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Apache Cassandra

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Apache Cassandra

Apache Cassandra

Duration
45 Hours

Course Description

            An Apache Cassandra course typically covers the fundamentals of NoSQL databases, Cassandra's architecture, data modeling, CQL (Cassandra Query Language), and its use in building scalable, fault-tolerant applications. The course will also delve into topics like replication, consistency, performance optimization, and cluster management. Hands-on experience with real-world projects is often included. 

Course Outline For Apache Cassandra

1. Introduction to Big Data and NoSQL databases

  • Understanding Big Data: An explanation of what Big Data is, its characteristics (the 3Vs or 5Vs – volume, velocity, variety, veracity, value), and the challenges it presents to traditional relational databases.
  • Limitations of RDBMS: Discusses the shortcomings of relational database management systems in handling large-scale, unstructured data.
  • Introduction to NoSQL Databases: Defines NoSQL databases, their characteristics, and how they differ from RDBMS.
  • CAP Theorem: Explores the CAP Theorem (Consistency, Availability, Partition Tolerance) and its significance in distributed systems, particularly for Cassandra's eventual consistency model. 

2. Introduction to Apache Cassandra

  • Cassandra Fundamentals: Introduction to Apache Cassandra itself – its origin (Facebook), main features (distributed, scalable, high availability, fault tolerance), and advantages over other database systems.
  • Key Features: Discusses elastic scalability, high availability, fault tolerance, tuneable consistency, schema-free design, and high performance.
  • Use Cases: Examples of how major companies like Netflix, Instagram, Apple, Walmart, and PayPal leverage Cassandra. 

3. Cassandra architecture

  • Cassandra as a Distributed Database: Explains the concept of a distributed database and how Cassandra achieves it.
  • Key Cassandra Components: Deep dive into components like nodes, clusters, keyspaces, column families (tables), commit logs, memtables, and SSTables.
  • Data Distribution and Replication: Understanding how Cassandra partitions and replicates data across nodes and data centers, along with concepts like replication factor, replica placement strategy, and snitches.
  • Read and Write Paths: Detailed explanation of how Cassandra handles read and write operations, including topics like hinted handoff, read repair, and consistency levels.
  • Failure Detection and Recovery: Exploring how Cassandra detects and handles node failures, including the Gossip protocol.
  • Compaction and Tombstones: Understanding how Cassandra manages data storage and optimization through compaction strategies and the concept of tombstones for deletes. 

4. Cassandra data model

  • Data Modeling Basics: Principles of designing an efficient Cassandra data model, including best practices for schema design and optimizing for queries.
  • Keyspaces, Tables, and Columns: Deep dive into the structure of Cassandra's data model, including the roles of keyspaces, tables (column families), and various column types (static, clustering, regular).
  • Primary Keys: Understanding the importance of primary keys, including partition keys and clustering keys, and how they determine data distribution and order within a partition.
  • Secondary Indexes: Learning when and how to use secondary indexes for efficient data retrieval.
  • Collections and User-Defined Data Types (UDTs): Working with collection data types (lists, sets, maps) and defining custom UDTs for complex data structures. 

5. Cassandra Query Language (CQL)

  • Courses cover CQL syntax, comparing it to SQL, and performing DDL, DML, and CRUD operations. Interaction with the database using the cqlsh utility is also typically included. 

6. Installation and configuration

  • Topics include understanding prerequisites, installation steps, and configuration for performance and security. Setting up single and multi-node clusters is also covered. 

7. Cluster management and monitoring

  • This section focuses on managing nodes and using tools such as Nodetool and JMX for monitoring. It may also introduce OpsCenter for visual management. Performance tuning, backup and recovery strategies (snapshots, restoration), and security measures (authentication, authorization, encryption) are also common topics. 

8. Integration with other technologies

  • Courses may explore how Cassandra integrates with ecosystems like Hadoop, Spark, and Kafka. 

9. Advanced concepts and best practices

  • Advanced topics include tunable consistency, advanced data modeling techniques, using the Cassandra Stress tool, and applying concepts to real-world projects. 
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