When designing data infrastructure for LLM fine-tuning, organizations often struggle with seemingly competing architectural choices. However, the reality is much simpler: successful LLM data repositories use a layered architecture with clear functionality separation between data storage and ML-related activities.
This post presents a technology blueprint for building LLM data repositories specifically suited for deploying AI in marketing use cases.
TL;DR
This post is intended for Marketing-focused Enterprise Architects looking to get a better understanding of the actual building blocks of an LLM data repository. For a generic overview of the proposed framework, please review part 1 of this sequel.
Two-Layer Architecture Framework
Think of an LLM data repository as consisting of two complementary layers:
Foundation Layer (Data Storage)
The base storage infrastructure that handles raw data ingestion, processing, and long-term retention. This layer serves multiple use cases beyond ML, including analytics, compliance, and reporting.
ML Workflow Layer
The specialized layer built on top of the foundation. This layer optimizes data for machine learning workflows, providing features like experiment tracking, model versioning, and production serving.
Foundation Layer: Raw Data Storage/Processing
Most organizations use a data lake, data lakehouse, or a combination of the two when it comes to storing raw data.
Option 1: Data Lake Architecture
Data lakes provide cost-effective, flexible storage for diverse data types, making them the starting point for most LLM data initiatives.
All data storage and processing occurs within 4 sub-layers.
Ingestion Layer
Handles the collection and initial intake of raw data from various sources into the data lake. Acts as the entry point for all data, managing different protocols, formats, and ingestion patterns (real-time vs batch).
Object Storage
Provides the foundational storage infrastructure for all data in the lake. Organizes data into zones, maintains metadata catalogs, and enforces security policies while offering scalable, cost-effective storage.
Processing Layer
Transforms, cleans, and prepares raw data for consumption. Handles data quality validation, format conversions, feature engineering, and large-scale distributed computing operations.
Consumption
Serves processed data to end users and applications. Supports diverse use cases including analytics, machine learning, reporting, and compliance through optimized data access patterns.
Technology Choices
Component | Technology Options |
Storage | AWS S3, Azure Data Lake Storage Gen2, Google Cloud Storage |
Processing | Amazon EMR, Azure Synapse, Google Dataproc, Apache Spark, Apache Flink |
Orchestration | Apache Airflow, AWS Step Functions, Azure Data Factory |
Metadata | AWS Glue Catalog, Azure Purview, Apache Atlas |
Option 2: Data Lakehouse Architecture
Data lakehouses combine the flexibility of data lakes with the performance and ACID guarantees of data warehouses, representing the evolution of data lake architecture.
From a technical standpoint, the key difference between the data lake and lakehouse lies the following three layers
Bronze Layer
- Raw data ingestion with minimal processing – stores data exactly as received from source systems with full fidelity and audit trail
Silver Layer
- Cleaned and validated data – applies data quality rules, deduplication, standardization, and schema enforcement for reliable consumption
Gold Layer
- Business-ready curated data – aggregated, enriched, and optimized datasets tailored for specific analytics and ML use cases with applied business logic
Data Lake vs Data Lakehouse for Marketing AI-Practical Scenario
A marketing team runs daily customer segmentation models and real-time personalization campaigns. They need both historical analysis and live customer behavior data.
Data Lake Challenge: During peak campaign periods, concurrent reads and writes create inconsistent customer profiles. The team might analyze a customer’s behavior while new clickstream data is being written, resulting in incomplete segments and poor personalization accuracy.
Data Lakehouse Solution: ACID transactions ensure marketing analysts always read complete, consistent customer snapshots. When the segmentation model runs at 9 AM, it sees either the complete previous day’s data or waits for the current write to finish – no partial reads. In addition to ACID properties, the lakehouse architecture enables time travel that lets marketers compare campaign performance across different data versions (“How did our Black Friday segments perform with November 15th data vs November 20th data?”), while schema evolution automatically handles new customer attributes without breaking existing campaigns.
Technology Choices
Platform | Description |
Databricks Lakehouse | Delta Lake with integrated ML capabilities and Unity Catalog |
Snowflake | Cloud data platform with native ML features |
Google BigLake | BigQuery integration with object storage |
AWS Lake Formation | Integrated data lake and analytics services |
ML Workflow Layer: Specialized ML Infrastructure
Once your foundation layer established, you need to decide how to optimize data access and management for ML workflows.
Option 1: Feature Store Architecture
Feature stores provide ML-optimized data management, focusing on feature consistency, sharing, and real-time serving capabilities.
Technology Choices
Platform | Description |
Databricks Feature Store | Integrated with MLflow and Unity Catalog |
AWS SageMaker Feature Store | Native AWS integration with real-time capabilities |
Google Vertex AI Feature Store | GCP-native with BigQuery integration |
Azure ML Feature Store | Integrated with Azure ML workspace |
Option 2: ML Platform Architecture
ML platforms provide comprehensive ML workflow management, focusing on experiment tracking, collaboration, and model lifecycle management.
Technology Choices
| Platform | Description |
| Weights & Biases (wandb) | Leading experiment tracking with dataset management |
| Neptune | Enterprise-focused MLOps platform with strong versioning |
| ClearML | Open-core platform with complete ML pipeline management |
| MLflow | Open-source with broad ecosystem support |
| DVC (Data Version Control) | Git-like versioning for ML datasets |
| Pachyderm | Data versioning and pipeline management |
| Labelbox | Focus on labeled dataset management and annotation |
| Scale AI | Managed data labeling and quality assurance |
Conclusion: Start Simple, Scale Smart
When it comes to building a data architecture for LLMs, the smartest move isn’t choosing one system over another—it’s layering the right components over time. Start with a robust data foundation (like a Data Lake or Lakehouse), then add ML-optimized layers such as a Feature Store or ML Platform as your needs evolve. This modular approach ensures flexibility, minimizes risk, and positions your organization to scale LLM initiatives efficiently as capabilities mature.
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