Data
Data you can
build on
We design and build data platforms your business can trust, starting from the question you need answered, not from the tool.
Talk to usIn practice
New data in
less than a day
At Essent, we improved ingestion into Snowflake and built a Python tool to speed up dbt development. Their data ingestion team can now make new data available in less than a day.
Read the Essent caseArchitecture and strategy
A clear target
before we build
Many data problems start before the first pipeline: an unclear goal, a platform picked for its name, rules nobody agreed on. We help you settle those first, whether you are moving legacy systems to the cloud, setting up a data mesh, or giving business users self-service BI and GenAI-assisted analysis.
Vision and goals
What the platform must do for the business, in terms everyone signs off on. It keeps every later design choice pointed the same way.
Principles and patterns
The rules behind the design, and proven patterns such as layered, event-driven or domain-driven architectures. They keep the solution consistent and let teams move faster with less risk.
Implementation and governance
Clear roles, standards and a way to control change, so the platform stays on course after go-live and keeps up as your needs change.
Data engineering
Pipelines and models
people rely on
We build the pipelines and models that turn raw data into something people use, and we automate and test them from the start.
Cloud platforms
Store and process data, run pipelines and development environments without managing servers, and scale up or down as needed.
Automation
Automated ingestion and transformation free your team for analysis, and make data products more accurate and reliable.
Transformation and integration
Combining sources and cleaning messy datasets is what turns raw information into something you can act on.
Testing and data quality
Tests and quality checks, with Great Expectations among others, keep pipelines reliable in production. Monitoring catches issues before your users do.
Data modeling
Structured models give the organization a common language and a blueprint for databases, pipelines and reporting.
Ready-for-AI data
AI is only as good as its input. We make sure your data is correct, complete and in a shape AI models can use.
Our AI offeringThe tools we use every day
Snowflake
dbt
Databricks
Spark
Airflow
Python
AWS
Azure and Fabric
Great Expectations
Docker
Data cases
Work we can show
From raw screening runs to self-service research data
For a pharma research team, we automated the path from screening runs to validated results, on Snowflake, dbt and Airflow.
Processing both medical and commercial sales data from multiple countries
Our team was tasked with processing both medical and commercial sales data from multiple countries to generate actionable recommendations for sales representatives. This initiative was designed to enable targeted outreach and improve overall sales effectiveness. This project presented several complex challenges that required innovative solutions and seamless collaboration across teams.
From Synapse to Fabric: How Glowi Modernized Its Data Architecture
Glowi explored migrating from Microsoft Synapse to Microsoft Fabric, the new data platform by Microsoft. This should be able to replace their older synapse system. Together with Glowi, we created the basis of their Fabric infrastructure, ensuring that they are future-proof and ready to get started with this new platform. We used our expertise to make sure the setup and infrastructure fits the needs and expectations of Glowi.
Insights
Written by the team
New job, new cert, new exam version: my SnowPro Core C03 experience
Data engineer Joline prepared for and passed the newly updated SnowPro Core C03 exam within her first two months. How she studied, and what changed since C02.
Explain Snowflake EXPLAIN: Catching Join Explosions Before They Cost You
Stop reacting to massive Snowflake bills after a query has already failed or stalled. Digital Hive shares how implementing the EXPLAIN command during code review shifts your data engineering from reactive debugging to proactive cost prevention. Reach out to Digital Hive to find out how our data team can optimize your pipelines and eliminate expensive cloud waste.
The Dashboard Migration Button Won't Save Your Snowflake Credits
Is your Snowflake dashboard migration just moving the "compute burn" to a new format? Digital Hive explains why Snowflake’s native conversion button is only phase one of a sustainable migration strategy. See our guide on reducing credits with Streamlit and reach out to Digital Hive to see how we can optimize your entire data stack.
Contact
Let’s talk about
your challenge
- You tell us the problem. A few lines is enough.
- A senior consultant replies within three business days.
- We meet and tell you honestly what we would do.
Prefer email or phone?
[email protected] · +32 11 140 236