From Data Warehouses to Data Platforms
The world of data architecture is evolving — fast. What started as traditional data warehouses has now become a dynamic ecosystem of technologies, roles, and use cases. At Scalefree, we no longer talk exclusively about data warehouses — we intentionally use the term data platforms. Why? Because it’s not just the technology that has changed, but also the people working with data and how they use it to generate value.
In this article:
From Data Warehouses to Data Ecosystems
Traditional data warehouses were built for structured data with predefined schemas — relational, static, and stable. They were and still are the backbone for reporting and classic business intelligence in most cases.
The advent of data lakes offered a revolutionary capacity to house and manipulate unstructured data. However, the absence of clear structure and robust governance often resulted in environments colloquially known as “data swamps.”
Hybrid architectures and, later, data lakehouses emerged as a logical evolution, blending the strengths of warehouses and lakes. Their key benefit: enabling different data consumers to work on a unified foundation.
The New Reality: Platforms Instead of Silos
Today, multiple roles interact with data — and each has unique needs:
Data Engineers work across all architectural layers: from raw data ingestion to business rules and curated marts.
Business Analysts need structured, refined data for reports and dashboards.
Data Scientists explore raw, granular data for predictive models — often working directly with data lakes or raw vaults.
The traditional concept of a data warehouse no longer covers this variety of use cases. It’s simply not enough.
Why We at Scalefree Speak of Data Platforms
To us, Data Platform is not just a buzzword — it’s a strategic shift that reflects today’s real-world demands. A data platform needs to fulfill multiple criteria.
For example:
Neutrality
It’s not tied to specific technologies. Whether Snowflake, Databricks, or Coalesce — the concept stays relevant.
Flexibility
It supports any data architecture: from classic warehouses to lakes and lakehouses — and whatever comes next.
Role Inclusivity
All roles — engineers, analysts, scientists — can work on the same platform, using the same data, without structural or technical barriers.
Future-Readiness
New technologies can be adopted without redefining the concept of the platform itself.
AI Enablement
A modern data platform provides the foundation for AI and machine learning by making all relevant data — structured and unstructured — accessible, governable, and ready for advanced modeling.
Conclusion: Thinking in Platforms that serves EVERYONE
The world of data is no longer binary. It’s not just “reporting” vs. “analytics,” “structured” vs. “unstructured,” or “IT” vs. “business.”
By using the term Data Platform, we acknowledge this reality and offer a unifying concept that bridges technology, people, and innovation.
At Scalefree, we actively help shape this new world — using modern architectures, Data Vault 2.0, automation tools like dbt, Coalesce, and cloud-native platforms.
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Meet the Speaker
Lennart Busche
Consultant
Lennart is working in Business Intelligence and Enterprise Data Warehousing (EDW), supporting Scalefree International since the beginning of 2023 as a BI Consultant. Prior to Scalefree, he had over eight years of experience in the financial IT sector with focus on project management, IT-Service management and client management. This helped him get a broad knowledge of business requirements, the needs of customers dealing with IT and communication with different customer groups.
