MongoDB – State of the R

mongodbNaturally there are two reasons for why you need to access MongoDB from R:

  1. MongoDB is already used for whatever reason and you want to analyze the data stored therein
  2. You decide you want store your data in MongoDB instead of using native R technology like data.table or data.frame

In-memory data storage like data.table is very fast especially for numerical data, provided the data actually fits into your RAM – but even then MongoDB comes along with a bag of goodies making it a tempting choice for a number of use cases:

  • Flexible schema-less data structures
  • spatial and textual indexing
  • spatial queries
  • persistence of data
  • easily accessible from other languages and systems

In case you would like to learn more about MongoDB then I have good news for you – MongoDB Inc. provides a number of very well made online courses catering to various languages. An overview you may find here.

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Free and Certified MongoDB Online Courses

MongoDB_University_LogoIn case you are interested in learning about MongoDB or generally curious about non-relational approaches to storage of data then my recommendation for you is to check out the online courses offered by MongoDB Incorporation. I promise you won’t be disappointed. MongoDB Inc’s educational department – MongoDB University – offers five courses for developers and dev ops:

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How to Import a CSV into MongoDB using AWK

In case the desired JSON objects structure is just a set of simple attributes this can be achieved by using mongoimport directly. But in case some of the fields are supposed to be combined into an array or a sub-document, mongoimport won’t help you. In this tutorial I will show you how to transform a CSV into a collection of GeoJSON objects and in the course of that teach you the basics of AWK.

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