Your Data Engineering partner

Merlin is the largest digital music licensing partner for independent labels, distributors, and other rightsholders. Merlin’s members represent 15% of the global recorded music market. The company has deals in place with Apple, Facebook, Spotify, YouTube, and 40 other innovative digital platforms around the world for its’ member’s recordings. The Merlin team tracks payments and usage reports from digital partners while ensuring that their members are paid and reported to accurately, efficiently, and consistently.

An essential part of Otis's business operations is the maintenance of their elevators. To time this effectively and proactively inform customers about the status of their elevator, Otis wanted to implement continuous monitoring. They saw great potential in predictive maintenance and remote maintenance.

Our consultants log the assignments they take on for our clients in our ERP system AFAS. In our CRM system HubSpot, we can see all the information relevant before signing a collaboration agreement. When we close a deal, all the information from HubSpot automatically transfers to AFAS. So, HubSpot is mainly used for the process before entering a collaboration, while AFAS is used for the subsequent phase. To tighten our people's planning and improve our financial forecasts, we decided to set up a data warehouse to integrate data from both data sources.

Agrico is a cooperative of potato growers. They cultivate potatoes for various purposes such as consumption and planting future crops. These potatoes are exported worldwide through various subsidiaries. All logistical and operational data is stored in their ERP system, Microsoft Dynamics 365. Due to the complexity of this system with its many features, the data is not suitable for direct use in reporting. Agrico asked us to help make their ERP data understandable and develop clear reports.

One of the largest online shops in the Netherlands wanted to develop a standardised way of data processing within one of its data teams. All data was stored in the scalable cloud data warehouse Google BigQuery. Large amounts of data were available within this platform regarding orders, products, marketing, returns, customer cases and partners.

The National Road Traffic Data Portal (NDW) is a valuable resource for municipalities, provinces, and the national government to gain insight into traffic flows and improve infrastructure efficiency.

Valk Exclusief is a chain of 4-star+ hotels with 43 hotels in the Netherlands. The hotel chain wants to offer guests a personal experience, both in the hotel and online.

TM Forum, an alliance of over 850 global companies, engaged our company as a data partner to identify and solve data-related challenges.

Stichting Donateursbelangen aims to strengthen trust between donors and charities. They believe that that trust is based on collecting money honestly, openly, transparently and respectfully. At the same time effectively using the raised donation funds to make an impact. To further this goal, Stichting Donateursbelangen wants to share information about charities with donors through their own search engine.

Dedimo is a collaboration of five mental healthcare initiatives. In order to continuously enhance the quality of their care, they organize internal processes more efficiently. Therefore, they use perceptions from the data that is internally available. Previously, they acquired the data themselves from different source systems with ad hoc scripts. They requested our help to make this process more robust, efficient and to further professionalise it. They asked us to facilitate the central storage of their data, located in a cloud data warehouse. The goal was to set up the data infrastructure within this environment, since they were already used to working with Google Cloud Platform (GCP).

At Royal HaskoningDHV, the number of requests from customers with Data Engineering issues continue to climb. The new department they have set up for this, is growing. So they asked us to temporarily offer their Data Engineering team more capacity. One of the issues we offered help with involved the Aa en Maas Water Authority.

FysioHolland is an umbrella organisation for physiotherapists in the Netherlands. A central service team relieves therapists of additional work, so that they can mainly focus on providing the best care. In addition to organic growth, FysioHolland is connecting new practices to the organisation. Each of these has its own systems, work processes and treatment codes. This has made FysioHolland's data management large and complex.

The Neuron provides a programmatic bidding platform to plan, buy and manage digital Out-Of-Home ads in real-time. They asked us to predict the number of expected impressions for digital advertising on billboards in a scalable and efficient way.

RNW Media is an NGO that focuses on countries where there is limited freedom of expression. The organisation tries to make an impact through online channels such as social media and websites. To measure that impact, RNW Media drew up a Theory of Change (a kind of KPI framework for NGOs).

Fietsvoordeelshop has seven physical stores and a webshop. It is one of the most successful bike shops at the moment. In the field of data, the bicycle shop mainly worked with Excel. This means that a lot was done manually, every week. Due to the growth of the organisation and the increasing number of processes, the Excel file became increasingly large and unclear. Fietsvoordeelshop asked us to demonstrate with a data pressure cooker of five days that data-driven work could be of added value.

Data projects often need to deliver results quickly. The field is relatively new, and to gain support, it must first prove its value. As a result, many organisations build data solutions without giving much thought to their robustness, often overlooking data quality. What are the risks if your data quality is not in order, and how can you improve it? Find the answers to the key questions about data quality in this article.

Since the field of big data emerged, many elements of the modern data stack became the data engineers' responsibility. What are these elements, and how should you build your data team?

The world of data is continuously shifting and so are its corresponding jobs and responsibilities within data teams. With this, an up-and-coming role appeared on the horizon: the Analytics Engineer.

Bringing machine learning models to production has proven to be a complex task in practice. MLOps assists organisations that want to develop and maintain models themselves in ensuring the quality and continuity. Read this article and get answers to the most frequently asked questions on this topic.

Working in a data-driven way helps you make better decisions. The better your data quality, the more you can rely on it. A good data architecture is a basic ingredient for data-driven working. In this article, we explain what a data architecture is and what a Data Architect does.

Basically, Data Engineers work on data pipelines. These are data processes that can retrieve data from a certain place and write it in somewhere. In this article you can read more about how data pipelines work and discover why they are so important for a solid data infrastructure.

A Data Engineer is someone who feels completely at home in seas of data and the technical engineering required to convert that data into meaningful signals and insights.

It is now clear to most companies: data-driven decisions by Data Science add concrete value to business operations. Whether your goal is to build better marketing campaigns, perform preventive maintenance on your machines or fight fraud more effectively, there are applications for Data Science in every industry.

Based on our know-how, the purpose of this blog is to transmit our knowledge and experience to the community by describing guidelines for implementing a data platform in an organisation. We understand that the specific needs of every organisation are different, that they will have an impact on the technologies used and that a single architecture satisfying all of them makes no sense. So, in this blog we will keep it as general as we can.

Infrastructure as Code has proven itself as a reliable technique for setting up platforms in the cloud. However, it does require an additional investment of time from the developers involved. In which cases does the extra effort pay off? Find out in this article.

In the article, we start by discussing Serverless Functions. Then we demonstrate how to use Terraform files to simplify the process of deploying a target infrastructure, how to create a Function App in Azure, the use GitHub workflows to manage continuous integration and deployment, and how to use branching strategies to selectively deploy code changes to specific instances of Function Apps.

This article provides a comparison between these two managed services Cloud Composer & MWAA. This will help you understand the similarities, differences, and factors to consider when choosing them. Note that there are other good options when it comes to hosting a managed airflow implementation, such as the one offered by Microsoft Azure. The two being compared in this article are chosen due to my hands-on experience using both managed services and their respective ecosystems.

This article explains the essence of Kubernetes Event Driven Autoscaling (KEDA). Subsequently, we configure a local development environment enabling the demonstration of KEDA using Docker and Minikube. Following this, we expound upon the scenario that will be implemented to showcase KEDA, and we guide through each step of this scenario. By the end of the article, you will have a clear understanding of what KEDA entails and how they can personally implement an architecture with KEDA.