Python and PostgreSQL for Huge Data Warehouses

Explore techniques for managing and processing massive datasets using Python and PostgreSQL in this 57-minute EuroPython 2013 conference talk. Delve into the components of large-scale data warehousing, including connection pooling, data replication, and optimization strategies. Learn how to configure and utilize tools like Peach Bouncer for efficient data handling. Discover methods for moving and processing data samples, optimizing performance, and conducting benchmarks. Gain insights into executing business intelligence queries and retrieving data from various database sources. Understand the intricacies of working with in-memory data structures to enhance processing speed and efficiency in huge data warehouse environments.

Introduction
How big is a big database
What will we do
Components
Connection Pool
Peach Bouncer
Configuration
Data Moving
Data Replication
Processing Sample
Optimizing
Benchmark
Business Intelligence Queries
Getting Data from Other Databases
Data in Memory