Introduction
Streamz is the Flemish video-on-demand platform offering local and international series and films through a subscription-based model. Streamz focuses on delivering high-quality content tailored to local audiences, supported by a strategic partnership with Paramount.
Client
Streamz
Client since
2019
Solutions
The problem
At launch, Streamz needed immediate actionable insights into viewer behavior and subscriptions while simultaneously building a foundation for rapid growth. The challenge lay in transitioning from early, high-speed solutions to a professionalized platform capable of handling increasing data volumes and an evolving source landscape. Ultimately, the goal was to transform these initial time-sensitive setups into a scalable, cost-efficient architecture that provided the business with a single source of truth.
How we solved it
A pragmatic, business-first mindset
The collaboration followed a simple rule: start with business questions, not technology. By focusing on the insights needed to steer the company, Streamz and Datashift quickly mapped data sources to stakeholder needs and delivered immediate results. Manual processes were used initially to maintain speed, then systematically automated as the platform matured to ensure long-term scalability.
Step-by-step platform evolution
As Streamz entered its scale-up phase, the focus shifted toward robustness, scalability, and future-proofing. Datashift supported this transition by designing a cloud-native architecture on AWS, leveraging serverless services for maximum flexibility. By automating ingestion, transformation, and testing, the team ensured the infrastructure could grow alongside the business. This incremental approach guaranteed that every technological choice remained perfectly aligned with the organization’s maturity and evolving needs.
Modern analytics foundation
A major milestone was the introduction of dbt Cloud as the central transformation layer, enabling Streamz to utilize modular data models and embedded quality checks. This shift decoupled transformations from visualization, allowing Tableau to evolve into a pure exploration layer while dbt became the analytical backbone. This architecture improved transparency between technical and business teams and facilitated a seamless migration from Google Analytics to Snowplow, modernizing the stack and aligning all new transformations with dbt standards.
A pragmatic, business-first mindset
The collaboration followed a simple rule: start with business questions, not technology. By focusing on the insights needed to steer the company, Streamz and Datashift quickly mapped data sources to stakeholder needs and delivered immediate results. Manual processes were used initially to maintain speed, then systematically automated as the platform matured to ensure long-term scalability.
Step-by-step platform evolution
As Streamz entered its scale-up phase, the focus shifted toward robustness, scalability, and future-proofing. Datashift supported this transition by designing a cloud-native architecture on AWS, leveraging serverless services for maximum flexibility. By automating ingestion, transformation, and testing, the team ensured the infrastructure could grow alongside the business. This incremental approach guaranteed that every technological choice remained perfectly aligned with the organization’s maturity and evolving needs.
Modern analytics foundation
A major milestone was the introduction of dbt Cloud as the central transformation layer, enabling Streamz to utilize modular data models and embedded quality checks. This shift decoupled transformations from visualization, allowing Tableau to evolve into a pure exploration layer while dbt became the analytical backbone. This architecture improved transparency between technical and business teams and facilitated a seamless migration from Google Analytics to Snowplow, modernizing the stack and aligning all new transformations with dbt standards.
The results
By collaborating closely with the business, we delivered clear data models, baked in data quality from the start, and laid a strong AWS foundation that supports scalable growth and long-term value. Beyond delivery, we partnered with the organization to define where data drives the most value and to ensure the solutions are fully adopted and carried forward by internal teams.
< €1,000 per month
We were able to keep monthly cloud costs below €1,000 for two years, despite processing increasing data volumes (mainly clickstream data)
Company wide impact
The data platform now sits at the core of the organization, where data is not only surfaced in dashboards but is also actively used to impact operational processes, for example churn prevention.
Scalable foundation
A scalable foundation that enables developers to rapidly build new products within a reusable structure, while keeping platform and data product management costs under control.
Datashift helped us make the right decisions on technological tooling and platform architecture during the growth of Streamz and our data team.
Datashift helped us make the right decisions on technological tooling and platform architecture during the growth of Streamz and our data team.
Datashift brings deep expertise and actively contributes to the evolution of our data platform. Their commitment to helping Streamz move forward as an organization is invaluable.
Datashift brings deep expertise and actively contributes to the evolution of our data platform. Their commitment to helping Streamz move forward as an organization is invaluable.
Key Learnings
Start small but invest in a solid, scalable foundation
Work closely with business users, think carefully about the data model, and, if needed, extract the data directly from the sources early on—but don’t start building blindly without a clear direction.
Leverage cloud flexibility and scalability
In many initiatives, cloud resources are provisioned from day one to cover all potential use cases. Instead, use your data lake as the foundation and choose technology based on the context at that moment (data volume, scope, etc.).
Think about data quality and validation from the very start of your use case
Too many initiatives fail because end users do not trust or adopt the final output, or because changes in a rapidly evolving source landscape impact results and undermine business confidence. Technologies like dbt and Great Expectations significantly lower the technical barrier to implementing data quality and validation, but success also depends on clear ownership and well-defined processes for when things go wrong.
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