Data Lakes: Storing Raw Frames & Insights Side-by-Side

Introduction – Why Lakehouses Need Pixels

Modern businesses generate and consume more visual data than ever before. Surveillance cameras, retail shelf monitors, vehicle-mounted drones and smartphone uploads are feeding torrents of raw frames into enterprise systems. Yet these images often remain disconnected from structured data — siloed in object stores or archived on servers — while decisions are still driven by rows and columns in SQL databases and dashboards.

That’s where the lakehouse architecture makes a difference. By combining the scalability of data lakes with the transactional power of data warehouses, lakehouses offer a unified platform where images, video frames and associated insights can coexist with business metrics. JPEGs, PNGs and TIFFs land next to JSON outputs from image-processing APIs. Together, these formats enrich decision-making and drive automation.

Imagine a manufacturing quality dashboard that not only shows which parts failed inspection but also displays the exact frame that triggered the rejection — along with metadata like the object class, bounding box confidence and timestamp. Or a retail analytics pipeline that correlates brand presence on shelves with sales velocity using logo recognition API outputs. These aren’t futuristic concepts — they’re today’s emerging best practices, enabled by a new kind of data infrastructure.

This post explores how businesses can store raw image frames and extracted insights side-by-side, govern them effectively and optimize storage costs without compromising performance. Whether you're capturing license plates, food labels or industrial defects, combining pixels with structured data opens new doors for analysis, compliance and value creation.

From Camera to Cloud – Multi-Format Ingestion Pipelines

The journey from raw camera footage to actionable insight begins with an efficient ingestion pipeline. In the context of a modern data lakehouse, this means being able to store not just the original image files, but also the results of any analysis applied to them — such as recognized objects, detected faces or extracted text — in a consistent and queryable format.

Images are typically uploaded from edge devices — like smart cameras, mobile apps or IoT sensors — into object storage buckets. From there, a trigger-based workflow kicks in. This may be event-driven, responding in real-time as new files arrive or run in batches on a fixed schedule. Either way, the goal is to seamlessly capture both the raw frame and any downstream metadata.

For instance, once an image is uploaded, it can be passed to a cloud-based vision API. Depending on your use case, this could be an OCR API to extract printed text, a brand and logo recognition API to identify visual trademarks or a face detection API to locate and anonymize people. The resulting insights — often returned in JSON format — contain structured data that is essential for business analysis and compliance tracking.

It’s important to timestamp every asset and enrich it with metadata like source device ID, GPS location and content hash. These attributes not only improve data integrity and traceability but also enable more meaningful joins later on. They form the bridge between the visual world and your business systems — linking a given image to a work order, customer account or SKU.

By treating images and their insights as a unified ingestion unit, businesses can ensure consistency across data layers and eliminate the delays and manual overhead that often plague traditional ETL pipelines. The result is a scalable, API-friendly architecture ready to power everything from compliance reports to predictive dashboards.

Designing Schemas That Speak Both Pixel and SQL

Storing images in a data lakehouse is only part of the equation. To unlock their full value, you need a schema design that makes visual data compatible with business analytics tools — especially SQL-based systems. That means structuring metadata and insights from vision APIs in a way that’s easy to query, join and analyze alongside traditional business metrics.

A well-designed schema treats each image as a data asset, just like a row in a database. In addition to the image URI or storage path, you'll want to record technical details like resolution, format and file size. More importantly, it should capture the outputs of any computer vision tasks — such as detected objects, text or labels — as structured fields. These might include arrays of tags, confidence scores, bounding box coordinates or even vector embeddings for similarity search.

To keep the system scalable and interpretable, many organizations adopt the Bronze–Silver–Gold layer model. In the Bronze layer, you store raw, unprocessed data — original image files and unparsed JSON from APIs. The Silver layer includes cleansed, structured versions with normalized fields like object names or face counts. Gold layers offer enriched data, often aggregated or joined with external business sources for reporting.

