Understanding NoSQL Databases: Introduction to NoSQL databases and how they differ from traditional relational databases. Key concepts, types, and when to use NoSQL.
History and Evolution of Cassandra: The origin of Cassandra at Facebook, its evolution as an open-source project, and its current status in the industry.
Cassandra Use Cases: Understanding when and why Cassandra is the preferred choice, including its application in areas like real-time big data analytics, IoT, and content management.
2. Cassandra Architecture and Concepts
Distributed Architecture: Learn about Cassandra’s decentralized and distributed architecture, including its ring topology, partitioning, and replication strategies.
Data Consistency and Availability: Detailed understanding of the CAP theorem, eventual consistency, and how Cassandra manages data availability and partition tolerance.
Nodes, Clusters, and Data Centers: Configuration and management of nodes, clusters, and data centers within Cassandra, along with understanding data distribution strategies.
3. Data Modeling in Cassandra
Cassandra Data Model: Deep dive into Cassandra’s data model, including keyspaces, tables, rows, and columns. Understanding the differences between relational and Cassandra data modeling.
Primary Keys and Partitioning: Importance of primary keys in data distribution and query optimization. Understanding partition keys, clustering columns, and how they affect data storage.
Designing Efficient Data Models: Best practices for designing data models that support efficient reads and writes, including handling denormalization and data duplication.
4. Cassandra Query Language (CQL)
Introduction to CQL: Learn the basics of Cassandra Query Language, similar to SQL but tailored for Cassandra’s architecture.
Working with CQL: Creating and managing keyspaces, tables, and indexes using CQL. Performing CRUD operations (Create, Read, Update, Delete) on data.
Advanced CQL Features: Using collections, User Defined Types (UDTs), counters, and batch operations in CQL. Handling complex queries and pagination.
5. Performance Tuning and Optimization
Optimizing Read and Write Operations: Techniques for optimizing read and write performance in Cassandra, including compaction strategies and tuning consistency levels.
Indexing and Caching: Implementing secondary indexes and using caching strategies to improve query performance.
Monitoring and Troubleshooting: Tools and techniques for monitoring Cassandra clusters, identifying performance bottlenecks, and troubleshooting common issues.
6. Cassandra Operations and Administration
Cluster Management: Setting up and configuring Cassandra clusters, including adding/removing nodes, scaling, and handling replication.
Backup and Recovery: Strategies for backing up data and performing disaster recovery in Cassandra, including snapshot management and incremental backups.
Security Best Practices: Implementing authentication, authorization, encryption, and other security measures in Cassandra.
7. Integration with Other Technologies
Cassandra with Apache Spark: Integrating Cassandra with Apache Spark for real-time analytics and processing.
Using Cassandra with Hadoop: Leveraging Cassandra alongside Hadoop for big data processing.
Cassandra in Cloud Environments: Deploying and managing Cassandra on cloud platforms such as AWS, Azure, and Google Cloud.
8. Advanced Topics
Time Series Data Modeling: Special considerations for modeling and querying time series data in Cassandra.
Multi-Datacenter Deployments: Configuring and managing Cassandra clusters across multiple data centers for high availability and disaster recovery.
Cassandra on Kubernetes: Deploying and managing Cassandra clusters in a Kubernetes environment.
9. Hands-On Projects
Building Real-World Applications: Practical exercises and projects to build and deploy applications using Cassandra.
Performance Tuning Exercises: Real-world scenarios for optimizing and scaling Cassandra databases.
Data Migration Projects: Learn how to migrate data from relational databases to Cassandra.
Who Should Take This Course?
Database administrators, data architects, and developers looking to specialize in NoSQL databases.
Big data professionals who want to integrate Cassandra into their technology stack.
Anyone interested in learning how to design, deploy, and manage scalable and fault-tolerant databases.
Prerequisites
Basic understanding of databases and SQL.
Familiarity with distributed systems and networking concepts is helpful but not mandatory.
Course Outcomes
Proficiency in Cassandra: Gain a deep understanding of Cassandra’s architecture, data modeling, and query language.
Skill in Deployment and Management: Learn to deploy, manage, and scale Cassandra clusters effectively.
Capability in Real-World Applications: Apply your knowledge to real-world applications, integrating Cassandra with other big data technologies.
Course Delivery
The course can be delivered online or in-person, with a combination of lectures, hands-on labs, and projects.
Many courses offer certification upon completion, validating your expertise in Cassandra.
