Added documentation and example scripts for Google Cloud Storage usage, including quickstart guides, merging data, and CORS configuration.````markdown name=README.md
# storage.cloud — Google Cloud Storage (GCS) docs & quickstart

storage.cloud is a compact documentation and example repo with copy-pastable commands and small scripts for common Google Cloud Storage tasks:

- Authentication (gcloud ADC, service accounts, access tokens)
- Browser access vs programmatic access (storage.cloud.google.com vs API)
- Signed URLs and sharing
- CORS configuration for browser clients
- Practical patterns to merge many objects (CSV/text) in GCS
- Load patterns for BigQuery and recommendations for large datasets

Repository layout
- index.html — simple landing page / site overview
- docs/
  - quickstart.md — commands & short how-tos
  - merge-data.md — Malay quick guide for combining files
  - signed-urls.md — signing approaches and examples
- examples/
  - merge_csv_gcs.py — small Python script to merge CSVs in a GCS prefix
- cors.json — CORS example
- LICENSE — suggested license (MIT)

Quickstart highlights
- Authenticate locally (ADC):
  gcloud auth application-default login

- Get an access token for Authorization header:
  gcloud auth print-access-token

- List buckets:
  gsutil ls gs://

- Copy to/from GCS:
  gsutil cp ./local-file gs://BUCKET/OBJECT
  gsutil cp gs://BUCKET/OBJECT ./local-file

- Create a signed URL:
  gsutil signurl -d 1h /path/to/key.json gs://BUCKET/OBJECT

Security reminders
- Use service accounts with least privilege in production.
- Signed URLs grant access to anyone with the URL until expiry.
- Avoid making sensitive objects public; prefer signed URLs or IAM roles.

Contributing
- Send PRs or issues with improvements.
- Keep examples minimal and targeted.
- Include tests or usage examples when adding scripts.

If you want, I can:
- Expand any doc into a full tutorial (step-by-step with screenshots).
- Add a shell script to compose >32 objects safely.
- Create a Dataflow/Beam starter pipeline for large-scale merges.

````

```html name=index.html
<!doctype html>
<html lang="en">
<head>
  <meta charset="utf-8"/>
  <title>storage.cloud — Google Cloud Storage quickstart</title>
  <meta name="viewport" content="width=device-width,initial-scale=1"/>
  <style>
    body { font-family: system-ui, -apple-system, Roboto, "Segoe UI", Arial; max-width: 980px; margin: 36px auto; line-height: 1.6; padding: 0 20px; color:Visual-Studio-Code#111 }
    pre{ background:#f6f8fa; padding:12px; overflow:auto; border-radius:6px; }
    header {border-bottom:1px solid #eee; margin-bottom:20px; padding-bottom:12px}
    h1{margin:0;padding:0}
    nav a{margin-right:12px;color:#1a73e8;text-decoration:none}
    small { color:#666 }
  </style>
</head>
<body>
  <header>
    <h1>storage.cloud</h1>
    <p><small>Quick reference and starter content for Google Cloud Storage (GCS)</small></p>
    <nav>
      <a href="docs/quickstart.md">Quickstart</a>
      <a href="docs/merge-data.md">Combine data</a>
      <a href="docs/signed-urls.md">Signed URLs</a>
      <a href="examples/merge_csv_gcs.py">Examples</a>
    </nav>
  </header>

  <main>
    <section>
      <h2>Overview</h2>
      <p>storage.cloud collects short guides, command snippets, and small reusable scripts for common GCS workflows:</p>
      <ul>
        <li>Authenticated access (gcloud, service accounts, access tokens)</li>
        <li>Signed URLs for temporary public access</li>
        <li>CORS configuration for browser-based apps</li>
        <li>Merging objects (CSV/text) and loading to BigQuery</li>
      </ul>
    </section>

    <section>
      <h2>Helpful links</h2>
      <ul>
        <li><a href="https://storage.cloud.google.com/">storage.cloud.google.com</a> — web UI (requires Google sign-in)</li>
        <li><a href="https://console.cloud.google.com/storage/browser">Cloud Console — Storage browser</a></li>
        <li><a href="https://cloud.google.com/storage/docs">GCS documentation</a></li>
      </ul>
    </section>

    <section>
      <h2>Get started</h2>
      <p>Open <code>docs/quickstart.md</code> for commands and brief examples you can run locally. For combining files, see <code>docs/merge-data.md</code> and <code>examples/merge_csv_gcs.py</code>.</p>
    </section>
  </main>
</body>
</html>
```

````markdown name=docs/quickstart.md
# Quickstart — Google Cloud Storage (GCS)

This page collects the most-used commands and short examples for getting started with GCS.