Versioning and evolution are critical. As detection models improve, new fields or label taxonomies may be introduced. Your schema must accommodate these changes gracefully without breaking downstream processes. Schema evolution tools and enforced contracts — like fixed label sets or predefined object categories — help ensure long-term consistency.

Governance also plays a role in schema planning. If your frames contain sensitive content like people’s faces or license plates, you'll need to track whether anonymization has occurred and whether the image is permitted for public or internal use. Tagging such attributes at the schema level ensures your queries are both powerful and policy-compliant.

Ultimately, a thoughtful schema enables you to run a single SQL query that correlates image-based insights — like the number of products on a shelf or the presence of safety gear — with operational data such as sales trends, defect rates or compliance logs. It turns images from static assets into dynamic contributors to business intelligence.

Guardrails & Governance – Keeping Raw Frames Compliant

With great volumes of visual data come great responsibilities. Whether you're capturing images of people in public spaces, scanning packaging with identifiable information or monitoring workspaces for safety compliance, raw frames often carry privacy and regulatory implications. Effective governance ensures that your image lake is not only useful but also safe, ethical and legally sound.

The first line of defense is access control. Sensitive fields — such as facial embeddings, license plates or text extracted from ID documents — should be protected with fine-grained permissions. This means implementing role-based access and in some cases, row-level or column-level restrictions that prevent unauthorized users from viewing or exporting sensitive content.

Metadata tagging is equally crucial. By tagging images with attributes like “contains PII,” “anonymized,” or “requires retention,” you can automate governance workflows. For example, only images tagged as anonymized can be exported to downstream marketing dashboards. Tools like data catalogs and lineage trackers help trace how each image was used, transformed or shared — essential for audits and internal accountability.

Encryption should be applied not just at rest but also in transit. This includes encrypting storage buckets where images are kept and securing APIs that serve image metadata. For extra control, businesses can tokenize image URIs so that only approved systems can resolve or access them.

Retention and lifecycle management also play a role in governance. While metadata or derived analytics may be retained for long periods, the raw frames themselves often don’t need to live forever. You can set lifecycle policies that automatically move files to colder storage after a set period or delete them altogether once their purpose has been fulfilled.

Finally, data quality and observability shouldn’t be overlooked. Just like with tabular data, image pipelines can break — frames can be missing, mislabeled or outdated. Monitoring freshness, completeness and drift in object detection or classification results helps keep your lakehouse reliable. Implementing quality checks at ingestion and enrichment stages ensures that decisions based on visual data remain trustworthy.

By embedding these governance practices directly into your architecture, you make it possible to scale your image storage strategy without sacrificing security, compliance or accountability. It transforms raw visual data from a liability into a well-governed digital asset.

Cost-Optimized Storage Tiers for Cold, Cool & Hot Frames

Storing millions of image frames in a data lake can quickly become expensive if not properly managed. Unlike traditional structured data, image files — especially in high-resolution formats — consume substantial storage space. Add the need for fast access to recent frames and long-term retention of insights and the case for a tiered storage strategy becomes essential.

Not all images need to be immediately accessible at all times. That’s why smart lakehouse architectures separate data into “hot”, “cool” and “cold” tiers based on how frequently each asset is accessed. Hot storage is used for fresh data — frames captured within the last few days or weeks that are still relevant for real-time dashboards, QA workflows or active machine learning retraining. This tier uses high-performance, low-latency storage solutions that come at a premium cost.

Cool storage fits the middle ground. These are images or insights still valuable for periodic reporting, trend analysis or compliance verification, but not queried every day. Moving this data to infrequent-access tiers helps balance cost and availability. Cool storage is ideal for assets that may need to be reviewed monthly, such as product quality snapshots or shelf visibility checks.

Cold storage is your archival layer. Here, the priority is retention over speed. Older frames, legal hold data and images used solely for audit purposes can be safely moved into long-term storage solutions. These tiers often use ultra-low-cost storage with slower retrieval times, making them perfect for backups or regulatory archives that must be retained for years.

