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Migrating To Amazon Redshift

AWSOfficialPublished by AWS

  • Skill
  • Workflow
  • Cloud and DevOps
  • Apache-2.0
  • Low risk

From the author

Guides an end-to-end data-warehouse migration to Amazon Redshift — discovery, schema/SQL/stored-procedure/macro/script conversion, data migration, validation, performance comparison, and reporting. Source-routed via `references/<source>/`; Teradata (Vantage) is the supported source; additional sources are added as their own `references/<source>/` sets. Text-only knowledge (no executable code) — the AI generates all execution at runtime. Applies when a user wants to migrate Teradata to Amazon Redshift, convert Teradata DDL/SQL/stored procedures/macros/BTEQ to Redshift/RSQL, or assess Teradata-to-Redshift migration complexity. Applies only to migrations targeting Amazon Redshift; migrations to other platforms (Snowflake, BigQuery, Databricks, etc.) are out of scope regardless of source. Does not cover general Redshift administration, performance tuning, or troubleshooting of existing Redshift clusters (no migration involved), or sources not listed under references/.
— the description by AWS

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides a structured, text-based framework for migrating Teradata Vantage environments to Amazon Redshift. It covers the entire migration lifecycle, including discovery, schema and SQL conversion, data migration, validation, and performance reporting. The skill acts as an expert guide, instructing the AI to generate execution scripts at runtime rather than providing pre-packaged code. It emphasizes secure, read-only discovery, best practices for Redshift architecture, and the conversion of legacy Teradata constructs like BTEQ, macros, and stored procedures into Redshift-compatible formats.

What it does

  • Inventories Teradata source systems using read-only discovery queries.
  • Converts Teradata DDL, SQL, stored procedures, and BTEQ scripts to Redshift/RSQL.
  • Maps Teradata physical architecture to Redshift distribution and sort keys.
  • Generates migration runners for data extraction, S3 staging, and COPY operations.
  • Provides validation patterns for verifying data integrity and row counts.
  • Assesses migration complexity and generates performance comparison reports.

When to use it

  • When planning or executing a migration from Teradata Vantage to Amazon Redshift.
  • When you need to convert legacy Teradata DDL or BTEQ scripts to Redshift syntax.
  • When assessing the effort and complexity of a Teradata-to-Redshift migration project.
  • When you require a structured, repeatable process for data warehouse migration.

Advantages

  • Knowledge-based approach avoids dependency drift and vendor lock-in.
  • Promotes secure, least-privilege practices for data extraction and migration.
  • Provides clear mapping rules for complex Teradata-to-Redshift architectural differences.
  • Supports both connected and disconnected migration environments.
  • Enables reproducible results through a standardized project-workspace layout.

Usage examples

“Assess the complexity of migrating my Teradata schema to Redshift.”
The agent will guide you through the discovery phase, inventorying your Teradata objects and identifying high-complexity constructs that require manual review.
“Convert this Teradata BTEQ script to RSQL for Redshift.”
The agent will apply the BTEQ-to-RSQL conversion rules, replacing .IMPORT/.EXPORT with S3-based COPY/UNLOAD commands and adapting transaction logic.
“How should I choose distribution keys for my Teradata fact tables in Redshift?”
The agent will analyze your table sizes and join patterns to recommend appropriate DISTKEY or DISTSTYLE settings based on the architecture-mapping reference.
“Generate a script to validate row counts between Teradata and Redshift.”
The agent will produce a validation runner script that executes count queries on both systems and generates a comparison report.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/aws/migrating-to-amazon-redshift/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip migrating-to-amazon-redshift.zip -d .agents/skills

Documentation by AWS

Migrating to Amazon Redshift

What this skill is

This skill is AI guidance, not an execution framework. It is entirely Markdown knowledge (rules, mappings, patterns, best practices) — no executable code. All execution — conversion, the discovery/migration/validation runners, dependencies, and infrastructure — you (the AI) generate at runtime from this knowledge, tailored to the customer's environment.

Principle: knowledge over shipped code → less drift, nothing for the customer to run or depend on, reliable first-time results. Do not look for a pyproject, a tools package, an orchestrator engine, or shipped scripts — there are none by design; you generate execution.

