Data Infrastructure for AI: Vector Databases, ETL Pipelines, and Data Quality in Depth
Every AI project stands or falls with its data. Not with the model -- with the infrastructure that provides, transforms, and quality-assures data. Connect GPT-4 or Claude to a chaotic data foundation, and you get chaotic results. Invest in clean pipelines, well-designed embedding strategies, and measurable data quality, and you build AI systems that survive in production.
This article is for CTOs, IT leaders, and technical decision-makers who are building or modernizing their data infrastructure for AI applications. We cover the three pillars: vector databases, ETL pipelines, and data quality frameworks -- with concrete comparisons, architecture decisions, and practical experience from our project work at IJONIS.
Why Data Infrastructure Is the Limiting Factor for AI
Most AI projects do not fail because of model quality. They fail because of the data foundation. According to a Gartner study, data engineering teams spend up to 80% of their time on data cleaning and integration -- before the first AI component even comes into play.
The root cause: enterprise data is fragmented. It lives in ERP systems, SharePoint folders, email inboxes, legacy databases, and Excel files. Without a dedicated infrastructure layer that consolidates, cleans, and converts these sources into a format usable by AI models, every LLM project remains an experiment.
Three infrastructure layers are critical:
- Storage Layer -- Where and how is data stored for AI access? This is where vector databases come in.
- Transformation Layer -- How does data flow from source systems into the storage layer? This is the domain of ETL pipelines.
- Quality Layer -- How do you know the data is current, complete, and correct? Data quality frameworks provide the answer.
For AI agents and RAG systems, this infrastructure is not an optional improvement -- it is the prerequisite for productive results.
Vector Databases: The Backbone of Semantic AI Search
Why Relational Databases Are Not Enough
Traditional databases store data in tables and answer exact queries: SELECT * FROM products WHERE name = 'Widget'. This works for structured data. But AI applications ask different questions: What is the most similar document to this customer inquiry? Which internal policies are relevant to this contract?
Vector databases store data as high-dimensional vectors -- numerical representations (embeddings) that encode semantic meaning. Instead of exact matches, they return the nearest neighbors in vector space: documents that are semantically similar, even if they use different words.
Architecture of a Vector Database Integration
A typical integration into an AI system consists of four components:
- Embedding Model: Converts text, images, or code into vectors (e.g., OpenAI
text-embedding-3-large, Cohere Embed, or open-source models likee5-large-v2) - Vector Database: Stores vectors and enables efficient nearest-neighbor search
- Indexing: Algorithm for fast similarity search (HNSW, IVF, PQ)
- Metadata Filtering: Combining semantic search with structured filters (e.g., by department, date, document type)
Vector Database Comparison: Pinecone vs. pgvector vs. Qdrant vs. Weaviate
The choice of vector database depends on your existing stack, scaling requirements, and operational model. Here is a detailed comparison of the four most relevant options:
Our Recommendation at IJONIS
- For a quick start: Pinecone. No infrastructure overhead, fast integration, reliable managed service. Ideal when you lack ops capacity and need results fast.
- For existing PostgreSQL setups: pgvector. No new database needed, proven operational model, SQL compatibility. For most mid-market enterprises, this is the most pragmatic entry point.
- For maximum control and performance: Qdrant. Open source, excellent latency, full data sovereignty with self-hosting. First choice when sub-30ms latency is business-critical.
- For hybrid search scenarios: Weaviate. Native BM25 integration, GraphQL API, and strong multi-tenancy capabilities. Particularly suited when you want to combine vector and keyword search without separate systems.
Embedding Strategies: More Than Just Converting Text to Vectors
The quality of your vector database stands and falls with the embedding strategy. Three aspects are critical:
1. Chunk Size and Strategy
Large documents must be split into chunks before embedding. Chunk size directly affects retrieval quality:
- Chunks too small (< 200 tokens): Lose context, deliver fragmented results
- Chunks too large (> 1,000 tokens): Dilute semantic precision
- Optimal range: 300--500 tokens with 50--100 token overlap
Advanced strategies use hierarchical chunking: a parent chunk provides context, child chunks provide precision. The parent-document retrieval pattern retrieves the small chunk for search but delivers the larger context chunk to the LLM. For detailed chunking strategies, see our article on RAG systems.
2. Model Selection
Not every embedding model suits every use case:
For GDPR-sensitive applications, self-hosting is critical. Models like e5-large-v2 or BGE-M3 run comfortably on a single GPU and deliver strong results for European-language content.
