MongoDB: Case Study with KPMG

  • SQL database
  • NoSQL(Not Only SQL) database

SQL Databases

Growth of NoSQL

MongoDB?

  • Rich Object Model: MongoDB supports a rich and expressive object model. Objects can have properties and objects can be nested in one another (for multiple levels). This model is very “object-oriented” and can easily represent any object structure in your domain. You can also index the property of any object at any level of the hierarchy — this is brilliantly powerful!
  • Secondary Indexes: Indexes speed up the queries significantly, but they also slow down writes. Secondary indexes are a first-class construct in MongoDB. This makes it easy to index any property of an object stored in MongoDB even if it is nested. This makes it really easy to query from the database based on these secondary indexes.
  • Replication and high availability: MongoDB supports a “single master” model. This means you have a master node and a number of slave nodes. In case the master goes down, one of the slaves is elected as master. This process happens automatically but it usually takes time, before the 3.2 release, 10–40 seconds were taken but after the release of MongoDB 3.2 and later, failures are detected faster and a new leader elected in under 2–10 seconds.

The KPMG story

Data Lake

Metadata Management

Logging Layer

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