Why structure data before integrating AI?

An LLM or ML model does not improve bad data quality. If data is fragmented across tools, uncleaned or lacks a common definition, the model will learn the inconsistencies. Data engineering upstream ensures AI works on a reliable foundation.

Which tools are used?

Python for collection and transformation, dbt for SQL transformations and data model documentation, Airflow or Prefect for orchestration, and PostgreSQL or BigQuery depending on context. The stack is chosen based on the existing setup and constraints.

Is it possible to start with very fragmented data?

Yes, that is precisely the most common case. The first step is always an audit to assess existing quality and structure. The work starts with the most critical sources for the priority use case, then extends gradually.

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Data engineering

Name: Pierre Kasparian - AI Integration & Data
Rating: 5

Centralising, cleaning and transforming data to make it finally usable.

PythonDBTSQLAirflow

What is it?

Data engineering means designing the pipelines that collect, transform and centralise raw data. It is the essential foundation before any AI integration: without clean, accessible data, LLMs and ML models cannot deliver reliable results.

How it works

1
Source audit
Mapping the data sources (SQL databases, APIs, files, SaaS tools) and identifying quality issues, duplicates and silos.
2
Pipeline architecture
Designing the target architecture: stack selection (dbt, Airflow, Prefect), data model, ingestion and transformation strategies.
3
Development and testing
Developing dbt transformations, Airflow DAGs and connectors. Unit tests and regression tests on the data.
4
Production monitoring
Deployment with pipeline error alerts, data freshness tests and technical documentation for the team.
5
Maintenance
Pipeline onboarding support, adjustments as data sources evolve and occasional support for new integrations.

What it covers

Reliable data collection and transformation pipelines, from zero to production
Modern tooling: Python, dbt, Airflow
From connecting new sources to preparing datasets for model training

Risks and compliance

Quality tests on duplicates, missing values, formats, volumes and schema breaks.
Processing logs to reconstruct a data point and explain an automated decision.
Personal data minimization before exposure to an AI model or external tool.
Separation of development, staging and production environments to reduce handling errors.

Related projects

Email Processing Pipeline - PrettoAutomatic analysis of emails exchanged between brokers and banks to extract up-to-date mortgage rates. Internal tools are then updated automatically.Quantified results2,000+ emails analysed / month+20% accuracy on market ratesRates updated in under 1 min

Frequently asked questions

Why structure data before integrating AI?: An LLM or ML model does not improve bad data quality. If data is fragmented across tools, uncleaned or lacks a common definition, the model will learn the inconsistencies. Data engineering upstream ensures AI works on a reliable foundation.
Which tools are used?: Python for collection and transformation, dbt for SQL transformations and data model documentation, Airflow or Prefect for orchestration, and PostgreSQL or BigQuery depending on context. The stack is chosen based on the existing setup and constraints.
Is it possible to start with very fragmented data?: Yes, that is precisely the most common case. The first step is always an audit to assess existing quality and structure. The work starts with the most critical sources for the priority use case, then extends gradually.

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