Use this skill when designing data warehouses, building star or snowflake schemas, implementing slowly changing dimensions (SCDs), writing analytical SQL for Snowflake or BigQuery, creating fact and dimension tables, or planning ETL/ELT pipelines for analytics. Triggers on dimensional modeling, surrogate keys, conformed dimensions, warehouse architecture, data vault, partitioning strategies, materialized views, and any task requiring OLAP schema design or warehouse query optimization.
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Quellanweisungen · Schreibgeschützte Vorschau
name
data-warehousing
version
0.1.0
description
Use this skill when designing data warehouses, building star or snowflake schemas, implementing slowly changing dimensions (SCDs), writing analytical SQL for Snowflake or BigQuery, creating fact and dimension tables, or planning ETL/ELT pipelines for analytics. Triggers on dimensional modeling, surrogate keys, conformed dimensions, warehouse architecture, data vault, partitioning strategies, materialized views, and any task requiring OLAP schema design or warehouse query optimization.
When this skill is activated, always start your first response with the 🧢 emoji.
Data Warehousing
A practical framework for designing, building, and optimizing analytical data warehouses
using dimensional modeling. This skill covers star and snowflake schema design, slowly
changing dimension (SCD) patterns, and platform-specific guidance for Snowflake and
BigQuery. The focus is on making the right modeling decisions that balance query
performance, storage cost, and maintainability for downstream analytics consumers.
When to use this skill
Trigger this skill when the user:
Designs a star schema or snowflake schema for analytical workloads
Implements slowly changing dimensions (Type 1, 2, 3, or hybrid)
Builds fact tables (transactional, periodic snapshot, or accumulating snapshot)
Writes analytical SQL targeting Snowflake or BigQuery
Plans ETL/ELT pipelines that load data into a warehouse
Creates conformed dimensions shared across multiple fact tables
Chooses between Snowflake and BigQuery for a new project
Do NOT trigger this skill for:
OLTP schema design or transactional database tuning (use database-engineering)
Data pipeline orchestration tools like Airflow or dbt (those have their own skills)
Key principles
Model for the query, not the source - Warehouse schemas exist to make analytical
queries fast and intuitive. Denormalize aggressively compared to OLTP. If analysts
need to join seven tables to answer a basic question, the model is wrong.
Grain is the single most important decision - Every fact table must have a clearly
declared grain (one row = one transaction, one day per customer, etc.). Mixing grains
in a single fact table causes double-counting and broken aggregations that are
extremely hard to debug.
Conformed dimensions enable cross-process analysis - A shared dim_customer or
dim_date table used across all fact tables lets analysts drill across business
processes without reconciliation headaches. Build conformed dimensions first.
Slowly changing dimensions must be an explicit design choice - Every dimension
attribute changes over time. Decide upfront whether to overwrite (Type 1), track
history (Type 2), or store previous value (Type 3). Defaulting to Type 1 and later
needing history is a painful migration.
Partition and cluster for your access patterns - Cloud warehouses charge by data
scanned. Partitioning by date and clustering by high-cardinality filter columns can
reduce costs and query times by 10-100x. Design these at table creation time.
Core concepts
Dimensional modeling entities
Entity
Role
Example
Fact table
Stores measurable business events (metrics)
fct_orders, fct_page_views
Dimension table
Stores descriptive context for facts
dim_customer, dim_product, dim_date
Surrogate key
Warehouse-generated integer/hash PK for dimensions
customer_sk (vs natural key customer_id)
Degenerate dimension
Dimension attribute stored directly on the fact table
order_number on fct_order_items
Conformed dimension
A dimension shared identically across multiple fact tables
dim_date, dim_geography
Star schema vs snowflake schema
A star schema has fact tables at the center with denormalized dimension tables
radiating outward - one join from fact to any dimension. A snowflake schema
normalizes dimensions into sub-dimensions (e.g., dim_product -> dim_category ->
dim_department). Star schemas are preferred for most analytical workloads because they
minimize joins and are easier for BI tools to consume. Snowflake schemas save storage
but add join complexity - only use them when dimension tables are extremely large
(100M+ rows) and share sub-dimensions across many parents.
