Designing data repositories to support LLM fine-tuning is not just about picking technology platforms. From an enterprise architecture perspective, data storage is a comprehensive architectural building block that encompasses far more: organizational boundaries, governance, cost models, data quality, and operational safeguards.
This article is the first in a three-part series proposing a blueprint for designing enterprise-grade LLM data repositories. Each post explores a core building block and breaks it into concrete sub-blocks that enterprise architects must define, regardless of which underlying technology stack they choose.
This first installment focuses on the Data Storage building block, detailing its architecture components and enterprise-level considerations.
Primer- What is an LLM Data Repository?
An LLM data repository is a specialized infrastructure built to support the training, fine-tuning, and inference of large language models. Unlike traditional data warehouses that handle mainly analytics queries, LLM repositories must accommodate:
- Diverse data formats (text, structured data, multimedia)
- Large-scale feature engineering pipelines
- Both batch training data and real-time inference workloads
While this post focuses on LLMs, the architectural blueprint applies broadly to other AI use cases, including computer vision, text-to-speech, and multimodal systems.
The Blueprint Approach: Blocks and Sub-Blocks
From an architecture viewpoint, building an LLM data repository involves designing three fundamental building blocks:
- Data Storage
- Data Processing
- Feature Management
Each building block contains specific sub-blocks—the critical details architects must explicitly design and govern. Architects define not only the technology stack but also the policies, standards, and operational principles that turn these technologies into a coherent enterprise architecture.
Data Storage: The Foundation for Marketing AI
The Data Storage building block handles the organization, retention, and scalable access to raw data. It’s foundational to business functions ranging from analytics and compliance to machine learning workflows.
Enterprise Architecture goes far beyond choosing S3, Azure Blob, or Snowflake. While selecting tools is important, it’s only part of the puzzle. Enterprise Architects look at the big picture—governance, costs, security, and business alignment. They ensure technology becomes true enterprise value.
Enterprise Architects must design:
- The structure of storage layers or zones
- Access and security policies
- Cost optimization strategies
- Standards for metadata, lineage, and governance
- Rules for data quality enforcement
- Operational safeguards against risks
These definitions form the enterprise blueprint, regardless of what technologies are used under the hood.
Dissecting Data Storage- 10 Sub-blocks
Here’s a practical look at the sub-blocks within Data Storage, why they matter, and examples of technologies used to implement them:
Component to Architect | Purpose / Why It Matters | Example Technologies |
Storage Layers / Zones | Structure data trust boundaries, data quality enforcement, and performance optimization | – Data Lake Zones (Raw, Processed, Curated)- Lakehouse Layers (Bronze, Silver, Gold) |
File Formats & Table Formats | Impact storage costs, query performance, schema enforcement, and time-travel capability | – Parquet, ORC, Avro- Delta Lake, Iceberg, Hudi |
Metadata Catalogs & Governance | Enable discovery, lineage tracking, security, and data quality enforcement | – AWS Glue Catalog- Apache Hive Metastore- Unity Catalog (Databricks)- Azure Purview- Collibra, Alation |
Access Controls & Security | Protect sensitive data, enforce compliance (GDPR, CCPA), and separate user roles | – IAM policies (AWS IAM, Azure RBAC)- Row-level security (Databricks, Snowflake)- Attribute-based access control |
Data Retention & Lifecycle Mgmt | Control costs by managing how long data remains in each zone/layer | – S3 Lifecycle Policies- Azure Blob Storage lifecycle rules- GCP Object Lifecycle |
Storage Cost Optimization | Balance between cheap storage and high-performance query costs | – Tiered storage (e.g. Glacier, Nearline)- Materialized views for aggregated data |
Data Quality Gates | Prevent low-quality data from propagating downstream | – Great Expectations- Deequ- dbt tests |
Data Lineage Tracking | Trace errors and support regulatory audits | – Unity Catalog Lineage (Databricks)- Azure Purview- Collibra Lineage- DataHub |
Streaming vs. Batch Storage | Support different ingestion patterns and real-time analytics | – Delta Lake Streaming- Kafka topics stored into S3- Kinesis Data Streams with S3 sinks |
Encryption & Privacy Controls | Comply with privacy laws and protect sensitive customer data | – Server-side encryption (S3 SSE-KMS)- Field-level encryption tools- Masking in Snowflake |
From Components to Blueprint: A Practical Example
Identifying the components of the Data Storage building block is only the first step. Enterprise architects must go further and define how these components come together as an actionable architecture.
