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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
• Optimizes warehouse query performance (clustering, partitioning, materialized views)
• 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

1. 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.

2. 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.

3. 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.

4. 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.

5. 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

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

Slowly changing dimensions (SCD)

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       INT         PRIMARY KEY,  -- YYYYMMDD integer
  full_date     DATE        NOT NULL,
  day_of_week   VARCHAR(10) NOT NULL,
  month_name    VARCHAR(10) NOT NULL,
  quarter       INT         NOT NULL,
  fiscal_year   INT         NOT NULL,
  is_weekend    BOOLEAN     NOT NULL,
  is_holiday    BOOLEAN     NOT NULL
);

-- Customer dimension
CREATE TABLE dim_customer (
  customer_sk   INT         PRIMARY 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 DATE       NOT NULL,
  expiry_date    DATE       NOT NULL DEFAULT '9999-12-31',
  is_current     BOOLEAN    NOT NULL DEFAULT TRUE
);

-- Product dimension
CREATE TABLE dim_product (
  product_sk    INT         PRIMARY 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       BIGINT      PRIMARY KEY,
  date_sk       INT         NOT NULL REFERENCES dim_date(date_sk),
  customer_sk   INT         NOT NULL REFERENCES dim_customer(customer_sk),
  product_sk    INT         NOT NULL REFERENCES dim_product(product_sk),
  quantity      INT         NOT NULL,
  unit_price    DECIMAL(12,2) NOT NULL,
  discount_amt  DECIMAL(12,2) NOT NULL DEFAULT 0,
  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 2
MERGE INTO 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 it
WHEN MATCHED
  AND (target.segment  != source.segment
    OR target.region   != source.region)
THEN UPDATE SET
  target.expiry_date = CURRENT_DATE - 1,
  target.is_current  = FALSE

-- No match: brand new customer
WHEN NOT MATCHED THEN INSERT (
  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 records
INSERT INTO dim_customer
SELECT dim_customer_seq.NEXTVAL,
       s.customer_id, s.name, s.segment, s.region,
       CURRENT_DATE, '9999-12-31', TRUE
FROM staging_customers s
JOIN dim_customer d
  ON s.customer_id = d.customer_id
  AND d.expiry_date = CURRENT_DATE - 1
  AND 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 MERGE
MERGE `project.dataset.dim_customer` AS target
USING `project.dataset.staging_customers` AS source
  ON target.customer_id = source.customer_id
     AND target.is_current = TRUE

WHEN MATCHED
  AND (target.segment != source.segment
    OR target.region  != source.region)
THEN UPDATE SET
  expiry_date = DATE_SUB(CURRENT_DATE(), INTERVAL 1 DAY),
  is_current  = FALSE

WHEN NOT MATCHED BY TARGET THEN INSERT (
  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 records
INSERT INTO `project.dataset.dim_customer`
SELECT GENERATE_UUID(), s.customer_id, s.name, s.segment, s.region,
       CURRENT_DATE(), DATE '9999-12-31', TRUE
FROM `project.dataset.staging_customers` s
INNER JOIN `project.dataset.dim_customer` d
  ON s.customer_id = d.customer_id
  AND d.expiry_date = DATE_SUB(CURRENT_DATE(), INTERVAL 1 DAY);

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 depth
SELECT SYSTEM$CLUSTERING_DEPTH('fct_sales');
-- Values close to 1.0 = well clustered; > 5 = recluster needed

-- Use result caching and warehouse sizing
ALTER WAREHOUSE analytics_wh SET
  WAREHOUSE_SIZE = 'MEDIUM'
  AUTO_SUSPEND = 60
  AUTO_RESUME = TRUE;

-- Materialized views for expensive aggregations
CREATE MATERIALIZED VIEW mv_daily_sales AS
SELECT date_sk,
       SUM(net_amount) AS total_sales,
       COUNT(*)        AS transaction_count
FROM fct_sales
GROUP BY 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     DATE        NOT NULL,
  customer_sk   STRING      NOT NULL,
  product_sk    STRING      NOT NULL,
  quantity      INT64       NOT NULL,
  net_amount    NUMERIC     NOT NULL
)
PARTITION BY 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 refresh
CREATE MATERIALIZED VIEW `project.dataset.mv_daily_sales`
AS
SELECT sale_date,
       SUM(net_amount) AS total_sales,
       COUNT(*)        AS transaction_count
FROM `project.dataset.fct_sales`
GROUP BY 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 spine
CREATE TABLE `project.dataset.dim_date` AS
WITH date_spine AS (
  SELECT date
  FROM UNNEST(
    GENERATE_DATE_ARRAY('2020-01-01', '2030-12-31', INTERVAL 1 DAY)
  ) AS date
)
SELECT
  CAST(FORMAT_DATE('%Y%m%d', date) AS INT64) AS date_sk,
  date                                        AS full_date,
  FORMAT_DATE('%A', date)                     AS day_of_week,
  FORMAT_DATE('%B', date)                     AS month_name,
  EXTRACT(QUARTER FROM date)                  AS quarter,
  EXTRACT(YEAR FROM date)                     AS fiscal_year,
  EXTRACT(DAYOFWEEK FROM date) IN (1, 7)      AS is_weekend
FROM date_spine;

Anti-patterns / common mistakes

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.

Related skills

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• analytics-engineering - Building dbt models, designing semantic layers, defining metrics, creating self-serve...
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• database-engineering - Designing database schemas, optimizing queries, creating indexes, planning migrations, or...

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