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While most AI discussions focus on models and compute, the true bottleneck often lies in the foundational data pipeline. This blog series introduces a three-stage storage model-covering ingest, training, and deployment-to address this challenge. We begin with Stage 1, the data ingest and curation tier, exploring why this "unsung hero" is the foundation of the AI data lake and how it transforms raw data into the high-quality, curated datasets essential for successful AI projects.
The Capacity Storage Tier
In the age of artificial intelligence, data is everywhere - streaming from sensors, buried in logs, scattered across cloud services, and locked inside legacy systems. For teams building large language models (LLMs) and other data-intensive AI systems, the challenge isn’t just acquiring data - it’s organizing it, accessing it, and making it usable.
That’s why centralized storage and its data management tools have quietly become one of the most critical components of modern AI infrastructure. While most discussions around AI focus on modeling, compute, and performance, the real bottleneck often lies upstream in the data pipeline. Specifically, it’s in the capacity storage that ingests, organizes, and prepares data for training and inference. This often-overlooked stage is the unsung hero of scalable AI development.
This series of articles introduces a three-stage storage model that addresses this challenge:
Stage 1 – Ingest and Curation: A capacity storage tier optimized for cost, designed to absorb and store vast volumes of raw, unstructured data from diverse sources.
Stage 2 – Data Staging and Modeling: A performance storage tier optimized for high throughput and low latency, feeding curated datasets into models during model development and training.
Stage 3 - Deployment: A go-live or production storage tier where the final AI model gains enterprise-level resiliency and reliability and is deployed to solve real business problems.
The data ingest stage is where the data journey begins. Its capacity storage is the foundation of the data lake - a centralized repository that stores raw data at scale, making it accessible for transformation, exploration, and analysis. Without this stage, AI teams would be stuck wrangling fragmented datasets, passing along poorly cleaned data, and losing valuable insights.
Data Ingest Storage: A Central Repository for AI-Scale Data
Data ingest storage is where it all begins. The capacity storage tier ingests raw structured or unstructured data - and stores them in a centralized, scalable system that forms the foundation of an AI data lake.
Unlike traditional data warehouses, which focus on high-speed queries against clean, well-structured data, the data ingest storage is designed to store everything from anywhere: the good, the bad, and the ugly. PDFs, CSVs, telemetry logs, video files, clickstreams, medical records, and more. The primary goal of this front-end stage is to collect and preserve data, which can then be filtered and refined into curated datasets for the data processing stage.
Once ingested, this raw data becomes indexable, discoverable, and available for downstream processing. Data engineers can explore datasets, apply transformations, or run metadata analysis. Data scientists can label and curate subsets which can then be advanced in the workflow for training. This storage stage supports not just volume but flexibility and interoperability, which are essential in a fast-changing AI environment.
One golden rule: never store raw data without adding context. Whether it’s metadata, semantic tags, or domain labels, contextual information becomes increasingly valuable as your data volume grows. It also plays a key role in governance, debugging, and reproducibility.
Beyond storing raw data, this capacity storage layer also holds key artifacts such as normalized datasets, curated and versioned datasets, logs from preprocessing jobs, project archives, and metadata files that define schemas or configuration states. Without a well-architected ingest layer, these elements become fragmented or duplicated across systems, eroding productivity and trust in the data.
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Store long-term models and archives cost-efficiently on a separate capacity tier.
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To support model reproducibility, debugging, and compliance, storage should enable versioning, logging, and traceability of model outputs, inputs, and intermediate artifacts.
The Central Role of Data Ingest Storage and its Benefits
This foundational storage layer is not merely a passive holding area; it is a dynamic participant that supports the entire data platform, ingesting data from a multitude of sources - from structured databases to unstructured logs and files. Data is meant to accumulate over time in this highly scalable storage, which is expected to span many projects and be a single source of reference for all of them. Without it, every new ML or AI initiative would start from scratch, requiring teams to hunt down and prepare data all over again. Instead, with an effective ingest and organizing layer, organizations can build reusable workflows, maintain version control, and rapidly onboard new AI use cases.
The ingest stage stores raw, untransformed data at massive scale, preserving its original format and context. This is crucial because it allows for future analysis and transformation without any loss of critical information. By using tools for indexing, querying, and transformation, the capacity-tier allows data scientists and engineers to find, access, and prepare data for downstream tasks.
