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OLAP & Data Warehouse Analytics Skill

Select and structure OLAP strategies with DuckDB and ClickHouse analytics components.

75|8|Updated Dec 20, 2023
Search Tags:#data warehouse#pivot tables#duckdb#clickhouse#star schema#olap#dashboard embedding
One-click install
npx skills add https://github.com/flowkraft/datapallas --skill olap-data-warehouse-analytics-skill
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: OLAP & Data Warehouse Analytics Skill
Source: https://github.com/flowkraft/datapallas/tree/main/asbl/src/main/external-resources/db-template/_apps/flowkraft/_ai-hub/.skills/olap-data-warehouse-analytics
Command: npx skills add https://github.com/flowkraft/datapallas --skill olap-data-warehouse-analytics-skill

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

It helps you choose and design the right OLAP/data-warehouse approach so your dashboards stay fast and maintainable as data volume and complexity grow.

Core Features & Use Cases

  • Embeddable OLAP analytics components: Configure and embed interactive reports, charts, pivot tables, parameter forms, and data tables using a shared set of web components.
  • Dashboards-ready analytics strategy: Pick the simplest level that works (DuckDB multi-source, DuckDB sync, DuckDB star schema, ClickHouse byte-to-byte, ClickHouse star schema) based on data size and query patterns.
  • ETL/architecture planning support: Use replication and denormalization (star schema) concepts to reduce JOIN complexity and improve dashboard responsiveness.
  • Practical guidance with real examples: Apply “start simple, scale only when needed” to avoid over-engineering while still planning for higher performance ceilings.

Quick Start

Ask: “Design my OLAP approach for a dashboard—should I start with DuckDB multi-source, then move to DuckDB sync or ClickHouse star schema, and how would I structure the dimensions and facts for my use case?”

Frequently Asked Questions about OLAP & Data Warehouse Analytics Skill

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

FAQPage Schema
How do I choose between DuckDB and ClickHouse for my data warehouse dashboards?▼

Selecting a data warehouse engine depends on data volume and query patterns. This approach advises starting with DuckDB multi-source or sync for simpler needs, then scaling to ClickHouse star schema for higher performance ceilings as complexity grows.

What is the best way to structure an OLAP star schema for fast embedded analytics?▼

Structuring an OLAP star schema involves denormalizing data into fact and dimension tables to reduce JOIN complexity. This improves dashboard responsiveness and supports consistent component configuration for interactive reports and pivot tables.

How do I embed pivot tables and interactive charts into my dashboard?▼

Embedding pivot tables and charts uses shared web components configured via a Groovy DSL. This enables interactive reports, parameter forms, and data tables with usage-linkable embedding parameters for scalable dashboards.

Do I need an OLTP-to-OLAP sync process to keep my dashboards fast?▼

An OLTP-to-OLAP sync process is needed when transactional data grows too large for direct querying. Replicating data into DuckDB or ClickHouse separates analytical workloads, keeping dashboards responsive.

When should I avoid over-engineering my data warehouse architecture?▼

Avoid over-engineering your data warehouse by applying a "start simple, scale only when needed" principle. Begin with DuckDB multi-source configurations and only move to a ClickHouse star schema when query performance demands it.

Can I use DuckDB for multi-source reporting before moving to a full star schema?▼

DuckDB supports multi-source reporting by querying data directly across sources. It serves as an initial OLAP layer before requiring a full star schema or replication to ClickHouse for larger datasets.

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