Among all sub-blocks, one of the most foundational—and often most complex—is the design of storage layers or zones. How these layers are structured and managed has a profound impact on costs, governance, performance, and the success of marketing AI use cases.
The next section shows how to architect these layers in practice, with specific deliverables and examples.
How to Architect Storage Layers and Zones Sub-block
Modern data platforms provide technical capabilities for layering data (e.g. Bronze/Silver/Gold in Databricks). But enterprise architects must decide how those layers should be used for business, governance, and cost management.
Here’s how to go about architecting the Storage Layers/Zones sub-block, including specific deliverables, explained through a marketing AI example:
1- Define Layer Purposes and Trust Boundaries
Deliverable: A documented standard describing what data belongs in each layer and how it’s trusted.
Example:
- Bronze: Raw clickstream logs ingested from the website, containing potential duplicates and errors.
- Silver: Cleaned sessions with standardized fields, validated timestamps, and user IDs resolved.
- Gold: Aggregated daily user behavior metrics (page views, conversions) for marketing dashboards and LLM training datasets.
2- Establish Data Movement Rules Between Layers
Deliverable: ETL or ELT specifications that dictate:
- When data can move from Bronze to Silver
- Required validation checks
- Data enrichment or transformations
Example:
- Data cannot move from Bronze to Silver until:
- All required fields are non-null
- No duplicate session IDs exist
- Gold aggregates must only source data from Silver, ensuring traceability.
3- Define Retention and Archiving Policies
Deliverable: A policy document specifying:
- How long to keep raw vs. curated data
- When to archive or delete historical data
Example:
- Keep Bronze raw logs for 3 months to allow for reprocessing if bugs are discovered.
- Retain Silver for 12 months for historical analysis.
- Gold aggregates older than 18 months are archived to Glacier storage to reduce costs.
4- Specify Access and Security Controls by Layer
Deliverable: A matrix mapping user roles to permissible layers and operations (read, write, delete).
Example:
- Data engineers can read/write Bronze and Silver.
- Business analysts can only read Gold.
- PII in Bronze requires restricted access and masking in Gold.
5- Design Performance and Cost Optimization Strategies
Deliverable: Guidelines for partitioning, indexing, and aggregation for each layer.
Example:
- Partition Bronze data by ingestion date to speed up troubleshooting.
- Pre-compute popular metrics in Gold to avoid scanning large datasets during dashboard queries.
6- Document Lineage and Data Quality Standards
Deliverable: Standards for tracking data transformations and defining quality checks.
Example:
- Every field in Gold must map back to columns in Silver.
- Data quality tests required on Silver to catch anomalies like sudden spikes in click volume.
Bottom line:
Architecting storage layers/zones means defining far more than simply “using Databricks Bronze/Silver/Gold.” It involves creating detailed standards, policies, and deliverables that ensure the storage architecture:
- aligns with marketing AI business needs,
- manages costs effectively,
- protects data privacy, and
- ensures operational resilience.
Conclusion
Data Storage architecture for LLM repositories goes far beyond simply selecting a storage platform. It’s an architectural discipline involving decisions about zones, governance, cost optimization, data quality, and operational safeguards.
Enterprise architects must architect not only the technologies but also the principles, standards, and processes that ensure storage systems align with business goals, compliance requirements, and operational realities.
This post outlined the Data Storage building block as part of a broader enterprise blueprint for LLM data repositories. In the next two posts in this series, we’ll explore:
- Data Processing: Strategies for transforming, cleaning, and preparing data at scale for AI workloads.
- Feature Management: Architectures for delivering ML-ready features reliably and efficiently.
By the end of this series, you’ll have a practical, enterprise-level blueprint for designing LLM data repositories that are scalable, compliant, and business-aligned.
Let’s Architect This Together
Curious about the detailed steps for architecting the Data Processing and Feature Management building blocks? Or how these fit into your unique marketing AI ecosystem?
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