Fact table types
Type
Grain
Example
When to use
Transaction
One row per event
fct_orders
Most common; captures atomic events
Periodic snapshot
One row per entity per period
fct_daily_inventory
Regular status measurements
Accumulating snapshot
One row per process lifetime
fct_order_fulfillment
Track milestones (ordered, shipped, delivered)
Factless fact
No measures, only dimension keys
fct_student_attendance
Record that an event occurred
Slowly changing dimensions (SCD)
Type
Behavior
Trade-off
Type 0
Never changes (fixed attributes)
Use for birth date, original sign-up date
Type 1
Overwrite old value
Simple but loses history
Type 2
Add new row with version tracking
Preserves full history; most common for analytics
Type 3
Add column for previous value
Tracks one prior value only; rarely sufficient
Type 6 (hybrid 1+2+3)
Type 2 rows + current value column
Best of both: history + easy current-state queries
Common tasks
Design a star schema
Model a retail sales domain with conformed dimensions.
-- Date dimension (conformed - used by all fact tables)CREATE TABLE dim_date (
date_sk INTPRIMARY KEY, -- YYYYMMDD integer
full_date DATENOT NULL,
day_of_week VARCHAR(10) NOT NULL,
month_name VARCHAR(10) NOT NULL,
quarter INTNOT NULL,
fiscal_year INTNOT NULL,
is_weekend BOOLEANNOT NULL,
is_holiday BOOLEANNOT NULL
);
-- Customer dimensionCREATE TABLE dim_customer (
customer_sk INTPRIMARY KEY, -- surrogate key
customer_id VARCHAR(50) NOT NULL, -- natural key
name VARCHAR(200),
segment VARCHAR(50),
region VARCHAR(100),
-- SCD Type 2 tracking
effective_date DATENOT NULL,
expiry_date DATENOT NULLDEFAULT'9999-12-31',
is_current BOOLEANNOT NULLDEFAULTTRUE
);
-- Product dimensionCREATE TABLE dim_product (
product_sk INTPRIMARY KEY,
product_id VARCHAR(50) NOT NULL,
product_name VARCHAR(200),
category VARCHAR(100),
subcategory VARCHAR(100),
brand VARCHAR(100),
unit_cost DECIMAL(12,2)
);
-- Sales fact table (transaction grain: one row per line item)CREATE TABLE fct_sales (
sale_sk BIGINTPRIMARY KEY,
date_sk INTNOT NULLREFERENCES dim_date(date_sk),
customer_sk INTNOT NULLREFERENCES dim_customer(customer_sk),
product_sk INTNOT NULLREFERENCES dim_product(product_sk),
quantity INTNOT NULL,
unit_price DECIMAL(12,2) NOT NULL,
discount_amt DECIMAL(12,2) NOT NULLDEFAULT0,
net_amount DECIMAL(12,2) NOT NULL,
order_number VARCHAR(50) NOT NULL-- degenerate dimension
);
Declare the grain explicitly in a comment or documentation: "One row per order line
item per day." Every team member must agree on the grain before building downstream
reports.
Implement SCD Type 2 in Snowflake
Track full history of customer attribute changes using MERGE.
-- Snowflake MERGE for SCD Type 2MERGEINTO dim_customer AS target
USING staging_customers AS source
ON target.customer_id = source.customer_id
AND target.is_current =TRUE-- Existing row where attributes changed: expire itWHEN MATCHED
AND (target.segment != source.segment
OR target.region != source.region)
THENUPDATESET
target.expiry_date =CURRENT_DATE-1,
target.is_current =FALSE-- No match: brand new customerWHENNOT MATCHED THENINSERT (
customer_sk, customer_id, name, segment, region,
effective_date, expiry_date, is_current
) VALUES (
dim_customer_seq.NEXTVAL,
source.customer_id, source.name, source.segment, source.region,
CURRENT_DATE, '9999-12-31', TRUE
);
-- Second pass: insert the new current row for changed recordsINSERT INTO dim_customer
SELECT dim_customer_seq.NEXTVAL,
s.customer_id, s.name, s.segment, s.region,
CURRENT_DATE, '9999-12-31', TRUEFROM staging_customers s
JOIN dim_customer d
ON s.customer_id = d.customer_id
AND d.expiry_date =CURRENT_DATE-1AND d.is_current =FALSE;
Implement SCD Type 2 in BigQuery
BigQuery lacks MERGE with multiple actions on the same row, so use a MERGE + INSERT pattern.
