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Amazon Redshift Guide

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Navigate Redshift SQL with confidence and accuracy.

by aws2.3k stars on aws/agent-toolkit-for-aws
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Updated Aug 10, 2026
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What Amazon Redshift Guide does

The Amazon Redshift Guide skill is designed for developers and data analysts who work with Amazon Redshift and need to navigate its unique SQL syntax and operational patterns. Unlike PostgreSQL, Redshift has specific behaviors and limitations that can lead to confusion, especially for those accustomed to PostgreSQL's features. This skill corrects common misconceptions by providing guidance tailored specifically for Redshift, ensuring that users avoid pitfalls associated with PostgreSQL-derived assumptions.

This skill covers a wide range of topics essential for effective Redshift usage, including SQL commands, data loading and unloading practices, system views, metadata discovery, and operational patterns. It emphasizes the importance of using Redshift-specific commands and functions, such as COPY, UNLOAD, and MERGE, while also highlighting differences in data types and system views. The included references serve as a comprehensive resource for users to quickly find the information they need, whether they're debugging queries or optimizing performance.

The skill is particularly useful when working in environments where Redshift's capabilities differ between provisioned clusters and serverless workgroups. By establishing the type of environment first, users can receive accurate guidance tailored to their specific setup. The skill also includes safety guardrails to prevent potentially destructive operations, ensuring users can work with confidence.

Overall, the Amazon Redshift Guide skill is a valuable resource for anyone looking to deepen their understanding of Redshift's unique features and best practices, making it an essential tool for developers and data analysts alike.

When to use it

Use this skill when you need accurate guidance on Redshift SQL commands, data loading, and operational patterns specific to Redshift.

When not to use it

This skill is not suitable for tasks involving other AWS services like S3 or Athena, or for users looking for PostgreSQL-specific guidance.

What you can build with it

Debugging Redshift Queries

When encountering errors in Redshift queries, use the skill to identify correct syntax and operational patterns specific to Redshift.

Optimizing Data Loading

Utilize the skill to learn best practices for loading data into Redshift using the COPY command and managing IAM roles.

Understanding System Views

Refer to the skill to clarify the differences between Redshift system views and those in PostgreSQL, ensuring accurate metadata discovery.

How to install Amazon Redshift Guide

View source

1. Install with the skills CLI

npx skills add aws/agent-toolkit-for-aws/redshift-guide --agent claude-code

2. Or install it manually

Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.

Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs

Inside SKILL.md

Written by aws

Amazon Redshift Guide

Redshift is NOT PostgreSQL (read first)

Redshift speaks PostgreSQL's wire protocol and shares much of its surface syntax, so LLMs assume PostgreSQL behavior carries over — it frequently does not. Divergences span system tables (pg_catalog is incomplete), DDL (no indexes, no sequences), functions (string_agg, SUBSTR on tables, leader-node-only functions), types (a text column becomes VARCHAR(256)), and comparison semantics (trailing blanks, unenforced constraints). Assume divergence and verify against the reference below — do not answer from PostgreSQL habit. Common PostgreSQL→Redshift divergences are in references/redshift-sql-syntax.md.

Works best with the AWS MCP server — it runs the AWS CLI and Redshift Data API calls below in a sandboxed, audit-logged environment. All guidance here is plain AWS CLI and SQL and works without it.

STEP 0: Serverless or Provisioned?

Establish this before answering — APIs, system tables, and capabilities differ. Take it from the question when it says which one; ask when it does not. SELECT version() does not identify it.

  • Serverless — identified by a workgroup (and namespace). Data API calls take --workgroup-name; the user says "workgroup"/"Serverless".
  • Provisioned — identified by a cluster. Data API calls take --cluster-identifier; the user says "cluster".
TargetSystem ViewsCredentials API
ProvisionedSYS_, all SVV_ + STL_, STV_, SVL_, SVCS_ (single-AZ only — disabled on Multi-AZ)redshift:GetClusterCredentials
ServerlessSYS_ + a subset of SVV_ ONLY (no STL/STV/SVL/SVCS)redshift-serverless:GetCredentials

Critical Facts

  • SHOW commands are the primary metadata interface — SHOW DATABASES, SHOW SCHEMAS, SHOW TABLES, SHOW COLUMNS, SHOW TABLE, SHOW VIEW. Do NOT default to pg_catalog or information_schema. → Load references/redshift-sql-metadata.md for metadata/discovery questions and any "relation does not exist" report — it has the diagnostic flow.
  • SYS_ views are the preferred system views — they work everywhere. STL_, STV_, SVL_, and SVCS_ are provisioned single-AZ only, and some SVV_ views are unsupported on Serverless. → Load references/redshift-sql-metadata.md for any system-view or monitoring question.
  • sys_load_error_detail for COPY debugging (not stl_load_errors, which is provisioned single-AZ only).
  • DATEADD/DATEDIFF — unit-first argument order: DATEADD(day, -30, GETDATE()), DATEDIFF(day, start, end).
  • APPROXIMATE COUNT(DISTINCT col) — Redshift-specific, ~2% error, much faster than exact COUNT(DISTINCT) on large datasets.
  • MERGE ... REMOVE DUPLICATES — simplified dedup when source and target have identical schemas.
  • COPY should use IAM_ROLE (the namespace role, not the caller role) + supports MANIFEST for explicit file lists + MAXERROR for error tolerance.
  • SUBSTR() is leader-node-only — works on literals but errors on table columns (SUBSTR() function is not supported (Hint: use SUBSTRING instead)). Use SUBSTRING() on columns.
  • UNIQUE / PRIMARY KEY / FOREIGN KEY are informational only — NOT enforced (duplicate rows are accepted with no error). Optimizer hints; enforce integrity in the application or via MERGE. NOT NULL IS enforced.
  • SHOW VIEW <schema.name> returns the definition of a regular view, materialized view, or late-binding view. MV freshness: SVV_MV_INFO (is_stale).
  • TOP N and LIMIT N both work (TOP N PERCENT does not). A text column becomes VARCHAR(256) — use VARCHAR(max) or explicit length.
  • Iceberg tables use CREATE TABLE ... USING ICEBERG (not STORED AS ICEBERG, not TABLE_FORMAT=ICEBERG).
  • Datashares support read and write operations — consumers can write once the producer grants write privileges. Treat "permission denied" on a datashare write as a missing grant, not an unsupported operation. → Load references/redshift-sql-metadata.md for requirements and limits.