Prerequisites
- Install Google Cloud SDK (gcloud, gsutil)
- For programmatic examples, install the relevant client libraries (Python: google-cloud-storage)

Authentication
- Local developer (Application Default Credentials - ADC):
  gcloud auth application-default login

- Service account (recommended for server apps):
  1. Create:
     gcloud iam service-accounts create my-sa --display-name="My SA"
  2. Grant roles (example: objectViewer):
     gcloud projects add-iam-policy-binding PROJECT_ID \
       --member="serviceAccount:my-sa@PROJECT_ID.iam.gserviceaccount.com" \
       --role="roles/storage.objectViewer"
  3. (Optional) Create key for local use:
     gcloud iam service-accounts keys create key.json \
       --iam-account=my-sa@PROJECT_ID.iam.gserviceaccount.com

Access tokens
- Print an access token for HTTP Authorization:
  gcloud auth print-access-token
- Use as header: Authorization: Bearer <ACCESS_TOKEN>

Common gsutil commands
- List buckets:
  gsutil ls gs://

- List objects in a bucket/prefix:
  gsutil ls gs://BUCKET/PREFIX/

- Download object:
  gsutil cp gs://BUCKET/OBJECT ./local-file

- Upload file:
  gsutil cp ./local-file gs://BUCKET/OBJECT

- Make object public (use sparingly):
  gsutil acl ch -u AllUsers:R gs://BUCKET/OBJECT

Signed URLs
- Short way to create an expiring URL using a service account key:
  gsutil signurl -d 1h /path/to/key.json gs://BUCKET/OBJECT
- Notes:
  - V4 signed URLs are supported; maximum expiry is 7 days.
  - Signed URLs allow access without a Google account.

CORS (for browser clients)
- Example file: cors.json (in repo)
- Apply:
  gsutil cors set cors.json gs://BUCKET

BigQuery ingestion
- BigQuery accepts wildcards — you can load many CSVs without pre-merging:
  bq load --autodetect --source_format=CSV dataset.table gs://BUCKET/PATH/*.csv

Troubleshooting
- Permission denied: check IAM roles (roles/storage.objectViewer or a custom role).
- Invalid credentials: re-run `gcloud auth application-default login` or refresh service account keys.
- CORS errors: ensure bucket CORS includes your domain and required methods/headers.

Security recommendations
- Use service accounts with least privilege.
- Prefer IAM + uniform bucket-level access over ACLs.
- Do not embed long-lived keys in client-side code; use signed URLs for browser access.

Further reading
- GCS docs: https://cloud.google.com/storage/docs
- Signed URLs: https://cloud.google.com/storage/docs/access-control/signed-urls
- gsutil reference: https://cloud.google.com/storage/docs/gsutil
````

````markdown name=docs/merge-data.md
# Gabungkan Semua Data (Combine all data)

Dokumen ringkas (Bahasa Melayu) untuk menggabungkan fail yang tersimpan di Google Cloud Storage.

Sebelum mula
- Pastikan anda mempunyai akses ke bucket (roles/storage.objectViewer atau storage.objectAdmin untuk penulisan).
- Jika dataset besar (GB/TB), pertimbangkan Dataflow/Dataproc atau import terus ke BigQuery.

Pilihan umum

1) Quick merge (fail kecil/sederhana)
- Jika saiz kecil supaya muat dalam memori:
  gsutil cat gs://BUCKET/PATH/*.csv | gsutil cp - gs://BUCKET/PATH/combined.csv
- Risiko: memori dan rangkaian. Pastikan hanya digunakan untuk set kecil.

2) gsutil compose (gabungkan objek di GCS tanpa muat turun)
- Gabungkan hingga 32 objek per operasi:
  gsutil compose gs://BUCKET/part1.csv gs://BUCKET/part2.csv gs://BUCKET/combined.csv
- Untuk >32 objek, buat compose berperingkat (tree compose).
- Nota: compose menggabungkan byte; pastikan setiap objek mempunyai newline yang sesuai dan tidak menggabungkan header berganda.

3) Load terus ke BigQuery (disarankan untuk analisis)
- BigQuery boleh baca wildcard CSV:
  bq load --autodetect --source_format=CSV dataset.table gs://BUCKET/PATH/*.csv
- Kelebihan: skala, tiada pra-gabungan, schema handling.

4) Skrip tersuai (contoh Python)
- Kelebihan: kawalan penuh (buang header berganda, normalisasi).
- Lihat `examples/merge_csv_gcs.py` untuk contoh.

