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Data Engineering

Data Integration is the process of combining data from different data sources (applications, files, messages and,….) into one unified data hub. We apply automation to accelerate this process and therefore use the open standards of Data Vault 2.0.

The purpose of a data warehouse is to bring your data together into one structured whole. This means that the integration must be flawlessly planned and executed, and a logical connection must be made to take the data out of their silos and provide a line of communication.

Data Warehouse Automation

Rather than losing time in linking and structuring data manually, we integrate all data sources inside and outside of your organization in a data hub by automating the model generation.

Integration Layer

We have developed an agile methodology with a “multiple speed approach”, allowing us to divide a Data Hub in two main parts. The first part is the integration of all data source systems, while the second part is used for the consumption of the data, i.e. analytics and applications. This enables you to meet your reporting needs, but also to establish a bidirectional communication between the sources.

Data Pipelines

Our Data Engineers build pipelines that transform raw data into ready-to-use formats for further analysis. Through flow management, a single change can be kept from having drastic repercussions throughout the pipeline.

Let's turn your data into actionable insights

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Our other solutions

Data Strategy
Align stakeholders, map business needs and available data in and outside an organization to find the perfect solution.
Data Architecture
Combine data from different data sources into one unified data hub using automation.
Data Engineering
Combine data from different data sources into one unified data hub using automation.
Analytics Engineering
Data Quality
Check whether the project matches expected requirements and ensure that there are no defects. Pinpoint errors or any missing requirements.
Data Visualization
Interactive applications and dashboards to facilitate the exploration and interaction with the data.
Create the intelligence your business requires by leveraging the results of the data pipeline processes.
Manage the entire ML lifecycle, including data preparation, model training, deployment, monitoring, and maintenance with MLOps.
Intensive training for both our and your employees to ensure they master the necessary skills and methodologies.