A centralized catalog makes data discoverable, allowing teams to quickly find relevant information, which drastically reduces the time spent on data wrangling. This catalog acts as a map to the vast amounts of data, enabling efficient search and retrieval. It enables direct querying for advanced analytics and machine learning without the need for data movement. Instead of copying and moving large datasets, analysts and models can access the data directly in place, which is both faster and more cost-effective. Ultimately, this centralized approach unlocks fast, flexible insights that are critical for competitive advantage. By providing a unified, accessible data source, it empowers teams to experiment with new ideas and iterate on models more quickly, turning raw data into valuable business intelligence
The recent MIT Technology Review Insights report suggested that a well-run data stage is one of the keys to AI project success, emphasizing that clean, well-organized, and efficiently accessible data directly impacts model accuracy, training speed, and overall deployment readiness. Without a robust data foundation, even the most advanced algorithms struggle to deliver meaningful results.
Bottom line: Without a strong ingest data cleaning phase, achieving high data quality can become a major project impediment.
The idea that data cleaning is no longer necessary because LLMs can handle messy inputs more gracefully is misleading. While these models can extract patterns from imperfect data, poor quality still poses serious risks—especially for sensitive training methods like fine-tuning and RAG, which often break down without clean inputs. Dirty data can also introduce bias, reduce accuracy, complicate debugging, and the lack of knowing where data comes from will obscure data lineage. In reality, a strong data cleaning and metadata enrichment process during the ingest phase leads to good quality data. Training with clean, labeled and high-quality data builds trust with stakeholders judging model release readiness and remains essential for producing successful AI projects.
Feeding the AI Pipeline: From Raw to Curated Data
A typical AI/LLM data pipeline is a multi-step journey that transforms raw data into a trained model. The front-end ingest stage covers collection, cleaning, and labeling.
Collection → Cleaning → Enrichment (Labeling) → Normalization → Tokenization → Training
It begins with Collection, where vast amounts of data are gathered from various sources. This is followed by Cleaning, which involves removing noise, duplicates, and errors. The data is then often subjected to enrichment (or Labeling), where humans or automated processes add metadata to provide context. Next, the data is Normalized and Tokenized to prepare it for the specific format required by the model, culminating in the Training phase. For this entire process to be successful, it requires robust tracking, versioning, and seamless collaboration by the team across the complete AI pipeline.
Capacity-storage and data tooling are the engines that power the crucial front-end stages of this pipeline: collection, cleaning, labeling, and tracking. It serves as the primary landing zone for the raw, untransformed data, providing a centralized location for teams to work from. This single source of truth is where cleaning scripts are run, and where labeled datasets are stored. By centralizing these stages, the data organizing layer ensures that every change is tracked and every version is saved, creating a transparent and reproducible workflow of data lineage. In essence, the data ingest and organizing layer acts as the "glue" that holds the entire process together, bridging the gap between disconnected tasks and enabling seamless collaboration across different teams and team members.
Why Centralizing Storage is a game-Changer for AI teams
Many in the industry acknowledge that AI teams tend to devote a considerable amount of time to handling data-related tasks—collecting, cleaning, and preparing data - rather than on the actual modeling process. Organizations are increasingly recognizing that a strong data cleaning stage is crucial to AI success - clean, organized, and accessible data boosts overall AI project success.
Without centralized storage:
Teams engage in redundant efforts, with different groups recreating the same datasets. Inconsistent data versions lead to unreliable model performance and difficult debugging. The entire development process is delayed, as teams waste time searching for, wrangling, and moving data.
With centralized storage:
It becomes easier to search, reuse, audit, and share data, accelerating the development cycle. It provides a clear and traceable lineage, from the raw data all the way to the final training input.