Runtime: this skill works with or without the AWS MCP server — step guidance uses AWS CLI syntax. Running it with the AWS MCP server is recommended for sandboxed execution and audit logging; without it, the AI runs the generated scripts on the host shell (assumes Bash, Python 3, and AWS CLI + credentials). Do not assume MCP-only tools are available.

Source routing

This skill migrates a supported source data warehouse to Amazon Redshift. First identify the source system, then load that source's knowledge under references/<source>/:

  • Teradata (Vantage) → references/teradata/ — supported (all references below).

  • Other sources (e.g. Snowflake, Oracle) — unsupported; each is added as its own references/<source>/ set when ready.

The workflow is source-agnostic (discovery → convert → migrate → validate → performance → report); only the conversion knowledge is source-specific. Everything below is the Teradata set.

When to use

  • Migrating a Teradata system (Vantage) to Amazon Redshift.

  • Converting Teradata DDL, SQL, stored procedures, macros, or BTEQ to Redshift/RSQL.

  • Assessing Teradata→Redshift migration complexity/effort.

Operating principles

  • Discovery is strictly read-only (SELECT-only) on the source. Never change production state: no DDL/DML, and never enable logging (BEGIN/REPLACE QUERY LOGGING). If DBQL is empty, mark it unavailable and fall back to always-on DBC.AMPUsageV — see references/teradata/discovery-queries.md.

  • Skill provides knowledge; you generate execution. Read the references/ to reason and convert — apply the rules in references/teradata/conversion-rules.md directly for conversion, and generate the discovery/migration/validation runners (and the read-only discovery collector from references/teradata/discovery-queries.md) tailored to the environment.

  • Generate, don't assume a framework. Assume the environment has Bash, Python 3, and AWS CLI + credentials. Any Python lib a generated script needs (teradatasql, boto3, …) is pip install-ed on demand by that script / its run-instructions — pin exact versions. Teradata TTU (BTEQ/TPT) is Linux/Windows-only — not macOS; prefer WRITE_NOS + teradatasql (cross-platform, no client) for discovery/extract unless a TTU/Linux host exists.

  • Credentials: use a read-only Teradata user; prefer IAM roles over IAM users. For production, reference credentials from AWS Secrets Manager or Systems Manager Parameter Store. For local development only, a git-ignored .env file or profile may be used — never commit it. Never hard-code or echo secrets. In a portable bundle, reference a co-located credentials file and ship a credentials.env.example template — the real file is git-ignored.

  • Persist state in files. All generated output goes under a git-ignored output/ in the user's working dir; keep output/state.md current so work is resumable.

Workflow (phases)

Run in order; each phase's result/ feeds the next (see references/teradata/orchestration.md).

  1. Discovery — inventory the source. → references/teradata/discovery-queries.md (read-only collection SQL + BTEQ driver template the AI generates) → output/discovery/result/inventory.json

  2. Conversion — schema + code. Apply the conversion rules directly, flag the manual-rewrite long tail, and fix Redshift errors from the references. → references/teradata/conversion-rules.md, references/teradata/data-type-mapping.md, references/teradata/architecture-mapping.md, references/teradata/stored-procedure-migration.md, references/teradata/bteq-to-rsql.md, references/teradata/common-errors.md

  3. Data migration — extract → S3 → COPY, restartable. → references/teradata/data-migration-patterns.md

  4. Validation — counts/aggregates/sampling. → references/teradata/validation-patterns.md

  5. Performance — baseline vs Redshift; size the target. → references/teradata/performance.md, references/teradata/sizing.md

  6. Reporting — aggregate all phases. → references/teradata/reporting.md

Conversion (how the AI applies it)

There is no converter to run — convert by applying the rules in references/teradata/conversion-rules.md directly (with the type / architecture / stored-procedure / BTEQ references): apply the deterministic rules to the well-understood bulk, flag the manual-rewrite constructs with their suggested rewrites, assign a confidence per object, and fix any Redshift errors using references/teradata/common-errors.md. The reference docs are the single source of truth; conversion-rules.md includes golden input→output examples to match.

Execution modes (connectivity)

  • Connected — your host can reach Teradata/Redshift → run the generated scripts in place.