-- BigQuery SCD Type 2 using MERGEMERGE `project.dataset.dim_customer` AS target
USING `project.dataset.staging_customers` AS source
ON target.customer_id = source.customer_id
AND target.is_current =TRUEWHEN MATCHED
AND (target.segment != source.segment
OR target.region != source.region)
THENUPDATESET
expiry_date = DATE_SUB(CURRENT_DATE(), INTERVAL1DAY),
is_current =FALSEWHENNOT MATCHED BY TARGET THENINSERT (
customer_sk, customer_id, name, segment, region,
effective_date, expiry_date, is_current
) VALUES (
GENERATE_UUID(),
source.customer_id, source.name, source.segment, source.region,
CURRENT_DATE(), DATE'9999-12-31', TRUE
);
-- Insert new current rows for changed recordsINSERT INTO `project.dataset.dim_customer`
SELECT GENERATE_UUID(), s.customer_id, s.name, s.segment, s.region,
CURRENT_DATE(), DATE'9999-12-31', TRUEFROM `project.dataset.staging_customers` s
INNERJOIN `project.dataset.dim_customer` d
ON s.customer_id = d.customer_id
AND d.expiry_date = DATE_SUB(CURRENT_DATE(), INTERVAL1DAY);
Optimize Snowflake performance
-- Cluster keys for large fact tables (order matters: most filtered first)ALTER TABLE fct_sales CLUSTER BY (date_sk, customer_sk);
-- Monitor clustering depthSELECTSYSTEM$CLUSTERING_DEPTH('fct_sales');
-- Values close to 1.0 = well clustered; > 5 = recluster needed-- Use result caching and warehouse sizingALTER WAREHOUSE analytics_wh SET
WAREHOUSE_SIZE ='MEDIUM'
AUTO_SUSPEND =60
AUTO_RESUME =TRUE;
-- Materialized views for expensive aggregationsCREATE MATERIALIZED VIEW mv_daily_sales ASSELECT date_sk,
SUM(net_amount) AS total_sales,
COUNT(*) AS transaction_count
FROM fct_sales
GROUPBY date_sk;
Snowflake charges per second of compute. Use AUTO_SUSPEND = 60 to avoid paying for
idle warehouses. Separate warehouses for ETL and analytics to prevent contention.
Optimize BigQuery performance
-- Partition by date (reduces bytes scanned = lower cost)CREATE TABLE `project.dataset.fct_sales`
(
sale_sk STRING NOT NULL,
sale_date DATENOT NULL,
customer_sk STRING NOT NULL,
product_sk STRING NOT NULL,
quantity INT64 NOT NULL,
net_amount NUMERICNOT NULL
)
PARTITIONBY sale_date
CLUSTER BY customer_sk, product_sk;
-- Check bytes scanned before running expensive queries-- Use dry run: bq query --dry_run --use_legacy_sql=false 'SELECT ...'-- Materialized view with automatic refreshCREATE MATERIALIZED VIEW `project.dataset.mv_daily_sales`
ASSELECT sale_date,
SUM(net_amount) AS total_sales,
COUNT(*) AS transaction_count
FROM `project.dataset.fct_sales`
GROUPBY sale_date;
BigQuery charges per TB scanned. Always partition by the primary date filter column
and cluster by up to four frequently filtered columns. Use INFORMATION_SCHEMA.JOBS
to monitor cost per query.
Build a date dimension
Every warehouse needs a pre-populated date dimension. Generate it once.
