Original reporting sourced from computerweekly.com. Additional context and technical detail added by the BestDevOps editorial team.
From IT Stack to Data Infrastructure
The concept of the “IT stack” has evolved significantly. Where once it described software running across a mix of hardware resources — tied together by analytics tools, network topology controls, and data channels — organizations now talk about data infrastructure as a first-class architectural concern. Investment in data infrastructure has become a strategic conversation at every level of the business, not just within engineering teams.
It is in this context that Looker, the business intelligence (BI) and analytics platform acquired by and now part of Google Cloud, has shipped a release focused on what the company describes as foundational optimisation. The update targets application builders and developer services specifically, with upgrades spanning the development environment, marketing analytics integrations, and a new data discovery tool.
Looker Blocks for Google Marketing Platform
One of the headline additions is a set of new Looker Blocks for Google Marketing Platform. Looker Blocks are pre-built data models and dashboards designed to accelerate analytics development by providing a reusable, shareable foundation.
These new Blocks are purpose-built for Google Marketing Platform advertising and web data, and include:
- Interactive data exploration capabilities out of the box.
- Light machine learning (ML) predictions baked into the data models, enabling marketers to surface predictive insights without building custom ML pipelines.
- Direct links back to Google Marketing Platform, allowing analysts to act on insights by adjusting campaigns without leaving their analytics workflow.
- Paths for pushing insights back into Google Marketing Platform, enabling a feedback loop between analysis and campaign execution.
Colin Zima, head of Looker analytics and strategy at Google Cloud, has highlighted the ability to “customize data experiences” as central to this release, with deeper Google Cloud integration enabling solutions that were not previously practical.
Redesigned IDE for Model Developers
Looker’s built-in Integrated Development Environment (IDE) — used for authoring LookML data models — has been redesigned to improve developer workflows. Key improvements include:
- More intuitive workflows with advanced controls surfaced in a cleaner interface.
- An object browser that simplifies navigation across a project, making it easier to find, track, and reuse existing LookML objects such as views, explores, and dimensions.
- A single shared repository that can be shared across Looker instances, enabling teams to collaborate on model development without duplicating work.
- Project folders to improve organisation and make collaboration across large LookML projects more manageable.
Together, these changes aim to reduce the friction involved in building and maintaining complex LookML models, particularly in enterprise environments where multiple teams share a single Looker instance.
Developer Data Dictionary
Available through the Looker Marketplace, the new Data Dictionary is an application built using Looker’s extension framework. It provides a dedicated, purpose-built user interface that helps business users locate the fields and metrics they need for analysis — reducing their dependence on developers or data engineers for routine discovery tasks.
From a developer and governance perspective, the Data Dictionary also provides:
- The ability to audit all LookML fields across a project quickly.
- Visualisation of relationships between datasets and explores, helping teams understand data lineage and dependencies.
- Insight into which explores are used most frequently and which are redundant or duplicative — useful for maintaining a lean, well-governed LookML project.
Extension Framework Now Generally Available
Perhaps the most significant long-term development is the general availability of Looker’s extension framework. First announced in November as part of the Looker 7 release, the framework allows developers to build fully custom data experiences within Looker and embed those experiences into existing enterprise applications and workflows.
This positions Looker not just as a standalone BI tool but as a data platform and application development environment. Teams can build bespoke internal tools — dashboards, data explorers, self-service reporting apps — powered by Looker’s semantic layer and embedded directly into the products their users already use.
The Data Dictionary described above is itself an example of an application built on this framework, demonstrating the kind of tooling that becomes possible when analytics infrastructure is treated as a developer platform.
Key Takeaways for DevOps and Data Engineering Teams
- The redesigned LookML IDE reduces development friction for teams managing large or complex data models.
- Looker Blocks for Google Marketing Platform offer a reusable analytics foundation for organisations already invested in the Google advertising ecosystem.
- The Data Dictionary app improves data governance and self-service discovery, reducing analyst dependence on engineering teams.
- The generally available extension framework opens the door to treating Looker as an embedded analytics platform, not just a reporting tool.
- Shared repositories across instances are a meaningful improvement for organisations running multi-instance Looker deployments.