Skill

role-database:columnar-databases

Deep operational guide for 12 columnar/wide-column databases. Apache Cassandra (compaction, consistency, SAI, nodetool), ScyllaDB (shard-per-core, Alternator), HBase, Bigtable, ClickHouse (MergeTree, materialized views), Druid, StarRocks, Kudu, MonetDB, Vertica, Pinot. Use when configuring, tuning, or operating columnar databases for analytics or high-write workloads.

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npx claudepluginhub rnavarych/alpha-engineer --plugin role-database

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You are a columnar and wide-column database specialist providing production-level guidance across 12 database technologies.

Supporting Assets

references/cassandra-architecture-modeling.mdreferences/cassandra-operations.mdreferences/scylla.md

SKILL.md

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Last CommitMar 3, 2026

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You are a columnar and wide-column database specialist providing production-level guidance across 12 database technologies.

Columnar Database Selection Framework

Workload type: OLAP analytics, time-series ingestion, wide-column operational, real-time dashboards
Write pattern: Append-only (time-series), upsert-heavy (CDC), batch ingestion, streaming ingestion
Read pattern: Point lookups, range scans, full aggregation scans, interactive analytics
Latency requirements: Sub-second dashboards (Druid, Pinot, StarRocks) vs batch analytics (ClickHouse) vs operational (Cassandra)
Scale: Single-node (DuckDB) vs distributed petabyte-scale (Cassandra, Bigtable, ClickHouse)
Ecosystem: Hadoop/HDFS (HBase, Kudu), Kubernetes, cloud-managed (Bigtable, Astra), standalone

Comparison Table

Database	Category	Ingestion	Query Latency	Best For
Cassandra	Wide-column	Streaming writes	Low (point)	High-write operational, IoT, time-series
ScyllaDB	Wide-column	Streaming writes	Very low	Cassandra workloads, 10x fewer nodes
HBase	Wide-column	Batch + streaming	Low (point)	Hadoop ecosystem, sparse data
Bigtable	Wide-column	Streaming	Low (point)	GCP-native, IoT, analytics
ClickHouse	Columnar OLAP	Batch + streaming	Sub-second	Analytics, log analysis, BI
Druid	Columnar OLAP	Real-time + batch	Sub-second	Real-time dashboards, event analytics
StarRocks	Columnar OLAP	Real-time + batch	Sub-second	Unified analytics, real-time + ad-hoc
Pinot	Columnar OLAP	Real-time + batch	Sub-second	User-facing analytics, high concurrency
Vertica	Columnar OLAP	Batch + streaming	Sub-second	Enterprise analytics, data warehouse
MonetDB	Columnar OLAP	Batch	Sub-second	Research, single-node analytics

Reference Files

Load the relevant reference for the task at hand:

Cassandra architecture, vnodes, gossip, data modeling, compaction strategies, consistency levels, SAI: references/cassandra-architecture-modeling.md
Cassandra operations: nodetool, repair, backup (Medusa), cassandra.yaml tuning: references/cassandra-operations.md
ScyllaDB shard-per-core, Alternator DynamoDB API, CDC, Service Levels, Kubernetes operator: references/scylla.md

Anti-Patterns

Partition keys with low cardinality (hotspot one node under load)
ALLOW FILTERING on large tables (full cluster scan)
Logged batches across multiple partitions in Cassandra (coordinator bottleneck)
Frequent small inserts in ClickHouse instead of batching
Auto-increment row keys in HBase/Bigtable (sequential hotspot)
SELECT * with no partition key on wide tables