s social media marketing platform.
  • Reduce costs, like a Tier 1 bank saving $40M over 5 years with MongoDB; and
  • Make the world a better place, like the Broad Institute’s open-source genomics platform.

Customer Examples


  • Agility. Today the marketing department wants to add Pinterest boards. Next quarter it may be a different social network. Changing the database with your application is difficult in a relational world.
  • Volume. The Broad Institute started with 400 data points. In just 24 months that jumped to 1.4M. The database must support rapid growth without downtime.
  • Velocity. Criteo’s online ad platform processes 1B requests and 350M updates per day. Can your database support that rate velocity?
  • Variety. The City of Chicago collects data from over 30 different departments, each in a distinct format. Doing that in a single data store is hard.

Why MongoDB

  • Dynamic schemas in MongoDB provide a simple way to evolve the database with your application.
  • Documents in MongoDB lend themselves to combining structured and unstructured data in a single data store.
  • Native analytics make it easy to deliver actionable insights in real-time.
  • Horizontal Scaling allows organizations to support massive data volumes and high ingestion rates.

Featured Resources