airbyte
# ClaudeWave - Airbyte Skill Description This Claude Code skill provides expert guidance on Airbyte, an open-source data integration platform with 300+ pre-built connectors for syncing data from SaaS tools, databases, and APIs into data warehouses and lakes. Use this skill when setting up self-hosted or cloud Airbyte instances, configuring data sources and destinations programmatically via API, establishing incremental syncs with Change Data Capture, managing schema evolution, and building production data pipelines with error recovery capabilities.
git clone --depth 1 https://github.com/TerminalSkills/skills /tmp/airbyte && cp -r /tmp/airbyte/skills/airbyte ~/.claude/skills/airbyteSKILL.md
# Airbyte — Open-Source Data Integration Platform
You are an expert in Airbyte, the open-source data integration platform with 300+ pre-built connectors. You help developers sync data from SaaS tools, databases, and APIs into data warehouses and lakes — handling incremental syncs, CDC (Change Data Capture), schema evolution, and error recovery for production data pipelines.
## Core Capabilities
### Self-Hosted Setup
```bash
# Docker Compose (recommended for small-medium)
git clone https://github.com/airbytehq/airbyte.git
cd airbyte && ./run-ab-platform.sh
# UI at http://localhost:8000
# Kubernetes (production)
helm repo add airbyte https://airbytehq.github.io/helm-charts
helm install airbyte airbyte/airbyte -n airbyte --create-namespace
# Cloud: https://cloud.airbyte.com (managed)
```
### Configuration via API
```python
# Create connections programmatically via Airbyte API
import requests
AIRBYTE_API = "http://localhost:8000/api/v1"
# Create a Stripe source
source = requests.post(f"{AIRBYTE_API}/sources/create", json={
"workspaceId": workspace_id,
"name": "Stripe Production",
"sourceDefinitionId": "e094cb9a-26de-4645-8761-65c0c425d1de", # Stripe
"connectionConfiguration": {
"account_id": "acct_xxx",
"client_secret": os.environ["STRIPE_SECRET_KEY"],
"start_date": "2025-01-01T00:00:00Z",
},
}).json()
# Create a BigQuery destination
destination = requests.post(f"{AIRBYTE_API}/destinations/create", json={
"workspaceId": workspace_id,
"name": "BigQuery Warehouse",
"destinationDefinitionId": "22f6c74f-5699-40ff-833c-4a879ea40133",
"connectionConfiguration": {
"project_id": "my-project",
"dataset_id": "raw_stripe",
"credentials_json": os.environ["GCP_CREDENTIALS"],
"loading_method": {"method": "GCS Staging", "gcs_bucket_name": "airbyte-staging"},
},
}).json()
# Create connection (source → destination)
connection = requests.post(f"{AIRBYTE_API}/connections/create", json={
"sourceId": source["sourceId"],
"destinationId": destination["destinationId"],
"syncCatalog": {
"streams": [
{
"stream": {"name": "subscriptions", "namespace": "stripe"},
"config": {
"syncMode": "incremental",
"destinationSyncMode": "append_dedup",
"cursorField": ["created"],
"primaryKey": [["id"]],
},
},
],
},
"schedule": {"scheduleType": "cron", "cronExpression": "0 */2 * * * ?"},
"namespaceFormat": "raw_${SOURCE_NAMESPACE}",
}).json()
```
### Custom Connectors (CDK)
```python
# Build a custom source connector with Airbyte CDK
from airbyte_cdk.sources import AbstractSource
from airbyte_cdk.sources.streams import Stream
from airbyte_cdk.sources.streams.http import HttpStream
class InternalAPIStream(HttpStream):
url_base = "https://api.internal.company.com/v1/"
primary_key = "id"
cursor_field = "updated_at"
def path(self, **kwargs) -> str:
return "events"
def parse_response(self, response, **kwargs):
for record in response.json()["data"]:
yield record
class Source(AbstractSource):
def check_connection(self, logger, config):
# Verify API credentials work
return True, None
def streams(self, config):
return [InternalAPIStream(authenticator=self.get_auth(config))]
```
## Installation
```bash
# Docker Compose
curl -o docker-compose.yaml https://raw.githubusercontent.com/airbytehq/airbyte/master/docker-compose.yaml
docker compose up -d
# Python CDK for custom connectors
pip install airbyte-cdk
```
## Best Practices
1. **Incremental syncs** — Use incremental mode for large tables; full refresh only for small reference tables
2. **CDC for databases** — Use Change Data Capture (logical replication) for real-time PostgreSQL/MySQL syncs
3. **Staging area** — Configure GCS/S3 staging for BigQuery/Snowflake destinations; direct insert is slow for large volumes
4. **Schema evolution** — Airbyte handles new columns automatically; configure `auto_propagation` in connection settings
5. **Alerting** — Set up webhook notifications for sync failures; integrate with Slack/PagerDuty
6. **Namespace per source** — Use `raw_${SOURCE}` namespace pattern; keeps raw data organized before dbt transforms
7. **Self-host for cost** — Airbyte Cloud charges per row synced; self-hosting is free for unlimited data
8. **Custom connectors** — Use CDK for internal APIs; publish to Airbyte's connector marketplace for community use>-
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When the user wants to plan, design, or implement an A/B test or experiment. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," or "hypothesis." For tracking implementation, see analytics-tracking.
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