Tag: Data Engineering

The Backyard Quarry, Part 6: Scaling the Quarry

So far, the Backyard Quarry system has worked well.

We have:

  • a schema
  • a capture process
  • stored assets
  • searchable data
  • digital twins

For a small dataset, everything feels manageable.

A few rocks here and there.

A handful of records.

It’s easy to reason about the system.

When the Dataset Grows

The moment the dataset starts to grow, the assumptions change.

Instead of a few rocks, imagine:

  • hundreds
  • thousands
  • eventually, many thousands

At that point, a few new questions appear:

  • How do we process incoming data efficiently?
  • Where do we store large assets?
  • How do we keep queries fast?
  • What happens when processing takes longer than capture?

These are the same questions that show up in any system dealing with real-world data.

The Pipeline Becomes the System

At small scale, the pipeline is implicit.

You take a photo.

You upload it.

You update a record.

At larger scale, that approach breaks down.

The pipeline becomes explicit.

Diagram showing a scalable data pipeline for physical objects including capture, ingestion queue, processing workings, storage, and indexing.
At scale, simple data flows evolve into multi-stage pipelines with decoupled processing and storage.

Each stage now has a role:

  • capture generates raw input
  • ingestion buffers incoming data
  • processing transforms it
  • storage persists it
  • indexing makes it usable

What used to be a simple flow becomes a system of components.

Decoupling the System

One of the first things that happens at scale is decoupling.

Instead of doing everything at once, we separate concerns:

  • capture does not block processing
  • processing does not block storage
  • storage does not block indexing

This introduces queues and asynchronous work.

Instead of:

take photo → process → store → done

we now have:

take photo → enqueue → process later → update system

This improves resilience.

It also introduces complexity.

Storage Starts to Matter

At small scale, storage decisions are easy.

At larger scale, they matter.

We now have different types of data:

  • metadata (small, structured)
  • images (large, unstructured)
  • 3D models (larger, computationally expensive to generate)

These tend to be stored differently:

  • database for structured data
  • object storage for assets
  • references connecting the two

This separation becomes critical for performance and cost.

Processing Becomes a Bottleneck

Not all steps in the pipeline are equal.

Some are fast:

  • inserting metadata
  • updating records

Others are slow:

  • generating 3D models
  • running image processing
  • extracting features

As the dataset grows, these slower steps become bottlenecks.

Which leads to another pattern:

Parallelization.

Instead of one process handling everything, we distribute the work.

Multiple workers.

Multiple jobs.

Multiple stages running simultaneously.

Indexing at Scale

Search also changes at scale.

At small scale:

  • simple queries are fast
  • no special indexing required

At larger scale:

  • indexes must be built and maintained
  • similarity search requires preprocessing
  • updates must propagate through the system

Search becomes an active part of the pipeline, not just a query on top of it.

Failure Becomes Normal

At small scale, failures are rare and easy to fix.

At larger scale, failures are expected.

Examples:

  • missing images
  • failed processing jobs
  • incomplete models
  • inconsistent metadata

The system must tolerate these failures.

Not eliminate them.

This leads to:

  • retries
  • partial results
  • eventual consistency

In other words, the system becomes more realistic.

A Familiar Architecture

At this point, the Backyard Quarry starts to resemble a typical data platform.

Layered architecture diagram showing physical world input flowing through capture, ingestion, processing, storage, indexing, and application layers.
A common architectural pattern for systems that transform physical inputs into digital data.

Different domains implement this differently.

But the structure is remarkably consistent.

The Tradeoff

Scaling introduces tradeoffs.

We gain:

  • throughput
  • flexibility
  • resilience

We lose:

  • simplicity
  • immediacy
  • ease of reasoning

What was once a straightforward system becomes a collection of interacting parts.

The Real Shift

The most important change isn’t technical.

It’s conceptual.

At small scale, you think about individual objects.

At larger scale, you think about systems.

You stop asking:

How do I store this rock?

And start asking:

How does the system handle many rocks over time?

That shift is what turns a project into a platform.

What Comes Next

At this point, the Backyard Quarry is no longer just a small experiment.

It’s a miniature version of a data platform.

And the patterns we’ve seen — schema design, pipelines, indexing, scaling — show up in many places.

In the next post, we’ll zoom out even further.

Because once you start recognizing these patterns, you begin to see them everywhere.

