ETL / ELT
Batch and incremental transformation pipelines. Idempotence, checkpoints, and non-regression tests reduce risk at every delivery.
En clair : The same job produces the same result, day after day.
In plain terms
Prepare data for a precise use.
We industrialize ETL/ELT, streaming, and business rules with particular attention to reproducibility and performance. An unversioned process is debt: impossible to explain, hard to fix.
The ETL vs ELT choice depends on context: strong upstream control, or compute power carried by a lakehouse. We document assumptions so the architecture stays readable.
Enrichment draws on internal repositories, partners, geospatial data and AI models when the value justifies it. AI is never a shortcut to hide poorly prepared data.
On output, data is ready for APIs, dashboards, fusion, RAG or automation — with an explicit quality and latency contract.
ETL/ELTTransformation modes
StreamContinuous processing
VersionAudited rules
SLATarget latency
What we do
From raw to contextualized.
Transformation pipelines calibrated to the business need and execution mode.
Streaming
Continuous processing on events and telemetry. Time windows, aggregations and stream joins for latencies suited to operations.
En clair : The decision does not wait for tomorrow’s batch.
Business rules
Normalization, calculations, and versioned business checks. Rules are testable and audited like code.
En clair : Business reads the rule; technology executes it.
Geospatial
Geocoding, zones, trajectories and spatial contextualization. Useful for field ops, logistics, security and IoT.
En clair : Position becomes actionable information.
AI / ML enrichment
Entity extraction, scoring and preparation for RAG or agents. Models integrate as an observable step, with thresholds and fallback.
En clair : AI enriches; it does not replace governance.
Analytical preparation
Models ready for dashboards, APIs, and data products. Grain, historization, and semantics are aligned with consumers.
En clair : Dashboards no longer fight over how numbers are defined.
Frequently asked questions
Processing & enrichment — questions.
Do you prefer ETL or ELT?
Case by case: ETL when transformation must be controlled upstream; ELT when the lake/lakehouse already carries the compute power. Often both coexist on the same platform.
Do you integrate AI models?
Yes, to enrich or classify streams, in line with our AI & intelligent systems expertise. Outputs are scored, traced, and optionally human-validated.
How do you manage schema changes?
Versioned contracts, temporary dual-write if needed, compatibility tests, and consumer communication. A breaking change is a managed event—not a surprise.
Can existing jobs be reused?
Yes. We audit, encapsulate, and industrialize what is useful rather than rewriting everything.
How do you measure performance?
Processing time, throughput, cost, error rate and output freshness. Latency budgets are defined with the business.
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