Safety Guardrails

BLOCK: DROP DATABASE, DELETE without WHERE, publicly-accessible=true, GRANT ALL ON ALL WARN then confirm: RESIZE, RESTORE, VACUUM on large tables, ALTER PASSWORD, WLM config change Confirm: CREATE, GRANT specific, COPY, UNLOAD

Security Considerations

Apply these defaults when generating anything that connects, loads, or exports. Details are in the reference files noted.

  • In transit: the Data API is HTTPS-only. For JDBC/ODBC set the require_ssl parameter and connect with sslmode=verify-full so the server certificate is checked.
  • At rest: keep cluster/namespace encryption enabled, and add ENCRYPTED KMS_KEY_ID '<arn>' to UNLOAD — it writes query results to S3, outside Redshift's own encryption. → references/redshift-sql-ddl-copy.md
  • Credentials: prefer SecretArn (Secrets Manager) or IAM Identity Center; DbUser is acceptable because it issues temporary credentials. Never place database passwords in code, environment variables, or SQL text. → references/redshift-sql-recipes-load-api.md
  • Least privilege: scope the namespace IAM_ROLE to the specific bucket and prefix (s3:GetObject on arn:aws:s3:::<bucket>/<prefix>/*), not s3:* or a managed full-access policy, and condition its trust policy on both aws:SourceArn (the cluster/namespace ARN) and aws:SourceAccountSourceArn alone still allows another resource in the account to assume it. Grant per-object privileges rather than GRANT ALL ON ALL.
  • Audit: CloudTrail records redshift-data:* API calls but not the SQL executed; enable Redshift audit logging (useractivitylog, connectionlog, userlog) for that. Both capture query text and user activity, so encrypt every destination in use: the CloudWatch Logs group (aws logs associate-kms-key), the CloudTrail trail (SSE-KMS), and the audit-log S3 bucket (SSE-S3 — audit logging to S3 supports only S3-managed keys, not KMS). Serverless only supports sending audit logs to CloudWatch.
  • Network: keep PubliclyAccessible=false and connect over a VPC endpoint. Do not open port 5439 to 0.0.0.0/0 or ::/0 — scope inbound rules to specific CIDRs or to a referencing security group.
  • Sensitive data: Data API results persist for 24h and sys_load_error_detail can echo fragments of rejected rows, so treat statement IDs and load-error output as sensitive.
  • Further reading: Security in Amazon Redshift for the full guidance behind these defaults.

Routing Table

MANDATORY: When a question matches a row below, you MUST load and read the referenced file BEFORE answering.

Ask whether the target is provisioned or Serverless before giving troubleshooting steps — unless the question already says which one, in which case use that and do not re-confirm.

User IntentRoute To
"CREATE TABLE", "DISTKEY/SORTKEY", "ENCODE", "IDENTITY", "COPY", "UNLOAD", "IAM_ROLE", "Iceberg table"references/redshift-sql-ddl-copy.md
"LISTAGG", "DATEADD/DATEDIFF", "NVL/DECODE", "type mapping", "text type", "VARBYTE", "recursive CTE"references/redshift-sql-functions-types.md
"QUALIFY", "PIVOT/UNPIVOT", "MERGE", "TOP N", "SUBSTR error", "UNIQUE/PK not enforced", "trailing blanks", "leader-node function", "JSON", "SUPER", "PartiQL", "nested/semi-structured data"references/redshift-sql-extensions-semantics.md
"system view", "SVV_/SYS_", "SHOW commands", "STL vs SYS", "list tables", "distkey/sortkey lookup", "datashare discovery", "2-part vs 3-part", "permission denied", "GRANT", "privileges", "relation/table does not exist"references/redshift-sql-metadata.md
"how do I write SQL", "PostgreSQL vs Redshift", "which SQL reference", general dialect questionreferences/redshift-sql-syntax.md (index of the 6 SQL references + PostgreSQL-vs-Redshift failure table)
"COPY failed", "load error", "Data API poll", "async query", "Data API throttle"references/redshift-sql-recipes-load-api.md
"materialized view", "MV refresh", "AUTO REFRESH", "stale view"references/redshift-sql-materialized-views.md
General Redshift question not matching aboveAnswer directly from general knowledge
Aurora, RDS, DynamoDB, Athena (non-Redshift)REFUSE. State this skill is for Amazon Redshift only. Do not provide guidance for other database services.

Data API Quick Reference

Load references/redshift-sql-recipes-load-api.md before answering ANY Data API, COPY-error, or async-query question. It carries the bounded poll loop, the HasResultSet and ResourceNotFoundException handling, the per-target parameters, and the auth options.

Data API calls are async by default — use long polling (--wait-time-seconds, 1–30) rather than blind sleeps, and keep a bounded loop for work that can exceed 30s. Serverless takes --workgroup-name, provisioned takes --cluster-identifier.

Frequently asked questions about Amazon Redshift Guide

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