Compression and format selection also influence storage efficiency. Switching from traditional JPEG to more modern formats like WebP or AVIF can reduce file sizes without sacrificing quality. For metadata and API responses, storing structured outputs in compressed formats like Parquet or optimized JSON further minimizes footprint. When embeddings or vector features are included, they too can be serialized and stored efficiently, allowing similarity search without bloating the lake.

An effective storage strategy doesn’t just reduce cost — it improves system performance. By keeping only the most relevant frames and metadata in fast-access zones, your analytics and visualizations remain responsive, while historical data remains safely tucked away but recoverable. This balance of cost and performance is the backbone of scalable, future-proof image infrastructure.

Querying Pixels with SQL, UDFs & Vector Search

Bringing images into a lakehouse is only the beginning — true value emerges when those images can be queried, joined and analyzed alongside structured business data. Thanks to modern data platforms and cloud-native tools, visual content is no longer limited to manual review or isolated dashboards. Today, it's entirely possible to query images like you would any other data source — using SQL, user-defined functions (UDFs) and even vector search.

With the right setup, structured metadata from image analysis — like detected objects, brand labels or text extracted via OCR — can be stored in queryable formats. Analysts can run SQL queries to filter frames by specific tags, detection confidence or timestamp ranges. For example, a retailer might look for all shelf images where a certain brand was missing or a quality team could surface frames showing recurring defect types.

To go further, UDFs make it possible to extract features directly from raw or base64-encoded images during query execution. These functions can calculate blur levels, detect scene brightness or parse embedded EXIF metadata on the fly. This adds flexibility, especially in exploratory scenarios where new visual dimensions need to be mined without reprocessing the entire dataset.

Perhaps the most transformative capability is vector search. By embedding visual features — such as the layout of objects or stylistic patterns — into compact numerical vectors, you can build similarity queries that retrieve images based on visual resemblance rather than keyword labels. This is incredibly useful in scenarios like identifying duplicate frames, spotting counterfeit goods or navigating large image catalogs. Vector indexes, often powered by tools like FAISS or pgvector, allow lightning-fast retrieval across millions of records.

These techniques also make machine learning pipelines more accessible. Engineers and analysts can use enriched image metadata and embeddings as input features in predictive models, trained directly on the data lake. From automated SKU recognition to predictive maintenance based on visual cues, the opportunities multiply when images are treated as first-class queryable assets.

This convergence of SQL, computer vision and machine learning workflows means images are no longer locked away in isolated archives. Instead, they fuel insights, drive automation and support real-time decisions — becoming a core part of the enterprise data fabric.

Conclusion – Turning Lake-Native Pixels into Competitive Moats

As businesses grow increasingly visual — monitoring shelves, verifying identity, inspecting parts or scanning documents — the ability to manage, analyze and extract value from raw image data becomes a strategic differentiator. The lakehouse paradigm enables this by treating images not as separate, unstructured blobs, but as fully integrated data assets that live side-by-side with structured insights.

By designing ingestion pipelines that capture both raw frames and the metadata returned by vision APIs, organizations can build a unified foundation for advanced analytics. With thoughtful schema design, strong governance and cost-aware storage strategies, visual data becomes easy to query, secure to manage and economical to scale. And with technologies like SQL extensions, user-defined functions and vector search, querying pixels becomes as routine as joining tables.

Computer vision APIs — from OCR and object detection to face recognition and brand identification — play a crucial role in this ecosystem. They act as the translators between raw pixels and structured intelligence, making it possible to index, analyze and act on visual signals in real time. For those with unique requirements, custom vision solutions add another layer of depth — tailoring insights to your industry’s needs and unlocking competitive advantage.

In the end, the real opportunity lies not just in storing images, but in connecting them to decisions. When raw frames and business metrics flow through the same system, your data lake stops being a repository and starts being a competitive moat — fueling automation, insight and growth at scale.