Python and PostgreSQL for Huge Data Warehouses

Overview

Explore techniques for managing massive data warehouses using Python and PostgreSQL, covering connection pooling, data replication, optimization, and efficient querying strategies.

Syllabus

• Introduction to Python and PostgreSQL
Overview of Python for Data Management
Overview of PostgreSQL in Data Warehousing
• Setting Up the Environment
Installing and Configuring PostgreSQL
Setting Up Python Development Environment
Connecting Python to PostgreSQL
• Connection Pooling in PostgreSQL
Introduction to Connection Pooling
Using Psycopg2 for Connection Pooling
Best Practices for Managing Connections
• Data Replication Strategies
Understanding Postgres Replication
Configuring Streaming Replication
Logical Replication and Use Cases
• Data Warehousing Optimization
Identifying and Designing Data Models
Indexing Strategies for Large Datasets
Partitioning Strategies
Vacuuming and Analyzing Databases
• Efficient Querying Strategies
Query Planning and Execution
Writing Optimized SQL Queries
Using Explain and Analyze for Performance Tuning
CTEs and Window Functions
• Advanced Python Techniques for Data Management
Using Pandas and NumPy with PostgreSQL
ETL Pipelines using Python
Integrating PostGIS for Geospatial Data
• Security and Maintenance
Implementing Database Security Practices
Regular Database Maintenance and Backups
• Capstone Project
Designing a Scalable Data Warehouse
Implementing Optimization Techniques
Presenting Findings and Solutions

Subjects

Conference Talks