Unlike their software counterparts, data teams lack established methodologies for overcoming scalability challenges. This article offers a set of guiding principles to effectively scale your data platform while maximizing its business impact.
When working on software components, developers can leverage a wide range of frameworks, design patterns, and principles to scale their products and seamlessly adjust their architecture to support new use cases and handle increasing usage and complexity. This allows software engineering teams to ensure optimized performance and reliability as their platform (and its value) grows in scale.
Data teams, however, are not so fortunate. While the initial months of a data platform’s lifecycle are often marked by the excitement of tackling complex technical challenges and the joy of delivering a first wave of data products, what often follows is a daunting spiral of mounting complexity, rising costs, and diminishing returns.
Unlike other problems that we need to navigate as data teams, our scalability struggles are inherently different from the ones faced by software teams. In the data world, these struggles come in the form of unavoidable technical complexity (like mixing a multitude of patterns to move and transform data across an ever-growing list of systems) and the data platform’s unique positioning within the company (since eventually every business unit gets connected to it either directly or indirectly).
So, in this post-MDS world, where data teams are thoroughly scrutinized over their spending and continuously asked to showcase their value, it is more important than ever to define standards and tenets for successfully scaling a data platform. This article will focus on five principles critical to achieving this while also offering strategies for applying them.
In most cases, data platforms have the potential to be one of the company’s most valuable assets. However, in…