3. Metadata Enrichment
Pure vector search is rarely sufficient. Metadata enables hybrid queries:
- Document type: Search only in contracts, not in emails
- Timestamp: Only consider current documents
- Department / area: Access control via metadata filters
- Language: Filter DE/EN documents separately
ETL Pipelines: Automating Data Flows for AI
Why Manual Data Integration Does Not Scale
Many companies start AI projects with manual data exports: CSV files from the ERP, copy-paste from SharePoint, ad-hoc scripts that transform data. This works for the prototype. In production, three problems emerge:
- Data staleness: Manual exports are snapshots. Your AI works with outdated data.
- Error-proneness: Every manual step is a potential source of error.
- Not reproducible: If the colleague who did the export is on leave, the pipeline stops.
ETL pipelines (Extract, Transform, Load) solve these problems: they automate the data flow from source systems into your AI infrastructure -- reliably, traceably, and at scale.
Modern ETL Architecture for AI Systems
A production-ready ETL pipeline for AI applications consists of four layers:
Extract -- Pull Data from Source Systems
- Database connectivity (PostgreSQL, MySQL, MSSQL) via Change Data Capture (CDC)
- API integration for SaaS tools (Salesforce, HubSpot, SharePoint)
- File-based extraction (SFTP, S3, local directories)
- Web scraping for publicly accessible data
Transform -- Prepare Data for AI
- Text extraction from PDFs, Word documents, emails (Apache Tika, Unstructured.io)
- Text cleaning: encoding issues, special characters, duplicates
- Structuring: converting unstructured text into defined formats
- Embedding generation: converting text chunks into vectors
Load -- Write Data to Target Systems
- Vector database (Pinecone, pgvector, Qdrant, Weaviate)
- Data warehouse for analytical queries (BigQuery, Snowflake)
- Knowledge graph for relationships (Neo4j)
Orchestrate -- Control and Monitor Pipelines
- Scheduling: When do which pipelines run?
- Dependencies: Pipeline B starts only when Pipeline A succeeds
- Alerting: Notification on failures
- Monitoring: Runtimes, data volumes, error rates
Tool Comparison: Airflow vs. dbt vs. Prefect
In practice at IJONIS, we frequently combine Apache Airflow for orchestration with dbt for SQL transformations. For smaller projects or purely Python-based pipelines, Prefect is a lean alternative. The choice always depends on the existing stack and team competencies.
ELT Instead of ETL: The Modern Approach
Increasingly, ELT (Extract, Load, Transform) is gaining ground: raw data is first loaded into a data warehouse, then transformed there. Advantages:
- No data loss: Raw data is preserved, transformations are repeatable
- Flexibility: New transformations without re-extraction
- Performance: Warehouses like BigQuery or Snowflake are optimized for transformations
For AI pipelines, we recommend a hybrid approach: ELT for structured data (database contents, CRM data), classic ETL for unstructured data (documents, emails) that must be transformed into embeddings before loading.
Data Quality: Measurable Data Quality as the AI Foundation
Why Data Quality Is More Critical for AI Than for BI
Business intelligence systems tolerate certain data quality problems -- a missing value in a dashboard is noticed and corrected manually. AI systems are less fault-tolerant: an LLM working on outdated or contradictory data produces hallucinated answers that sound plausible but are wrong.
The risk is directly proportional to the degree of automation. When an AI agent makes automated decisions, data quality becomes a safety issue.
The Five Dimensions of Data Quality
A robust data quality framework measures quality along standardized dimensions. The following five dimensions are particularly critical for AI systems:
Each dimension needs measurable thresholds. Example: completeness > 95%, timeliness < 24 hours, duplicate rate < 1%. Without these thresholds, you have no basis for alerting and no criteria for releasing data to AI systems.
Data Quality Framework in Practice
A production-ready data quality framework comprises three levels:
Level 1: Schema Validation (Automated)
- Check data types (string, integer, date)
- Verify mandatory fields
- Value range checks (e.g., prices > 0)
- Format validation (email, phone number, IBAN)
Level 2: Business Rules (Configurable)
- Referential integrity (customer ID exists in customer table)
- Temporal consistency (delivery date after order date)
- Plausibility checks (invoice amount < EUR 1,000,000)
- Cross-source validation (ERP data matches CRM)
Level 3: AI-Specific Quality Metrics
- Embedding quality: cosine similarity distribution of chunks
- Retrieval precision: does the search return relevant results?
- Chunk coverage: are all relevant documents indexed?
- Freshness SLA: maximum age of data in the vector database
Tools for Data Quality
- Great Expectations: Open-source framework for data validation with declarative expectations. Integrates seamlessly with Airflow and dbt.
- dbt Tests: SQL-based data tests, integrated into the transformation pipeline. Ideal for referential integrity and business rules.
- Soda: Data quality checks as YAML configuration, SaaS or self-hosted. Beginner-friendly with good visualization.
