---
name: data-source-integration-evaluator
description: "Evaluates a new data source and recommends a complete integration approach — covering source classification, connectivity method, extraction pattern, schema and change-data-capture strategy, incremental loading, error handling, monitoring, and a phased implementation plan. Use when deciding how to ingest a new API, database, SaaS platform, file drop, or event stream into a data pipeline."
argument-hint: "[source-type] [destination]"
allowed-tools: ["Read"]
---

# Data Source Integration Evaluator

## Purpose
Assess any new data source and produce a concrete integration recommendation — extraction method, load pattern, change-capture strategy, error handling, and a phased build plan — so the engineer can move from "we need to ingest this" to a defensible implementation decision without reinventing the evaluation each time.

## When to Use
Invoke this skill when the user asks to:
- Decide how to ingest a new API, database, SaaS platform, file source, or event stream
- Compare batch vs. streaming vs. CDC for a given source
- Evaluate whether to build a custom connector or use an off-the-shelf tool (Fivetran, Airbyte, Debezium, etc.)
- Design an incremental extraction strategy for a source with rate limits or large tables
- Plan schema handling and drift management for a new integration
- Estimate effort and risk before committing to a source integration

## Inputs Required
Before starting, collect:
- **Source type**: REST/GraphQL API, relational DB, NoSQL DB, SaaS app, flat files (S3/SFTP), or event stream (Kafka/Kinesis/PubSub)
- **Access method available**: API key/OAuth, DB credentials + network path, webhook, log-based CDC access, file drop location
- **Data volume & velocity**: Approximate row/record count, daily change volume, and how fresh the data must be (real-time, hourly, daily)
- **Destination**: Warehouse/lake target (Snowflake, BigQuery, Redshift, Databricks, Postgres, etc.)
- **Constraints**: Rate limits, API pagination limits, maintenance windows, PII/compliance requirements
- **Existing tooling**: Orchestrator (Airflow, Dagster, dbt), any ingestion platform already in use
- **Team capacity**: Rough sense of build-vs-buy appetite and who maintains it

If volume/velocity or freshness requirements are unknown, assume daily batch as the default and flag the assumption explicitly.

## Instructions

### Step 1: Classify the Source
Identify the source archetype, because it dictates the entire integration pattern:
- **Pull API (REST/GraphQL)**: Rate-limited, paginated, often no native change feed
- **Relational database**: Supports incremental keys and/or log-based CDC
- **NoSQL / document store**: Schema-flexible; extraction depends on export API or change streams
- **SaaS application**: Often best served by a managed connector; APIs may be undocumented or unstable
- **Flat files (S3/SFTP)**: Batch by nature; watch for late-arriving and out-of-order files
- **Event stream (Kafka/Kinesis/PubSub)**: Streaming-native; ordering, offsets, and replay are the concerns

State which archetype this source is and the one property of that archetype that most constrains the design.

### Step 2: Determine Connectivity & Auth
Assess how you actually reach the data:
- **Auth model**: API key, OAuth2 (token refresh handling), service account, DB user with least-privilege read
- **Network path**: Public endpoint, VPC peering, PrivateLink, SSH tunnel, static IP allowlisting
- **Reliability of interface**: Documented + versioned API vs. unversioned; stable schema vs. frequently changing
- Flag any auth pattern that requires token rotation or credential vaulting (Secrets Manager, Vault)

### Step 3: Recommend Extraction Pattern
Choose and justify one primary pattern:
- **Full refresh**: Small tables (<1–5M rows), no reliable change key — simplest, most robust
- **Incremental by high-water mark**: `updated_at` / monotonic ID exists and is trustworthy — most common
- **Log-based CDC**: DB with WAL/binlog access, high change volume, low-latency need — use Debezium or native connector
- **Event consumption**: Source already emits events — consume with consumer groups and offset management
- **Change API / cursor**: SaaS with a native "changes since cursor" endpoint

For the chosen pattern, specify the state to persist (cursor value, offset, LSN, last file timestamp) and where it lives.

