MapReduce lets you process very large amounts of data in a distributed fashion.
MapReduce was developed by Google in the mid-2000s. It is 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.
In this webinar, we will compare the two to see how they meet your needs.
• What happens when you need complex aggregations?
• What happens when you need aggregations that get increasingly complex over time as your data store expands?
Oren Eini, CEO of RavenDB, will take you on a tour of MapReduce aggregations at a large scale to determine which option delivers performance in producing sum totals, averages, and more in real time.
You will learn:
• How MapReduce works
• How you can do complex computation over time at high performance
• How MapReduce in RavenDB works
• Contrast RavenDB MapReduce with MongoDB MapReduce
• Contrast RavenDB MapReduce with other aggregation systems available in other databases