ingesting-data

# Data Ingestion Patterns

This skill provides patterns for getting data INTO systems from external sources.

## When to Use This Skill

- Importing CSV, JSON, Parquet, or Excel files
- Loading data from S3, GCS, or Azure Blob storage
- Consuming REST/GraphQL API feeds
- Building ETL/ELT pipelines
- Database migration and CDC (Change Data Capture)
- Streaming data ingestion from Kafka/Kinesis

## Ingestion Pattern Decision Tree

```
What is your data source?
├── Cloud Storage (S3, GCS, Azure) → See cloud-storage.md
├── Files (CSV, JSON, Parquet) → See file-formats.md
├── REST/GraphQL APIs → See api-feeds.md
├── Streaming (Kafka, Kinesis) → See streaming-sources.md
├── Legacy Database → See database-migration.md
└── Need full ETL framework → See etl-tools.md
```

## Quick Start by Language

### Python (Recommended for ETL)

**dlt (data load tool) - Modern Python ETL:**
```python
import dlt

# Define a source
@dlt.source
def github_source(repo: str):
    @dlt.resource(write_disposition="merge", primary_key="id")
    def issues():
        response = requests.get(f"https://api.github.com/repos/{repo}/issues")
        yield response.json()
    return issues

# Load to destination
pipeline = dlt.pipeline(
    pipeline_name="github_issues",
    destination="postgres",  # or duckdb, bigquery, snowflake
    dataset_name="github_data"
)

load_info = pipeline.run(github_source("owner/repo"))
print(load_info)
```

**Polars for file processing (faster than pandas):**
```python
import polars as pl

# Read CSV with schema inference
df = pl.read_csv("data.csv")

# Read Parquet (columnar, efficient)
df = pl.read_parquet("s3://bucket/data.parquet")

# Read JSON lines
df = pl.read_ndjson("events.jsonl")

# Write to database
df.write_database(
    table_name="events",
    connection="postgresql://user:pass@localhost/db",
    if_table_exists="append"
)
```

### TypeScript/Node.js

**S3 ingestion:**
```typescript
import { S3Client, GetObjectCommand } from "@aws-sdk/client-s3";
import { p