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1
Intro
2
MongoDB
3
MongoDB aggregation framework
4
mongoDB pipeline
5
pymongo
6
demo structure
7
Taylor Swift
8
Build the first pipeline
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unwind operator
10
stages
11
grouping
12
dates
13
Match operator
14
Competition
15
unwind
16
arm
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map operator
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MapReduce
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Best practices
20
Useful resources
Description:
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.

Data Analysis and Map-Reduce with MongoDB and PyMongo

EuroPython Conference
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