-- BigQuery: generate a date spineCREATE TABLE `project.dataset.dim_date` ASWITH date_spine AS (
SELECTdateFROMUNNEST(
GENERATE_DATE_ARRAY('2020-01-01', '2030-12-31', INTERVAL1DAY)
) ASdate
)
SELECTCAST(FORMAT_DATE('%Y%m%d', date) AS INT64) AS date_sk,
dateAS full_date,
FORMAT_DATE('%A', date) AS day_of_week,
FORMAT_DATE('%B', date) AS month_name,
EXTRACT(QUARTER FROMdate) AS quarter,
EXTRACT(YEARFROMdate) AS fiscal_year,
EXTRACT(DAYOFWEEK FROMdate) IN (1, 7) AS is_weekend
FROM date_spine;
Anti-patterns / common mistakes
Mistake
Why it's wrong
What to do instead
Mixed grain in a fact table
Adding daily and monthly aggregates in one table causes double-counting when summed
Loses fiscal calendar, holiday flags, and forces repeated date logic in every query
Build a shared dim_date and join all facts to it
Defaulting everything to SCD Type 1
Loses history; cannot answer "what segment was this customer in last quarter?"
Choose SCD type per attribute explicitly during design
No partitioning on large fact tables
Full table scans on every query; cloud costs explode
Partition by date and cluster by top filter columns
Over-normalizing dimensions (deep snowflake)
Adds join complexity; BI tools struggle with 5+ join paths
Flatten to star schema unless dimension is 100M+ rows
Gotchas
Mixed grain in a fact table is the hardest bug to find - Adding a row-level sale and a daily summary row into the same fact table looks harmless until someone sums net_amount across a date range and gets 2x the correct revenue. Grain violations produce numerically plausible but wrong answers. Declare the grain in a comment on the CREATE TABLE statement and enforce it with a uniqueness test.
SCD Type 2 MERGE leaving orphaned "current" rows - A MERGE statement that expires old rows but fails (network timeout, transaction rollback) after the UPDATE but before the INSERT leaves the dimension in an inconsistent state: the old row is expired but no new current row exists. Always wrap SCD Type 2 updates in a transaction and verify row count after each MERGE + INSERT pair.
Natural keys as fact table foreign keys break on source changes - Using customer_email instead of customer_sk as the foreign key on a fact table means a customer who changes their email address breaks all historical joins. Surrogate keys are immune to natural key changes. Always generate surrogate keys for dimension foreign key references.
BigQuery partition filter not used in JOIN conditions - BigQuery only prunes partitions when the partition column appears in a WHERE clause, not in a JOIN ON condition. A query joining a partitioned table only on customer_id performs a full table scan on the partitioned table, negating the cost benefit. Always include the partition column in the WHERE clause.
Snowflake warehouse not auto-suspending in development - A development or analytics warehouse left running with AUTO_SUSPEND disabled (or set to a long interval) accumulates compute costs continuously. Set AUTO_SUSPEND = 60 on all non-production warehouses, and use separate warehouses for ETL loads vs. analyst queries to prevent compute contention.
References
For detailed implementation patterns and platform-specific guidance, load the relevant
file from references/:
references/snowflake-patterns.md - Snowflake-specific features: stages, streams,
tasks, time travel, zero-copy cloning, and warehouse sizing strategies
references/bigquery-patterns.md - BigQuery-specific features: nested/repeated fields,
federated queries, BI Engine, slots vs on-demand pricing, and scheduled queries
references/scd-patterns.md - Deep dive on all SCD types with complete SQL
implementations, hybrid patterns, and migration strategies between types
Only load a references file if the current task requires it - they are long and will
consume context.
Companion check
On first activation of this skill in a conversation: check which companion skills are installed by running ls ~/.claude/skills/ ~/.agent/skills/ ~/.agents/skills/ .claude/skills/ .agent/skills/ .agents/skills/ 2>/dev/null. Compare the results against the recommended_skills field in this file's frontmatter. For any that are missing, mention them once and offer to install:
