Data Analysis and Map-Reduce with MongoDB and PyMongo

Explore data analysis techniques using MongoDB and pymongo in this EuroPython conference talk. Learn how to leverage the MongoDB aggregation framework for calculating aggregated values without relying on complex map-reduce operations. Discover the power of data-aggregation-pipelines for performing averages, summations, groupings, and data reshaping. Gain insights into working with documents, sub-documents, and grouping data by various time intervals. Through live examples, master the art of extracting valuable insights from your data using pymongo with minimal code. Access the accompanying iPython notebook and sample data for hands-on practice. Dive into topics such as MongoDB pipelines, unwind operators, stages, grouping, date operations, match operators, and best practices for efficient data analysis.

Intro
MongoDB
MongoDB aggregation framework
mongoDB pipeline
pymongo
demo structure
Taylor Swift
Build the first pipeline
unwind operator
stages
grouping
dates
Match operator
Competition
unwind
arm
map operator
MapReduce
Best practices
Useful resources