Blob Storage Use Cases

Understanding Blob Storage in Modern Systems

Imagine you’re designing the next generation of Instagram. On day one, a hundred thousand users upload five million images. The next day, it’s ten million. By month three, you’re handling two billion images and growing. Now ask yourself: where do all these images live? You can’t store binary image data in PostgreSQL—that’s a quick path to database bloat and performance collapse. You need something different. You need blob storage.

Blob storage is the unsung hero of modern systems. It powers every platform that deals with user-generated content: YouTube’s video infrastructure, Dropbox’s file synchronization, Netflix’s media delivery, and AWS’s backup ecosystem. In this chapter, we’ll explore how blob storage works, why it’s fundamentally different from traditional databases, and how to architect systems around it effectively.

What Are Blobs, Really?

A Binary Large Object (blob) is exactly what it sounds like: any unstructured binary data that’s too large or too irregular to fit neatly into a relational database. Images, videos, audio files, PDFs, archives, log streams, machine learning models, firmware binaries—all blobs. Unlike structured data with defined schemas, blobs are opaque to the storage system. The system doesn’t care what’s inside; it just stores bytes.

This fundamental difference shapes everything about blob storage architecture:

Aspect	Relational Database	Blob Storage
Data Structure	Structured, typed columns	Opaque binary content
Query Capability	SQL, indexes on fields	Key-based or listing only
Access Pattern	Small frequent reads/writes	Streaming, sometimes sequential
Durability	ACID transactions	Eventually consistent replication
Pricing Model	Per-operation, per-storage	Per-storage, egress-based
Ideal Size	Kilobytes to megabytes	Megabytes to terabytes

Blob storage separates concerns that relational databases conflate. It handles raw data storage, while metadata (filename, creation date, permissions) lives in a separate index or database. This separation is crucial—you query metadata from a fast index, then retrieve the actual blob only when needed.

The Self-Storage Analogy

Think of blob storage like a self-storage facility. You rent a unit (container or bucket) and fill it with boxes of any shape or size (blobs). The facility doesn’t care what’s in each box; it just stores them. You label each box (assign a key or path) so you can find it later.

Now, storage facilities offer different tiers. Climate-controlled units (hot tier) cost more but keep everything accessible instantly. Standard units (cool tier) are cheaper but take longer to access. Deep archive units (archive tier) are cheap but require scheduling a retrieval appointment a day in advance (and that appointment’s a day long). Your bills reflect both the space you rent and the access patterns you choose.

That’s blob storage’s tiering: you pay for where data sits and how often you grab it.

Architecture and Internal Mechanisms

Modern blob storage systems solve three intertwined problems: distribution, durability, and retrieval. Let’s walk through how they work.

Distributed Storage and Consistent Hashing

Blob storage spreads data across thousands of machines. When you upload a file, the system must decide which physical nodes store it. A naive approach—picking nodes randomly—leads to load imbalance. The solution is consistent hashing, an algorithm that maps blob keys to physical nodes deterministically while minimizing data movement during scale-out.

Here’s the concept: imagine a ring with positions 0 to 2^32-1. You hash each server’s ID onto this ring, then hash each blob’s key. The blob lands on the first server clockwise from its hash position. When you add a new server, only blobs between that server and the next server clockwise need to move. This minimizes disruption during infrastructure changes.

Erasure Coding for Durability

Storing three complete replicas of every blob wastes 66% of space. Erasure coding does better. The idea: split a blob into k data chunks and compute m parity chunks. You need only k of the k+m total chunks to reconstruct the blob. With k=10 and m=4, you get 40% overhead (compared to 200% for triplication) while tolerating 4 chunk failures.

The trade-off: reconstructing a blob now requires reading multiple chunks and performing XOR computations. This is slower than simple replication but vastly more storage-efficient.

Tiered Storage and Lifecycle Policies

Hot data lives on fast storage (SSDs or fast HDDs). After 30 days, a lifecycle policy automatically moves blobs to cool tier (slower HDDs, maybe even tape). After a year, they move to archive tier. You specify policies per bucket:

{
  "transitions": [
    { "days": 30, "tier": "cool", "storage_class": "STANDARD_IA" },
    { "days": 90, "tier": "archive", "storage_class": "GLACIER" }
  ]
}

This automation reduces costs dramatically without requiring manual intervention.

