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MassimilianoPili
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dba-cassandra

Design and administer Apache Cassandra and ScyllaDB 5.0+ databases with CQL schemas and performance tuning.

Updated Mar 7, 2026
Search Tags:#database#data modeling#performance tuning#cql#cassandra#dba#scylladb
One-click install
npx skills add https://github.com/MassimilianoPili/agent-framework --skill dba-cassandra
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: dba-cassandra
Source: https://github.com/MassimilianoPili/agent-framework/tree/main/.claude/agents/dba-cassandra
Command: npx skills add https://github.com/MassimilianoPili/agent-framework --skill dba-cassandra

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill streamlines the design and administration of Apache Cassandra and ScyllaDB databases, ensuring optimal performance and scalability for wide-column stores.

Core Features & Use Cases

  • Data Modeling: Design efficient, query-driven data models with appropriate partition and clustering keys.
  • Schema Management: Write and optimize CQL schemas, including advanced features like SAI indexes and Time-Window Compaction Strategy (TWCS).
  • Administration: Tune consistency levels, manage compaction strategies, and perform essential nodetool operations.
  • Use Case: Design a new Cassandra table for real-time event tracking, optimizing for high write throughput and efficient time-based querying using TWCS.

Quick Start

Use the dba-cassandra skill to design a new table for user activity logs, optimizing for time-series queries and a 30-day TTL.

Frequently Asked Questions about dba-cassandra

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I design a Cassandra data model for high write throughput and time-based querying?▼

Cassandra data modeling for high throughput requires query-driven schemas using appropriate partition and clustering keys, optimized further by Time-Window Compaction Strategy (TWCS) to efficiently manage time-series data and TTLs.

What's the best way to optimize CQL schema creation for wide-column stores?▼

Optimizing CQL schema creation for wide-column stores involves leveraging Storage-Attached Indexes (SAI) and tuning consistency levels to ensure scalable performance and flexible querying without compromising write speeds.

Can I use this approach to administer and tune ScyllaDB 5.0+ databases?▼

Yes, the same query-driven data modeling, CQL schema optimization, and performance tuning techniques for wide-column stores apply directly to administering ScyllaDB 5.0+ databases, ensuring consistent high performance across both platforms.

How does TWCS compaction improve time-series data management in Cassandra?▼

TWCS compaction improves time-series data management in Cassandra and ScyllaDB by grouping data into discrete time windows, which optimizes compaction performance and efficiently handles TTL expiration for wide-column stores.

When do I need to adjust consistency levels in Cassandra or ScyllaDB?▼

Adjust consistency levels in Cassandra or ScyllaDB when balancing transactional guarantees against latency and availability, ensuring your wide-column store meets specific application requirements for read and write performance.

What nodetool operations are essential for administering Apache Cassandra?▼

Essential nodetool operations for administering Apache Cassandra include managing compaction strategies, monitoring cluster health, and tuning performance to maintain scalable and efficient wide-column store architectures.

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