- Monte Carlo: Data observability platform, automatically detects anomalies. Worthwhile from medium complexity upward.
Data Governance: Organizational Frameworks
Technology alone is not enough. Data infrastructure requires organizational governance -- without it, data silos, accountability gaps, and compliance risks emerge.
Data Ownership
Every dataset needs a responsible owner -- not IT, but the business department that creates and uses the data. The owner decides on quality standards, access rights, and retention periods. Without clear ownership, data quality problems become blame games instead of solutions.
Data Catalog
A central directory of all data sources, schemas, and quality metrics. Tools like DataHub, Atlan, or Amundsen make data landscapes searchable. For AI systems, the data catalog is the starting point for every new integration: what data exists, who is responsible, what is its quality?
Access Control
Which AI agent may access which data? Row-level security and metadata-based filters prevent sensitive data from reaching the wrong contexts. Particularly relevant for RAG systems, where the vector database must control access at the document level.
Lineage Tracking
From source through ETL transformations to embedding -- every change must be traceable. This is not just best practice; for the EU AI Act, it is a requirement. When an AI system makes a decision, it must be documented which data, at what quality level, formed the basis for that decision.
For enterprises deploying process automation with AI, data governance is not a bureaucratic exercise but the foundation for trust in automated decisions.
Architecture Blueprint: Data Infrastructure for AI Systems
In summary, the following reference architecture emerges, which we at IJONIS use as a starting point for client projects:
Layer 1 -- Source Systems: ERP, CRM, SharePoint, email, file systems, APIs
Layer 2 -- Ingestion: Change Data Capture, API connectors, file watchers, web scrapers
Layer 3 -- Transformation: dbt models (structured), Unstructured.io + embedding model (unstructured)
Layer 4 -- Storage: Data warehouse (structured) + vector database (embeddings) + knowledge graph (relationships)
Layer 5 -- Access: RAG pipeline, AI agents, dashboards, APIs
Layer 6 -- Quality & Governance: Great Expectations, monitoring dashboards, data catalog, lineage tracking
This blueprint is not a rigid schema. It is adapted for each project to the existing IT landscape, regulatory requirements, and scaling goals.
FAQ: Data Infrastructure for AI
Do I need a vector database, or is my existing PostgreSQL instance sufficient?
If you already use PostgreSQL, pgvector is the simplest entry point -- no new database, proven operational model. For up to a few million vectors, pgvector delivers adequate performance. For higher requirements in latency (< 30 ms), scale (billions of vectors), or advanced filtering capabilities, switching to a dedicated solution like Qdrant or Weaviate is worthwhile. Pinecone is the right choice if you want zero operational overhead.
How long does it take to build an AI data infrastructure?
Expect 4--8 weeks for a production-ready base infrastructure: vector database, initial ETL pipelines, and basic quality checks. More complex setups with multiple source systems, data governance, and comprehensive monitoring require 3--6 months. At IJONIS, we start with an analysis phase that delivers a roadmap in 2 weeks.
Which data should I load into the vector database first?
Start with the data that holds the highest business value for your first AI use case. Typically, these are internal knowledge documents (manuals, SOPs, policies) that serve as the foundation for a RAG system. Avoid trying to index everything at once -- start focused and expand iteratively.
How do I ensure my data is GDPR-compliant in the vector database?
Three measures are critical: First, anonymize or pseudonymize personal data before embedding. Second, host the vector database in the EU (self-hosted or EU cloud region). Third, implement a deletion concept -- when a data subject requests deletion of their data, the associated vectors must also be deletable. Note: vectors are not trivially traceable back to source data, which is why a clean mapping table between source records and vector IDs is mandatory.
What does a production-ready data infrastructure for AI cost?
Costs vary significantly depending on scope. A benchmark: for a mid-market setup with a vector database, ETL pipelines, and basic monitoring, expect EUR 1,500--5,000/month in infrastructure costs. Add one-time setup costs for architecture, implementation, and integration. The ROI comes from the quality and speed of your AI applications -- poor data infrastructure is the most expensive mistake you can make.
Conclusion: Data Infrastructure Is Not Overhead -- It Is the Foundation
The temptation is great to start directly with the LLM prototype and retrofit the data infrastructure later. In practice, this does not work. Without clean ETL pipelines, your AI works with outdated data. Without a well-designed embedding strategy, semantic search returns irrelevant results. Without quality metrics, you do not know when the data is no longer correct.
Data infrastructure is not a cost factor -- it is the multiplier for the ROI of your entire AI strategy. Companies that invest here scale their AI applications. Companies that cut corners here build one AI project after another that never gets beyond the prototype.
Ready to build your data infrastructure for AI? Talk to our experts -- we analyze your data landscape and develop an architecture that makes your AI systems production-ready.
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