Technical Requirements of Capacity Storage
For a data ingest and organizing storage solution to be effective in an AI pipeline, it must meet several technical requirements:
Integration: Seamless integration with advanced data cleaning and lineage tracking tools ensures that stored data remains accurate, traceable, and ready for curated data sets. By embedding these capabilities directly into the storage platform, it helps achieve high data quality while optimizing space and performance across the data infrastructure. Performance: It must be able to load balance multiple incoming simultaneous streams, ensuring smooth ingestion even under high load. Scalability: With data volumes growing to petabyte-scale and beyond, the storage must be able to scale massively to be future-proof. Accessibility: Universal access via standard web protocols is crucial for integration with diverse tools and platforms to store data from anywhere it resides. Equally important is the ability to ingest data using both object and file-based networking protocols within a unified system. Cost-efficiency: Leverage data duplication and compression to minimize storage footprint, combined with tiered storage architecture using a mix of HDDs and QLC flash SSDs to efficiently manage hot and cold data. Cloud-native: Modern solutions must integrate seamlessly with web tools and support a hybrid cloud movement, offering flexibility and agility. Metadata storing: It must support the storage of schemas, data lineage, and configuration tracking to maintain data governance.
Object Store as the Preferred Front-End Pipeline Storage for LLM Development
Today's cloud-native teams are increasingly gravitating toward object stores as their preferred solution for data ingest storage. The benefits of this approach are clear: they are highly scalable, relatively inexpensive, and offer a simple, API-friendly interface (like S3 or GCS) that seamlessly integrates with modern cloud tools. This flexibility and cost-efficiency make object stores an ideal foundation for a data lake.
Filesystem-based approaches, in contrast, are often seen as less suitable for today's AI workloads. They are frequently too complex to manage at the petabyte scale required for LLM development, and they are less compatible with the distributed, cloud-based AI workflows that have become the industry standard. Furthermore, traditional filesystems are simply not optimized for the massive, internet-scale data flows that are now common in AI pipelines.
Rise of Table Stores for Metadata running on object storage
In AI and machine learning workflows, metadata is increasingly flexible, semi-structured, and dynamic. Traditional relational databases, with their rigid schemas and scalability limitations, often become bottlenecks in these environments. This has led to a shift toward table stores, typically NoSQL-based systems, which offer schema flexibility, horizontal scalability, and native integration with cloud object storage platforms like Azure Table Storage and BigQuery Object Tables from Google. By running table stores on top of object storage, AI teams gain a unified, cost-efficient architecture for managing both data and metadata - enabling real-time access, simplified infrastructure, and more agile, scalable development pipelines.
Performance-Tiered Object Storage
There’s a strong trend toward unified object storage platforms that merge the capacity of traditional object stores with the performance of SSDs. This enables high-speed access for active workloads like real-time data queries, interactive analytics and data labeling. This has proven to be a good use of tiering.
Unified Platforms for doing the training
Creating very high-performance object stores could be especially useful for AI/ML pipeline model training. These TLC SSD based systems in theory could allow one storage type for the whole storage pipeline. However, so far most unified platforms haven’t proven fast enough to be used for training, though the fastest could be deployed for data prep and inferencing.
Conclusion: Storage as a strategic enabler for better AI
Centralized, cloud-native storage has evolved from a nice-to-have into a foundational pillar of modern AI infrastructure. In today’s data-driven landscape, the ability to store, manage, and access vast volumes of information efficiently is critical—not just for operational ease, but for unlocking the full potential of AI systems.
The end goal of the ingest and organization stage is to provide curated datasets of the highest possible quality, while bringing structure to what is too often chaos. When executed properly, this stage of the data pipeline produces data that is:
Centralized – all raw and semi-processed data in one place Format-agnostic – ready to support multiple downstream use cases Quality-controlled – cleaned, deduplicated, and validated Enriched with metadata – including provenance, licensing, and lineage Governed – with access controls, compliance tagging, and audit logs
By gaining control over the quality of training data, teams increase the likelihood that models will be production-ready. Even if you're not yet prepared for this level of maturity, putting basic mechanisms in place now will serve as stepping stones toward it. As AI initiatives scale, the volume and complexity of data will grow exponentially—making this foundation not just helpful, but essential. A well-executed plan for managing raw data becomes a strategic advantage in building reliable, scalable AI systems.
Object storage stands out as the ideal foundation for building scalable and flexible data lakes. Its ability to handle unstructured data, support massive scalability, and integrate with modern analytics and machine learning tools makes it indispensable for AI workloads. Looking ahead, the future of AI infrastructure lies in unified, cloud-native architectures that can scale effortlessly, adapt to evolving demands, and provide a seamless experience from data ingestion to model deployment. Embracing this future means investing in storage solutions that are not only robust and reliable but also intelligent and agile.
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