Data engineering, analytics and artificial intelligence for companies that need to decide with confidence.

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Why Aule

Your systems generate data all the time: sales, operations, customers, market. The difference between hoarding it and using it comes down to strategy, engineering and culture. That is exactly where Aule comes in.

Delivery record

Projects in production

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large enterprises served

Nestlé · BASF · Amaggi · Ambev · Aegea · Serasa Experian

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sectors of the economy

Food · Chemicals · Agribusiness · Beverages · Sanitation · Credit · See sectors

0%

less runtime for Spark jobs

Partitioning, join and AQE optimisation

0%

reduction in infrastructure cost

Azure · Databricks

What we do

All services

Data Engineering

Reliable pipelines, modern architectures (lakehouse, streaming) and data that is ready to use. The foundation of everything.

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Analytics & BI

Dashboards that answer business questions, trustworthy metrics and self-service analytics for every team.

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Artificial Intelligence

Predictive models, generative AI and intelligent automation applied where they generate measurable return.

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Governance & Culture

Data quality, security and cataloguing, plus the data-driven culture that makes all of it last.

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Aule product

A parameter-driven platform for Databricks. Every dataset becomes validated configuration, with loading, quality and auditing inside the customer's environment.

  • Customer workspace
  • 9 source formats
  • 4 load strategies
  • Quality before write
Meet Eclusa
dataset.config

Sources · 9 formats

  • CSV
  • JSON
  • PARQUET
  • DELTA
  • AVRO
  • XML
  • ORC
  • TEXT
  • EXCEL

Eclusa

one line per dataset

source.format
csv
load.strategy
merge
quality.severity
fail

Medallion architecture

  1. bronzeFaithful ingestion from source, audited per run
  2. silverTyped contract and quality applied before the write
  3. goldConsumption layer, modelled for analysis

Our team's experience on projects for

How we work

01

Diagnosis

Immersion in your business: we map sources, flows, pain points and opportunities. We come out with a plan prioritised by impact.

02

Foundation

We build the base: architecture, pipelines and data quality. Without a solid foundation there is no trustworthy analytics.

03

Intelligence

Dashboards, models and automations go into production. Deliveries every two weeks, visible value from the first month.

04

Evolution

We transfer knowledge, train your team and evolve the platform. Autonomy is part of the delivery.

Featured case · Nestlé

A modular PySpark framework on Delta Lake, streaming with Databricks Auto Loader and automated data quality: less rework, less cost, more trust in the data.

PROJECT RESULTS

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critical data available faster

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reduction in Spark job runtime

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less development effort

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faster incident detection

Azure · Databricks · PySpark · Delta Lake · Auto Loader

Insights

Technical reads and documented cases, with the architecture, the decisions and the method behind every number.

Technical article

Real-time does not mean streaming everywhere

Where to stop streaming, how to build idempotency at every boundary and five lessons from an airline ticketing platform in production.

Data engineering · Azure Databricks · Technical read

Documented case

A near real-time airline ticketing pipeline

From deeply nested XML events to nine analysis-ready business entities, with safe reprocessing, exact reconciliation and idempotency at every boundary.

Case · Aviation · Anonymous client

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Credentials

Four certifications, all verifiable.

  • Microsoft Certified: Azure Data Engineer Associate · DP-203
  • Microsoft Certified: Azure Fundamentals · AZ-900
  • Microsoft Certified: Azure Data Fundamentals · DP-900
  • Databricks Certified Data Engineer Associate
See stack and certifications

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