MapReduce lets you process very large amounts of data in a distributed fashion.
It was first developed by Google in the mid-2000s as a method of aggregating data by processing increments of your data at distributed points then sending the results from each point onward to be updated again.
An election is a MapReduce problem.
Each voting station sums up the local results and sends it to the regional location where they send all the results in the region to the head office where they sum up the total tally.
MongoDB uses MapReduce to handle aggregations.
RavenDB uses MapReduce in a slightly different way: To allow you to handle updates to aggregation over time.
Let’s get ready to rumble! In this webinar, we will compare the two databases to see:
What happens when you need complex aggregations?
What happens when you need aggregations that get increasingly complex over time as your data store expands?
Database Developer and Microsoft MVP Oren Eini will take you on a tour of MapReduce aggregations at a large scale to determine which option delivers performance in real-time for producing sum totals, averages, and more.
You will learn:
- How MapReduce works
- How you can do complex computations over time at high performance
- The difference between RavenDB MapReduce and MongoDB MapReduce