Not just in rock piles.

But in systems across industries.

And somewhere along the way, the Quarry stopped being about rocks.

It became about how systems grow.

The Rock Quarry Series

Facebooktwitterredditlinkedinmail

The Backyard Quarry, Part 4: Searching a Pile of Rocks

By this point, the Backyard Quarry has a schema, a capture process, and a growing collection of records.

Each rock has:

  • metadata
  • images
  • possibly a 3D model

In theory, everything is organized.

In practice, it quickly becomes difficult to find anything.

The First Search Problem

With a handful of rocks, you can rely on memory.

You remember roughly where things are.

You recognize shapes and colors.

But as the dataset grows, that breaks down.

You start asking questions like:

  • Which rocks are under 5 pounds?
  • Which ones are suitable for landscaping?
  • Where did that smooth gray stone go?

At that point, you’re no longer dealing with a pile.

You’re dealing with a dataset.

And datasets need to be searchable.

Filtering by Metadata

The most straightforward approach is to use structured queries.

If we have metadata like weight, color, and classification, we can filter directly.

Conceptually:

SELECT *
FROM rocks
WHERE weight_lb < 5
AND color = 'gray'
AND rock_class <= 'Class 2'

This works well for clearly defined attributes.

It’s predictable.

It’s efficient.

And it’s the foundation of most data systems.

The Role of Classification

This is where the Quarry Taxonomy starts to pay off.

Instead of requiring precise measurements, we can use categories:

  • Pebble Class
  • Hand Sample
  • Landscaping Rock
  • Wheelbarrow Class
  • Engine Block Class

This allows for simpler queries:

  • “Show me everything below Wheelbarrow Class”
  • “Exclude Engine Block Class entirely”

Classification reduces complexity.

It turns continuous values into discrete groups.

This is a common pattern in real-world systems.

When Metadata Isn’t Enough

Structured queries work well when you know exactly what you’re looking for.

But sometimes you don’t.

Sometimes the question looks more like:

Find rocks that look like this one.

Or:

Find something similar to the smooth stone I saw earlier.

At that point, metadata alone isn’t enough.

We need another way to compare objects.

Similarity and Representation

Images and 3D models contain information that isn’t captured in simple fields like color or weight.

To use that information, we need to represent it in a comparable way.

One approach is to generate embeddings — numerical representations of images or shapes.

Conceptually:

  • each rock image → vector representation
  • similar images → vectors close together
  • dissimilar images → vectors further apart

This allows for similarity search.

Instead of filtering by attributes, we search by resemblance.

A Different Kind of Query

With similarity search, queries look different.

Instead of:

color = 'gray'
weight < 5

We might have:

find nearest neighbors to this image

This shifts the system from exact matching to approximate matching.

It’s less precise.

But often more useful.

A Familiar Pattern

At this point, the Backyard Quarry starts to resemble systems used in:

  • image search engines
  • product recommendation systems
  • digital asset management platforms
  • AI-powered retrieval systems

The objects are different.

The pattern is the same.

Store data.

Index it.

Provide multiple ways to retrieve it.

Combining Approaches

In practice, the most useful systems combine both methods.

Structured filtering:

  • weight
  • class
  • location

Similarity search:

  • appearance
  • shape
  • texture

Together, they provide flexibility.

You can narrow down the dataset and then explore it.

The Cost of Search

Search doesn’t come for free.

It introduces:

  • indexing overhead
  • additional storage
  • preprocessing steps
  • more complex queries

And like everything else in the Quarry system, these tradeoffs become more significant as the dataset grows.

The Realization

At this point, something interesting becomes clear.

The hard part isn’t collecting rocks.

It isn’t even modeling them.

The hard part is making the data usable.

And usability, in most systems, comes down to one thing:

Search.

What Comes Next

With data captured and searchable, the next step is to zoom out.

What we’ve built so far is more than just a rock catalog.

It’s a small example of a larger idea.

In the next post, we’ll look at that idea more directly:

Digital twins.

Because once you can represent, store, and search objects, you’ve taken the first step toward building systems that mirror the physical world.

And somewhere in the process, it becomes clear that even a pile of rocks benefits from thoughtful indexing.

Which is not something I expected to say when this started.

The Rock Quarry Series

Facebooktwitterredditlinkedinmail