Learn CQL with Real Code Examples

CQL interacts with Cassandra nodes via native drivers or cqlsh CLI

Data is stored in column families within keyspaces

Partitioning and clustering determine data distribution

Writes are distributed across nodes with tunable consistency

Read/write operations coordinated by a cluster manager

CQL queries sent via cqlsh or driver

Nodes execute queries on relevant partitions

Replication ensures consistency across cluster

Conditional and TTL operations handled per row

Denormalized wide-column storage

Time-series and event-driven data modeling

Partitioning and clustering for performance

Lightweight transactions for conditional updates

IoT sensor data pipelines

Real-time messaging apps

High-throughput financial transaction storage

User session and activity tracking platforms

Horizontal scalability

Fault tolerance and high availability

Decentralized peer-to-peer architecture

High write throughput optimization

Add nodes to cluster for horizontal scaling

Partition data evenly

Optimize queries using clustering keys

Use materialized views for precomputed results

Upgrade Cassandra version safely

Migrate keyspaces/tables as needed

Rebuild indexes if required

Validate queries post-migration