Analytics-as-Code

Analytics as Code is revolutionizing the work of data analysts. This approach overcomes quality issues related to the duplication and manual modification of dashboards and SQL queries. By adopting software development methodologies, it introduces modularity and versioning, significantly improving the quality and reproducibility of analyses.

The transition to code is essential but complex. A promising evolution is underway, with the emergence of markup-based semantic layers such as LookML and the shift from graphical ETL tools to code-first solutions like dbt. Although these advancements remain fragmented, they pave the way for a more consistent analytical experience.

Innovative companies are offering integrated solutions, including:

• Programmatic query languages for modular analysis (e.g., Malloy).
• Unified platforms for both data engineers and analysts (e.g., Holistics).
• VSCode extensions for visual creation and markup (e.g., Quary).

These tools have the potential to radically transform the analytical workflow, making it more efficient and collaborative. Some solutions excel in modularity but struggle to demonstrate a quick ROI, while others, though more comprehensive, lack the power of established systems.

Theodo’s point of view

We recommend exploring these technologies with interest but caution. Their potential is undeniable, but their maturity and integration into complex environments remain unproven. A careful evaluation is necessary to determine their added value compared to existing cloud solutions and modern data stacks.