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:

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):

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:

import { S3Client, GetObjectCommand } from "@aws-sdk/client-s3";
import { parse } from "csv-parse/sync";

const s3 = new S3Client({ region: "us-east-1" });

async function ingestFromS3(bucket: string, key: string) {
  const response = await s3.send(new GetObjectCommand({ Bucket: bucket, Key: key }));
  const body = await response.Body?.transformToString();

  // Parse CSV
  const records = parse(body, { columns: true, skip_empty_lines: true });

  // Insert to database
  await db.insert(eventsTable).values(records);
}