Migrating To Amazon Redshift

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.