Content-Addressable Storage

Some blob systems use content-addressable storage: the blob’s key is a hash of its content. If you upload the same file twice, both uploads produce the same key, and the system stores it once. This deduplication saves space in systems like backups where redundancy is rife. The cost: you can’t rename or modify files (you’d need a new key).

Access Patterns and Optimization

Streaming and Multipart Uploads

Uploading a 10GB video file in one operation is risky—network hiccups mean starting over. Multipart upload splits the blob into independent parts, uploaded in parallel. If part 3 fails, you retry only part 3, not the entire file. This transforms a risky operation into a resilient pipeline.

import boto3

s3 = boto3.client('s3')
bucket, key = 'my-videos', 'movie.mp4'

# Initiate multipart upload
response = s3.create_multipart_upload(Bucket=bucket, Key=key)
upload_id = response['UploadId']

# Upload parts in parallel
parts = []
with open('movie.mp4', 'rb') as f:
    for part_num in range(1, 11):  # 10 parts
        data = f.read(1024 * 1024 * 100)  # 100MB per part
        part_response = s3.upload_part(
            Bucket=bucket,
            Key=key,
            PartNumber=part_num,
            UploadId=upload_id,
            Body=data
        )
        parts.append({
            'ETag': part_response['ETag'],
            'PartNumber': part_num
        })

# Complete the upload
s3.complete_multipart_upload(
    Bucket=bucket,
    Key=key,
    UploadId=upload_id,
    MultipartUpload={'Parts': parts}
)

Pre-signed URLs and Temporary Access

You want users to upload directly to blob storage, bypassing your application servers. But you can’t hand out your storage credentials. Pre-signed URLs solve this: your backend generates a temporary URL that grants limited access (upload-only, expires in 15 minutes) without exposing credentials.

# Generate a pre-signed URL for upload
url = s3.generate_presigned_url(
    'put_object',
    Params={'Bucket': 'my-uploads', 'Key': 'user-photo-123.jpg'},
    ExpiresIn=900  # 15 minutes
)

# Client receives URL, uploads directly
# POST to url with image file

This architectural pattern decouples upload bandwidth from your application, enabling massive scale.

CDN Integration

Blob storage is usually co-located with compute (same region, same datacenter). But users are global. A CDN (Content Delivery Network) caches blobs at edge locations near users, reducing latency and load on the origin. Configuration is typically straightforward—point the CDN at your blob storage bucket, and it handles the rest.

User in Tokyo → Tokyo CDN Edge (cache hit, 10ms)
User in São Paulo → São Paulo CDN Edge (cache miss)
                 → Origin Blob Storage (100ms total)
                 → Cache at São Paulo Edge for future requests

Designing Systems Around Blobs

Media Platform: Video Upload Pipeline

Let’s design Instagram. When a user uploads a video:

1. Client generates pre-signed URL from your backend
2. Client uploads directly to blob storage (multipart for large files)
3. Storage system triggers a webhook on upload completion
4. Your backend processes the video: thumbnail generation, transcoding, metadata extraction
5. Processed derivatives (thumbnail, HD version, SD version) stored in separate blobs
6. Metadata indexed in a database: video_id, title, description, thumbnail_url, variants
7. CDN serves all derived blobs to viewers

The flow is event-driven. Your backend stays out of the upload path, scaling independently from media ingestion.

Backup and Disaster Recovery

Enterprise backups demand immutability and compliance. Blob storage enables this:

• WORM (Write-Once-Read-Many) locks prevent deletion for N years
• Replication across regions enables disaster recovery
• Lifecycle policies archive old backups automatically
• Point-in-time recovery by versioning blobs

A backup system might store daily snapshots for 30 days (hot tier), monthly snapshots for 7 years (archive tier), all protected by WORM locks. Compliance rules enforce this, not manual discipline.

Data Lake and ETL

Data lakes collect raw data (logs, sensor streams, user events) in blob storage, then transform it. The blob storage acts as the system of record:

Raw Data (blobs) → ETL Pipeline → Processed Data (blobs)
                              → Indexed Tables (data warehouse)

Because blobs are immutable (or versioned), you can rerun ETL jobs without fear of data corruption.

Static Website Hosting

Deploying a static website is blob storage with a metadata twist:

Bucket: my-website.com
  index.html (served when requesting /)
  about.html
  styles/main.css
  images/logo.png
  ...

