Cassandra: Syllabus

Mastering Apache Cassandra: Scalable Data Warehousing with Python

Apache Cassandra is a highly scalable, distributed NoSQL database designed for handling large amounts of data with high availability and fault tolerance. This book provides a hands-on approach to implementing Cassandra for data warehousing, covering schema design, performance tuning, data ingestion, and real-world integrations with Python.

Module 1: Introduction to Apache Cassandra and NoSQL#

• Understanding NoSQL databases and Cassandra’s architecture
• Key advantages: High availability, scalability, and fault tolerance
• Setting up Apache Cassandra locally and on cloud platforms
• Understanding the CAP theorem and where Cassandra fits

Module 2: Cassandra Data Modeling#

• Understanding keyspaces, tables, partitions, and clustering keys
• Designing efficient schemas for data warehousing
• Best practices for avoiding anti-patterns
• Query-first approach to data modeling in Cassandra

Module 3: CRUD Operations and Querying with CQL#

• Working with Cassandra Query Language (CQL)
• Creating, inserting, updating, and deleting data
• Understanding primary keys, composite keys, and indexes
• Performing advanced queries using ALLOW FILTERING and secondary indexes

Module 4: Python Integration with Apache Cassandra#

• Connecting to Cassandra using Python and the cassandra-driver
• Executing CQL queries from Python scripts
• Handling large-scale data ingestion with Python
• Implementing batch processing and pagination

Module 5: Performance Optimization and Scaling#

• Optimizing data partitions and avoiding hotspots
• Understanding compaction strategies and garbage collection tuning
• Monitoring and benchmarking Cassandra performance
• Scaling horizontally: Adding and removing nodes dynamically

Module 6: High Availability and Disaster Recovery#

• Implementing replication strategies for fault tolerance
• Setting up multi-datacenter replication
• Backup and restore strategies in Cassandra
• Configuring consistency levels for read and write operations

Module 7: Advanced Data Warehousing Techniques#

• Implementing time-series data storage in Cassandra
• Handling large-scale ETL processes with Apache Spark and Cassandra
• Using materialized views and denormalization strategies
• Implementing CDC (Change Data Capture) for real-time updates

Module 8: Deploying Cassandra in Production#

• Deploying Cassandra clusters using Kubernetes and Docker
• Securing Cassandra: Authentication, encryption, and role-based access
• Automating monitoring and alerting with Prometheus and Grafana
• Best practices for maintaining a production-ready Cassandra cluster

Hands-On Projects

Project 1: Building a Scalable Data Warehouse with Cassandra#

• Designing a schema for a real-world use case
• Implementing efficient partitioning and indexing
• Writing optimized queries for analytical processing

Project 2: Real-Time Data Ingestion Pipeline with Python#

• Using Python to insert and retrieve data from Cassandra
• Handling batch inserts and streaming data ingestion
• Monitoring and optimizing write performance

Project 3: Implementing ETL Pipelines with Cassandra and Apache Spark#

• Extracting data from multiple sources and storing it in Cassandra
• Running Spark transformations for real-time analytics
• Writing transformed data back to Cassandra for querying

Project 4: High Availability Deployment and Load Balancing#

• Setting up a multi-node Cassandra cluster on Kubernetes
• Configuring replication and fault tolerance mechanisms
• Benchmarking performance under high loads

Project 5: Real-Time Analytics Dashboard with Cassandra#

• Connecting Cassandra to a BI tool for visualization
• Implementing materialized views for fast queries
• Securing data access with authenticati