Data Analysis on House Pricing Data Using MongoDB Aggregation Pipelines

Learn how to perform data analysis on house pricing data using MongoDB aggregation pipelines in Python. Create a free Atlas cluster and explore different aggregation techniques including match, group, and sort operations. Discover how to create JSON files and utilize pymongoarrow for efficient data handling. Follow along with code examples and gain practical insights into leveraging MongoDB's powerful data analysis capabilities for real estate market analysis.

- Introduction
- Agenda
- Sponsor Message
- Why aggregation pipelines are used?
- Starting with code
- Different ways to perform aggregation in MongoDB
- Creating a new database
- Operations in the aggregation pipeline
- Stages in the aggregation pipeline
- match operation
- group operation
- sort operation
- Creating JSON file
- pymongoarrow
- Final Note