Contoh strategi untuk >32 objek menggunakan gsutil compose (pattern)
- Bahagikan senarai objek kepada kumpulan 32, compose setiap kumpulan ke objek sementara, kemudian compose hasil sementara (ulang sehingga satu objek terakhir).
- Atau gunakan pipeline Dataflow untuk penstriman dan penulisan semula.

Amalan terbaik
- Untuk output akhir yang akan dianalitik: lebih baik tulis ke BigQuery atau Parquet (kolumnar) untuk kos dan prestasi.
- Pastikan logging dan versi objek dihidupkan jika data penting (object versioning).
- Gunakan encryption keys (CMEK) jika diperlukan.

Contoh ringkas penggunaan skrip
- Jika anda mahu, berikan:
  - Bucket name (contoh: gs://my-bucket/data/)
  - Prefix dan jenis fail (.csv)
  - Saiz anggaran (GB/TB)
  Saya akan hasilkan skrip automatik yang sesuai (bash atau Python) untuk anda.
````

````markdown name=docs/signed-urls.md
# Signed URLs — Quick reference

Signed URLs grant temporary access to an object without requiring the requester to have a Google account.

Key points
- Signed URLs include authentication in query parameters.
- Anyone with the URL can access the object until expiry.
- Maximum expiry for V4 signed URLs is 7 days.

Create with gsutil (using service account key file)
  gsutil signurl -d 1h /path/to/key.json gs://BUCKET/OBJECT

Create programmatically (Python example sketch)
- Use google-auth library to sign and generate a V4 URL, or use google-cloud-storage's blob.generate_signed_url method.
- Prefer service account credentials with least privilege.

Security tips
- Monitor signed-URL usage where possible (Cloud Storage logs).
- Shorter expiry reduces risk.
- Do not embed long-lived private keys in public repositories.

When to use
- Temporary downloads for users without Google accounts.
- Browser uploads (PUT) when combined with appropriate CORS settings.
````

```python name=examples/merge_csv_gcs.py
#!/usr/bin/env python3
"""
examples/merge_csv_gcs.py

Merge CSV files in a GCS prefix into one CSV while keeping only the first header.

Requirements:
  pip install google-cloud-storage

Usage:
  export GOOGLE_APPLICATION_CREDENTIALS="/path/to/key.json"
  python3 examples/merge_csv_gcs.py my-bucket data/prefix/ output/combined.csv
"""
import sys
import csv
from io import StringIO
from google.cloud import storage

def merge_csvs(bucket_name, prefix, output_blob_name):
    client = storage.Client()
    bucket = client.bucket(bucket_name)
    blobs = sorted([b for b in bucket.list_blobs(prefix=prefix) if b.name.endswith('.csv')])

    if not blobs:
        print("No CSV files found with prefix:", prefix)
        return

    out_buf = StringIO()
    writer = None
    first = True

    for blob in blobs:
        print("Reading:", blob.name)
        # Use streaming download if files are large: download_as_text() may load entire object
        data = blob.download_as_text()
        reader = csv.reader(StringIO(data))
        header = next(reader, None)
        if first:
            writer = csv.writer(out_buf)
            if header:
                writer.writerow(header)
            for row in reader:
                writer.writerow(row)
            first = False
        else:
            # skip header for subsequent files
            for row in reader:
                writer.writerow(row)

    out_blob = bucket.blob(output_blob_name)
    out_blob.upload_from_string(out_buf.getvalue(), content_type='text/csv')
    print(f'Uploaded gs://{bucket_name}/{output_blob_name}')

if __name__ == "__main__":
    if len(sys.argv) != 4:
        print("Usage: merge_csv_gcs.py BUCKET PREFIX OUTPUT_BLOB")
        print("Example: merge_csv_gcs.py my-bucket data/ output/combined.csv")
        sys.exit(1)
    merge_csvs(sys.argv[1], sys.argv[2], sys.argv[3])
```

```json name=cors.json
[
  {
    "origin": ["https://example.com"],
    "method": ["GET", "HEAD", "PUT", "POST"],
    "responseHeader": ["Content-Type", "x-goog-meta-custom"],
    "maxAgeSeconds": 3600
  }
]
```

```text name=LICENSE
MIT License

Copyright (c) 2025

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction...
(You can replace with your full MIT text or preferred license.)
```

Would you like me to:
- generate a shell helper to compose >32 objects safely (tree compose), or
- produce a Dataflow/Beam starter for large merges, or
- create a translated Malay full-site version?

Tell me which follow-up and I will produce the exact script or pipeline.