Collection (ingestion)
We connect your sources (ERP, databases, spreadsheets, APIs, sensors) and bring the data in automatically.
Data & Intelligence
Data Engineering
Before charts and insights, you need a well-built foundation. We design and implement your company’s entire data foundation (collection, integration, processing and storage), building reliable pipelines that deliver the right data, to the right place, at the right time.
The pain points we solve
Is your data working against you?
When information is scattered and messy, decision-making becomes guesswork. Data engineering fixes that foundation so you can trust every number.
- Data scattered across multiple systems (ERP, spreadsheets, legacy systems) that don’t talk to each other
- Reports that don’t match because each department has “its own version” of the number
- Manual integrations that break all the time and eat up your team’s time
- Data takes too long to arrive, and when it does it’s already out of date
- Without a reliable foundation, you can’t trust your BI or use AI
How it works
From raw data to decisions
We build an automated path that takes data from its source to the people who make decisions.
Processing
We clean, standardize and combine the data (ETL/ELT) so it becomes reliable information.
Storage
We store everything in a data lake and/or data warehouse, organized, secure and ready to use.
Consumption
The data feeds BI dashboards, reports and artificial intelligence models.
No jargon
Data lake and data warehouse, made simple
Two ways to store data. The difference is easy to grasp with an analogy.
Data Lake (a lake of data)
Picture a huge reservoir where you pour every kind of data, just as it comes: spreadsheets, photos, videos, text, machine logs. No need to organize it first; it keeps everything, it’s inexpensive and it holds massive volumes.
It’s like a storage unit: everything fits, and you organize it when you need it.
Data Warehouse (a warehouse)
Now picture a fully labeled, tidy cabinet, with everything in its place. The data arrives already processed and organized, ready for fast queries and instant reporting.
It’s like an organized bookshelf: everything labeled and easy to find.
In practice, we use both together: the lake stores everything (inexpensive and flexible) and the warehouse delivers BI-ready data. When we combine the two into a single architecture, we call it a Lakehouse.
Going deeper
The technical layer
For those who want to understand what runs under the hood.
Lake: schema-on-read
Object storage (S3, ADLS Gen2) with open formats such as Parquet and Delta. You store the raw data and define the schema only when reading it: maximum flexibility at low cost.
Warehouse: schema-on-write
Columnar database optimized for analytical SQL (Synapse, Redshift, BigQuery, Snowflake). Data comes in already modeled and indexed, for fast, consistent BI queries.
ETL vs ELT
ETL: transform before loading. ELT: load the raw data and transform it inside the platform (more common in a Lakehouse, leveraging the power of Spark/SQL).
Lakehouse architecture · Medallion layers
With each layer the data becomes more reliable, from raw (Bronze) to decision-ready (Gold).
Where we build
Platforms we master
We choose the right stack for your situation, from the cloud you already use to a modern Lakehouse.
Microsoft’s cloud, integrated with the ecosystem your company already uses.
- Azure Data Factory (ingestion)
- Data Lake Storage (ADLS Gen2)
- Synapse Analytics
- Microsoft Fabric
The Lakehouse platform, combining the flexibility of a lake with the governance of a warehouse.
- Lakehouse + Delta Lake
- Apache Spark (processing)
- Bronze / Silver / Gold layers
- Unity Catalog (governance)
Amazon’s cloud, robust and scalable for any data volume.
- Amazon S3 (data lake)
- AWS Glue (ETL)
- Amazon Redshift (data warehouse)
- Amazon Athena (queries)
Ready for a data foundation you can trust?
Tell us where your data stands today and we’ll design the right architecture for your business together.