The blob storage system routes requests, applies caching headers (metadata on blobs), and serves directly. No application servers, no databases, just blobs and CDN. Serverless hosting, essentially.

Trade-offs and Limitations

Cost Optimization Across Tiers

Tiering saves money but requires prediction. If you aggressively archive data then immediately retrieve it, you pay retrieval costs that exceed cool-tier storage. Model your access patterns: if data’s accessed under 3 times per year, archive it; if accessed weekly, keep it cool.

Retrieval Latency vs. Storage Cost

Archive tier might cost 40% of cool tier but requires hours to retrieve. For compliance data you’ll never actually need, that’s fine. For active working data, it’s a trap. Design thoughtfully.

Consistency Guarantees

Most blob storage systems offer strong consistency for single-blob operations (after you receive the success response, all reads see the new data), but weaker guarantees across blobs. If blob A references blob B, deleting B while writes to A are in-flight can leave A broken. Your application must handle this.

Egress Costs

Storing a blob in blob storage costs money (per GB per month). Transferring it out costs more (per GB transferred). In AWS, egress is expensive; download a TB of data and you’ll feel it. In some geographic regions, it’s prohibitive. This is a hidden cost that surprises teams at scale.

Vendor Lock-in

S3 (AWS), Blob Storage (Azure), Cloud Storage (GCS) all have different APIs, different consistency models, different pricing. Moving millions of objects is non-trivial. Design for portability: use SDKs that abstract differences, test on mock storage services locally, and document your dependencies.

When NOT to Use Blob Storage

Blob storage is wrong for:

• Small structured data (use a database)
• Frequently updated records (blob storage is write-once or versioned, not mutable)
• Transactional data (no ACID guarantees across blobs)
• Data requiring complex queries (no search or indexing on blob content)

If you need to query “all images taken in Japan between 2020-2023,” that’s metadata query with blob retrieval, not a blob storage query.

Key Takeaways

• Blob storage is a different beast: Unstructured data, key-based access, immutable semantics. It complements databases; it doesn’t replace them.
• Scale blob uploads with multipart and pre-signed URLs: Distribute ingestion load, tolerate network failures, protect credentials.
• Tiering and lifecycle policies are cost superpowers: Automatically migrate cold data to cheaper tiers. Model your economics.
• CDN integration is essential for global distribution: Don’t serve blobs from a single region; cache at the edge.
• Erasure coding outperforms replication at scale: 40% overhead for 4-fault tolerance beats 200% for 2-fault tolerance.
• Metadata lives separately: Index blob metadata in a database or search system. Query that for discovery, then retrieve blobs on demand.

Practice Scenarios

Scenario 1: Designing a Video-on-Demand Platform

You’re building a Netflix-like platform. Users upload raw videos; your system must create HD and SD transcoded variants, extract thumbnails, and serve globally with 10ms latency. Design the blob storage architecture. Consider:

• Where do transcoding jobs run? (Hint: close to blobs or distributed?)
• How do you prevent serving incomplete transcodes?
• How do you handle a user deleting a video that’s mid-transcode?
• What metadata do you index, and where?

Scenario 2: Building a Compliant Backup System

Enterprises require immutable backups with legal holds (can’t delete until court order). Design a backup system supporting:

• Daily backups for 30 days (instant retrieval required)
• Monthly backups for 7 years (48-hour retrieval acceptable)
• WORM locks enforcing immutability
• Cost optimization across regions and tiers
• Efficient point-in-time recovery

Scenario 3: Data Lake for Real-time Analytics

Your company ingests 10TB of logs daily from thousands of servers. You want to run both real-time queries (last hour of data) and batch analytics (last year of data). Design the blob storage strategy. Consider:

• How do you organize blobs (by timestamp, by source, by type)?
• Which tier for which data age?
• How do you query efficiently without scanning all blobs?
• How do you prevent egress costs from exploding?

Looking Ahead

Blob storage is fundamentally about scale: it lets you store more data, faster, cheaper than traditional databases. But scale introduces complexity. How do you query blobs when you have millions of them? How do you analyze temporal data efficiently? How do you extract structure from unstructured bytes?

The next frontier is time-series storage. While blob storage is write-once and relatively static, time-series databases are append-optimized and query-heavy. They power monitoring systems, real-time analytics, and IoT platforms. Understanding their differences from blob storage—and how to use both together—is crucial for modern system design.