Building a Modern, AI-Ready Data Lake (Without the Surprise Cloud Bill)

By Michelle Montano

AI isn’t just creating more data. It’s raising expectations for how quickly teams can use the data they already have. Modern workloads don’t care whether information arrives as a clean export or a 300-page PDF. They expect everything to be accessible on demand: logs, clickstreams, transcripts, images, video, documents, and internal knowledge, much of which was never designed for analytics.

A data lake is the storage foundation many organizations use for this reality: a centralized repository that can hold large volumes of data in raw, native formats, from structured tables to semi-structured events to unstructured files. Traditionally, the appeal was simple: land data cheaply, keep it flexible, and decide later how to process it.

In the AI era, that model shifts. The data lake is no longer a parking lot; it becomes the shared source layer for analytics, machine learning, and retrieval-augmented AI. Data is reused more often, accessed by more workloads, and pulled in smaller slices to supply context for models. When the storage layer introduces friction (slow reads, tiering delays, unpredictable fees), every downstream workflow feels the impact. If data is hard to access, it’s hard to use.

This article breaks down what it means for a data lake to be AI-ready, how storage and compute must align to function as a single system, and how this approach powers a real internal lake in production.

What makes a data lake AI-ready?

An AI-ready data lake solves for a different standard than the lakes of 10 years ago. Now we need to both store data, as well as keep it queryable, transformable, and economically usable at scale, so teams can explore, train, and retrieve without constantly moving data around or getting surprised by cost and latency issues.

For a data lake to be truly AI-ready, it needs to meet a few key requirements:

• A single system of record for all data types: The lake should store structured, semi-structured, and unstructured data in one place, eliminating the need for separate storage silos.

• A metadata and governance layer that ensures usability: Data must be discoverable, traceable, and controlled. Proper metadata management is crucial for making the data usable across teams and workflows.

• In-place access for analytics and AI workflows: The lake should allow for query and transformation directly on the data without the need for constant copying or delays caused by data movement.

• Cost predictability for regular operations: AI workflows touch data more frequently than traditional systems, so it’s essential to maintain predictable costs for reading, scanning, and transferring data, as well as running recovery or test drills.

These principles are fundamental to building a lake that can scale with modern workloads. The next step is ensuring the lake is designed with the right storage and compute layers, so the data remains easily accessible and ready for processing as workloads evolve.

The storage layer: Wasabi Hot Cloud Storage

Once AI workloads start leaning on a data lake, storage becomes the primary data foundation the rest of the architecture depends on. If storage is expensive, hard to secure consistently, or incurs unpredictable costs under frequent reads and tests, teams compensate with workarounds and the lake becomes harder to operate at scale.

Wasabi Hot Cloud Storage is the ideal foundation because it’s designed to keep access and economics straightforward, while providing the controls you need for governance and recovery:

• Predictable pricing when data gets reused: Capacity-based pricing with no fees for egress or API requests makes routine reads, scans, transfers, and validation workflows easier to budget for, especially as AI increases how often data gets touched.

• Designed for durability and operational reliability: As a primary foundation for lake data, object storage has to be built for long-term retention and consistent access patterns, not “archive first” assumptions.

• Security and resilience that support lake governance: Strong access controls, encryption, and immutability options support regulated data handling and ransomware-resilient copies. Covert Copy adds a hidden immutable copy designed to stay out of an attacker’s line of sight while remaining recoverable.

Wasabi + Snowflake: A unified architecture for analytics and AI

With the storage foundation set, the next piece is compute: an analytics and AI platform that can query and transform lake data efficiently. This is where S3 compatibility matters, because it lets the compute layer connect through standard object storage interfaces, so data can stay put while analytics and AI services run on top.

Snowflake plays this role in the architecture. It connects to Wasabi through an external stage, giving teams direct access to structured, semi-structured, and unstructured files without migrations or duplicated datasets.

In this architecture, raw operational data, logs, documents, and exports are stored as the long-term system of record. Analytics and AI platforms access that data through secure object interfaces, query it directly, and selectively materialize only the subsets that require additional governance or performance optimization.

The result is a unified system where analytics, machine learning, and AI applications share the same data foundation without constant copying, tiering, or duplication. This keeps the architecture simpler to operate and easier to scale as new workloads are introduced.

Turning documents into AI-ready knowledge

Once the core lake architecture is in place, unstructured content becomes a first-class part of the data environment. Documents such as reports, specifications, logs, and compliance materials remain stored in the lake but can be transformed into structured representations that analytics and AI systems can work with.

Document processing services extract text and structure from these files, while search and retrieval services index that content so it can be queried using both keywords and semantic meaning. The result is a unified knowledge layer where documents are no longer static artifacts, but searchable, analyzable data that can support analytics, discovery, and AI-driven applications.

How Wasabi uses this architecture internally

At Wasabi, we use this architecture to power our own internal analytics and knowledge management. Operational data from billing systems, API logs, and platform telemetry lands in Wasabi Hot Cloud Storage as the core system of record. Snowflake connects to those buckets via external stages, transforms the data, and powers dashboards that track usage, performance, and customer trends in near real time.

For unstructured content like PDFs, logs, and engineering documents, the same architecture applies. Files are stored in Wasabi buckets, Snowflake parses them into structured outputs, and Cortex Search supports retrieval across the resulting knowledge layer. Internal teams can then use natural language to ask questions like, “Show API usage trends by region over the last quarter,” or “Find the latest compliance report that references object durability.”

Looking ahead: Wasabi Fire for next-generation data lakes

Wasabi Hot Cloud Storage excels as a foundation for data-at-rest workloads and large-scale analytics. The upcoming Wasabi Fire storage class extends that foundation to real-time, low-latency scenarios that demand even higher performance.

Wasabi Fire is designed for data lake use cases such as:

• Real-time IoT and telemetry streams

• AI and ML training or inference that requires very low access latency

• Edge workloads and event-driven analytics

By bridging our Hot and Fire classes under one cloud storage ecosystem, organizations can scale from cost-optimized analytics to real-time intelligence without introducing a patchwork of storage products and pricing models.

A practical path to AI-ready data lakes

Modern data lakes now sit at the center of analytics and AI strategy. They need to hold more data, support more workloads, and stay flexible as models and business requirements evolve, all without turning into a financial or operational burden.

By combining predictable, always-hot storage with compute that can work directly on the lake, organizations gain:

• A scalable home for all data types

• A unified foundation for analytics and AI

• A document layer that’s fully searchable and model-ready

• A cost structure that encourages experimentation instead of constraining it

It all adds up to a modern, AI-ready data lake that aligns with how teams actually want to work: more data online, more workloads on top, and fewer surprises on the monthly bill.