### Step 4: Decide Build vs. Buy
Score against three questions:
1. Does a mature managed connector exist (Fivetran/Airbyte/Stitch) for this exact source?
2. Is the data volume/rate within the connector's cost tolerance?
3. Do compliance or transformation needs require custom control?

**Rule of thumb**: Standard SaaS + common DB → managed connector unless cost or compliance forces custom. Bespoke internal API or event stream → custom is usually warranted.
State the recommendation and the single deciding factor.

### Step 5: Schema & Drift Strategy
- **Schema handling**: Explicit typed schema vs. schema-on-read (JSON/variant column)
- **Drift policy**: Auto-add new columns, alert-on-drop, fail-on-incompatible-change — pick one and justify
- **PII/compliance**: Fields requiring masking, hashing, or exclusion at ingest
- **Contracts**: Whether a data contract / schema registry (e.g., Avro + registry for streams) applies

### Step 6: Error Handling & Idempotency
- **Retries**: Exponential backoff with jitter for transient/429 errors; cap and dead-letter after N attempts
- **Idempotency**: Deduplication key and merge/upsert strategy so re-runs don't duplicate data
- **Partial-failure recovery**: How to resume from the last committed cursor/offset without gaps or double-counting
- **Late/out-of-order data**: Watermarking or reprocessing window for streams and file sources

### Step 7: Monitoring & SLAs
Define what to watch:
- **Freshness**: Time since last successful load vs. the required freshness SLA
- **Volume anomaly**: Row-count deltas outside expected range trigger alerts
- **Schema drift alerts**: Fired on unexpected structural changes
- **Failure alerting**: Retry exhaustion and dead-letter growth
State the specific metric and threshold for each, not just "add monitoring."

### Step 8: Phased Implementation Plan
Produce a 3-phase rollout:
- **Phase 1 — Proof of connection**: Auth, single successful pull, schema inspection
- **Phase 2 — Production extraction**: Chosen pattern, incremental state, error handling, load to destination
- **Phase 3 — Hardening**: Monitoring, drift policy, alerting, documentation and runbook
Include a rough effort estimate (S/M/L) per phase and the top risk for each.

## Output Format
- **Structure**: 8 labeled sections following the steps, plus a one-line recommendation summary at the top
- **Length**: 900–1,300 words
- **Format**: Structured document — short prose per section with bulleted decisions; extraction-pattern and build-vs-buy rendered as clear recommendations with a stated deciding factor
- **Sections**: Recommendation Summary → Source Classification → Connectivity & Auth → Extraction Pattern → Build vs. Buy → Schema & Drift → Error Handling & Idempotency → Monitoring & SLAs → Phased Plan
- **Tone**: Senior data engineer in a design review — decisive, tradeoff-aware, no hedging

## Quality Checklist
Before delivering output, verify:
- [ ] The source is classified into exactly one archetype with its key constraint named
- [ ] A single primary extraction pattern is recommended, with the persisted state artifact specified (cursor/offset/LSN/timestamp)
- [ ] Build-vs-buy gives a clear verdict and names the one deciding factor
- [ ] Error handling addresses idempotency with a stated dedup/merge key — not just "add retries"
- [ ] Each monitoring item has a concrete metric and threshold
- [ ] The phased plan has 3 phases, each with an effort estimate (S/M/L) and a named top risk

## Notes
- The most common failure mode is trusting an `updated_at` column that isn't reliably updated on every mutation — verify before committing to incremental-by-timestamp.
- For SaaS sources, a managed connector is usually cheaper than the true fully-loaded cost of maintaining a custom one; only override this for compliance, cost-at-scale, or unsupported-source reasons.
- Log-based CDC is powerful but adds operational surface area (connector, offsets, schema history) — recommend it only when change volume or latency genuinely requires it.

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## How to use this skill

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