Apache Cassandra Course in Bangalore

Module 1: Introduction to Apache Cassandra

• Cassandra overview: distributed, highly available, and linearly scalable
• Cassandra architecture: ring topology, vnodes, and gossip protocol
• CAP theorem: Cassandra's AP-focused consistency model
• Cassandra use cases: IoT, messaging, time-series, and recommendation data

Module 2: Cassandra Data Model and CQL

• Keyspaces, tables, partition keys, and clustering columns
• CQL fundamentals: CREATE, INSERT, SELECT, UPDATE, and DELETE
• Data types: text, int, uuid, timeuuid, list, set, and map
• Primary key design: composite partition keys and clustering order

Module 3: Cassandra Architecture Deep Dive

• Consistent hashing and token-based data distribution
• Replication strategies: SimpleStrategy and NetworkTopologyStrategy
• Replication factor and data durability considerations
• Snitch types: SimpleSnitch, GossipingPropertyFileSnitch, and EC2Snitch

Module 4: Read and Write Paths

• Write path: CommitLog, Memtable, and SSTable lifecycle
• Compaction strategies: STCS, LCS, and TWCS for different workloads
• Read path: Bloom filters, key cache, row cache, and SSTable reads
• Consistency levels: ONE, QUORUM, ALL, LOCAL_QUORUM, and EACH_QUORUM

Module 5: Data Modelling for Cassandra

• Query-first data modelling: designing tables around access patterns
• Denormalisation: duplicating data for query efficiency
• Avoiding anti-patterns: allowing filtering and unbounded partitions
• Time-series data modelling with TTL and TWCS compaction

Module 6: Cassandra Operations and Maintenance

• nodetool commands: status, ring, compaction, and repair
• Cassandra repair: full repair vs incremental repair best practices
• Adding and removing nodes: bootstrapping and decommissioning
• Backup and restore: snapshot-based backup strategy

Module 7: Cassandra Performance Tuning

• JVM tuning: heap size, GC configuration, and GC logging
• Read and write performance benchmarking with cassandra-stress
• Identifying and resolving partition hotspots
• Monitoring with Prometheus, Grafana, and DataStax OpsCenter

Module 8: Capstone Project and Assessment

• Cassandra data model design for a time-series IoT application
• CQL application development and query optimisation exercise
• Cluster operations: node addition, repair, and monitoring setup
• Final assessment and course certification

Experience in the Industry Learn from Apache Cassandra specialists who have architected and operated distributed NoSQL clusters for IoT platforms, social networks, financial transaction systems, and messaging applications processing millions of writes per second.

Backgrounds at the Top Our Cassandra trainers have managed large Cassandra clusters at technology companies, telecommunications providers, and data engineering teams where Cassandra's linear scalability and fault tolerance are critical requirements.

Clear & Effective Teaching Cassandra architecture, CQL, data modelling, consistency levels, compaction strategies, cluster operations, and performance tuning are taught with practical distributed database design examples.

Hands-On Learning Focus Students design Cassandra data models, write CQL applications, perform cluster operations, tune compaction strategies, and monitor cluster health through structured hands-on lab exercises.

Up-to-Date Knowledge Trainers keep content current with the latest Apache Cassandra releases, DataStax Astra serverless Cassandra, Cassandra 5.0 features, and evolving distributed database best practices.