  • Disconnected — it can't → generate a self-contained bundle under output/<phase>/ (script + co-located credentials template + relative result/ + run-instructions.md); the operator runs it on a reachable host and copies result/ back. The copied-back result/ is the durable state — read it (+ state.md) and continue.

Project-workspace layout (per migration run)

<project-workspace>/
  migration-config.yaml          # operator-authored: endpoints, scope, strategy
  .gitignore                     # ignores output/
  output/                        # everything generated (git-ignored)
    state.md                     # progress cursor
    discovery/   …  result/inventory.json
    conversion/  …  result/{ddl,sql,procedures,rsql}/  manual_review.json
    data_migration/ … result/{extract,load,templates}/  migration_manifest.json
    validation/  …  result/validation_report.json
    performance/ …  result/{perf_baseline,perf_compare}.json
    reporting/      result/migration_report.md

Security considerations

  • No shipped code or dependencies. This skill is text-only — the customer runs nothing from it. Any runner the AI generates MUST pin exact dependency versions, validate/sanitize inputs (file paths, SQL, shell args), and never print or log credentials, secrets, or PII.

  • Least privilege + ephemeral credentials. Use a read-only Teradata user for discovery. On AWS prefer IAM roles over IAM users and IAM auth over username/password. Keep secrets in AWS Secrets Manager / Parameter Store — never hard-code, echo, or commit them (credentials files are git-ignored; ship only *.example templates).

  • Data in transit / at rest. Use TLS to both engines; stage extracts in an encrypted S3 bucket (SSE) with a least-privilege bucket policy; load via COPY … IAM_ROLE (not access keys). Enable encryption on the target Redshift cluster.

  • Blast radius. Discovery is read-only by design. Migration writes to the target — validate against a throwaway / non-production Redshift first, and never point a generated write-path at production without explicit operator confirmation.

  • No secret leakage in artifacts. Generated output/… (manifests, reports, state.md) MUST NOT embed credentials or endpoints beyond what the operator supplies in migration-config.yaml.

  • COPY IAM_ROLE hardening. Scope the role's policy to the specific staging prefix (not bucket-wide s3:*), and include condition keys in its trust policy (aws:SourceAccount / aws:SourceArn, or sts:ExternalId for cross-account) to prevent confused-deputy assumption — per Redshift IAM-role authorization best practices.

  • Logging & monitoring. Enable CloudTrail (S3 data events on the staging bucket + Redshift management events), Redshift audit logging (connection/user-activity logs to S3 or CloudWatch), and CloudWatch alarms on COPY failures or unusual staging-bucket access during the migration.

The AWS MCP server (recommended runtime) additionally provides sandboxed execution and audit logging for the generated scripts.

References (specialized knowledge)

FileTopic
references/teradata/orchestration.mdphase workflow + state model
references/teradata/conversion-rules.mdthe 72 conversion rules (source of truth)
references/teradata/data-type-mapping.mdTD→RS type mapping
references/teradata/architecture-mapping.mdPI→DISTKEY, PPI→SORTKEY, Join Index→MV
references/teradata/stored-procedure-migration.mdSP → PL/pgSQL
references/teradata/bteq-to-rsql.mdBTEQ → RSQL
references/teradata/common-errors.mdcommon Redshift errors + fixes
references/teradata/discovery-queries.mdDBC system-view inventory queries
references/teradata/data-migration-patterns.mdCOPY/TPT/micro-batch/checkpoint
references/teradata/validation-patterns.mdrow-count/aggregate/sample compare
references/teradata/performance.mdrepresentative-query extraction + compare
references/teradata/sizing.mdRG node type + count from the source profile
references/teradata/reporting.mdmigration status-report generation

Files

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Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs an MCP server, Needs Python, Needs Node.js

Security

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Attribution and licence

Published by
AWS
Publisher
AWS (Official)
Source
aws/agent-toolkit-for-aws · skills/specialized-skills/analytics-skills/migrating-to-amazon-redshift · commit 0a218f8
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Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • NOTICE (added by AutoGineer from NOTICE)
  • ATTRIBUTION.md (written by AutoGineer)
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