How to Apply Data Modeling Best Practices in Redshift

What are the best practices for data modeling in Amazon Redshift?

Design schemas, keys, and table properties in Amazon Redshift to maximize query speed, minimize cost, and keep maintenance simple.

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Description

Why focus on data modeling first?

Bad models slow queries and inflate storage. Up-front planning on schema layout, distribution key (DISTKEY), and sort key (SORTKEY) produces predictable performance and lower cost.

Which schema style works best—star or wide tables?

Use a star when dimension tables change rarely and fact tables grow fast. Opt for a wide, denormalized table when joins dominate workload and updates are minimal.

How do I pick a DISTSTYLE and DISTKEY?

Choose the column most used in joins—often customer_id or order_id. If the table is small (<5M rows), use DISTSTYLE ALL to replicate it across slices. For unrelated queries, choose EVEN to balance.

Checklist for DISTKEY selection

1. High cardinality
2. Even distribution
3. Frequently joined

When should I define a SORTKEY?

Create a COMPOUND SORTKEY on columns with range predicates (order_date) to enable prune-and-scan. Use INTERLEAVED only when multiple columns are equally common in filters.

Should I use compression encodings?

Always. Run ANALYZE COMPRESSION on a sample, then apply the suggested ENCODE values during CREATE TABLE to cut storage up to 70% and improve I/O.

Why isolate staging and analytics schemas?

Separate raw, staging, and analytics layers. Raw mirrors sources 1:1, staging cleanses, analytics provides curated facts and dims. This protects analysts from breaking ETL and keeps lineage clear.

How do I manage slowly changing dimensions (SCD)?

Implement SCD Type 2 with start_date, end_date, and is_current flags. Use DISTSTYLE ALL because dim tables are small and heavily joined.

How often should I VACUUM and ANALYZE?

Run VACUUM DELETE after large deletes, VACUUM SORT after heavy inserts into sorted tables, and ANALYZE after 5–10% data change to refresh statistics.

Can materialized views help?

Yes. Summarize large fact tables by day or product to speed dashboards. Refresh on schedule or manually after ETL loads.

What naming convention should I follow?

snake_case for tables and columns, prefix dims with dim_, facts with fct_, and staging with stg_. Consistent names enable fast autocomplete and easier AI copilot suggestions.

Why How to Apply Data Modeling Best Practices in Redshift is important

How to Apply Data Modeling Best Practices in Redshift Example Usage


-- Join respects distribution and sort keys
SELECT  c.name,
        o.order_date,
        o.total_amount
FROM    fct_orders o
JOIN    dim_customers c
  ON    o.customer_id = c.id
WHERE   o.order_date BETWEEN '2024-01-01' AND '2024-01-31';

How to Apply Data Modeling Best Practices in Redshift Syntax


-- Fact table with sort & dist keys
CREATE TABLE fct_orders (
  id            BIGINT       ENCODE az64,
  customer_id   BIGINT       ENCODE az64,
  order_date    DATE         ENCODE az64,
  total_amount  DECIMAL(12,2)ENCODE az64,
  created_at    TIMESTAMP    ENCODE az64
)
DISTSTYLE KEY
DISTKEY(customer_id)
SORTKEY(order_date);

-- Dimension table replicated to all nodes
CREATE TABLE dim_customers (
  id          BIGINT   ENCODE az64,
  name        VARCHAR(255) ENCODE lzo,
  email       VARCHAR(255) ENCODE lzo,
  created_at  TIMESTAMP    ENCODE az64,
  is_current  BOOLEAN      ENCODE raw
)
DISTSTYLE ALL
SORTKEY(id);

Common Mistakes

Ignoring distribution keys: Default EVEN style can trigger massive data shuffles during joins. Always set an intentional DISTKEY on high-cardinality join columns.
Using INTERLEAVED SORTKEY everywhere: It slows inserts and VACUUM. Reserve INTERLEAVED for multi-column filter workloads; default to COMPOUND for time-series data.