# looker Published articles for looker. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Empowering Data Through Self-Service: Behind the Scenes of Our Data Platform DevFeed: [Empowering Data Through Self-Service: Behind the Scenes of Our Data Platform]() Original publisher: [Read original article]() Author: Karen Fehmer Published: 2025-06-02T03:53:58Z Content type: article Language: en Sources: [Kogan.com]() Topics: [data](), [Architecture & Design](), [BigQuery](), [CI/CD](), [GitHub Actions](), [Git]() Tags: [architecture](), [bigquery](), [ci-cd](), [data](), [dbt](), [github-actions](), [looker](), [self-service](), [tableau]() ### AI overview Kogan.com describes replacing a request-based BI model with a governed self-service data platform built around BigQuery, dbt, Looker, and Acryl (DataHub). The architecture uses layered models, GitHub Actions CI/CD, testing, documentation, and controlled environments to support trusted analysis and reporting. ### Source excerpt At Kogan.com, our data needs have grown alongside the business. As more teams relied on insights to move quickly, it became clear our request-based BI model couldn't scale. We needed a platform that empowered teams to answer their own questions, trust the numbers, and move independently. That journey led us to build a self-service platform grounded in governance, transparency, and scalability--powered by dbt, Looker, and Acryl (DataHub). Rethinking Our BI Model We originally relied on Tableau. It served us well but had limitations: duplicated logic, inconsistent metrics, and limited collaboration with dbt. Tableau workbooks weren't version-controlled, which made maintaining consistency difficult. To bridge modeling and reporting, we often created extra presentation tables in dbt, adding complexity. We needed a platform that integrated tightly with dbt and supported governed exploration. A New Architecture: Modular, Transparent, Scalable We redesigned the platform around a clean, modular flow: Raw Sources -> BigQuery -> dbt -> Looker -> Acryl (DataHub) Our data transformations are built in dbt, where we follow a layered modeling structure. While we use stg_ (staging) and int_ (intermediate) models primarily for data cleaning and standardization, the marts_ models are the ones that power our analysis and reporting. These models contain our fact and dimension tables, fully aligned with business logic and ready for consumption in Looker. We've integrated CI/CD pipelines using GitHub Actions, and every change is tested before deployment. This includes dbt tests, schema validations, and model documentation to ensure confidence at every layer. Why Looker Was the Right Fit for Self-Service Looker offered a structured, governed approach that aligned with our dbt-first architecture. LookML let us centralize business logic, version it with Git, and deploy changes through CI/CD. With support for multiple environments (UAT and Production), we can test safely before releasing to users ## Configuring a Multi-Instance Looker Deployment DevFeed: [Configuring a Multi-Instance Looker Deployment]() Original publisher: [Read original article]() Author: Dennis Mutia Published: 2023-11-13T15:00:28Z Content type: tutorial Language: en Sources: [Engineering at Premise - Medium]() Topics: [dashboards](), [GitHub](), [Backstage](), [releases](), [Development](), [Deployment]() Tags: [backstage](), [business-intelligence](), [cloud-computing](), [dashboards](), [developer](), [developer-portal](), [development](), [gcp](), [github](), [looker](), [operational](), [production](), [releases]() ### AI overview This article explains how Premise configured three Looker instances to separate development from production for LookML and dashboards. It describes standardized data connections, view-only production access, GitHub-based synchronization and releases, Backstage-driven promotion, and Slack notifications. ### Source excerpt By Dennis Mutia, Software Engineer Image by ismagilov on Unsplash This article provides a high level overview of how we have configured multiple Looker deployments to separate development and production environments for both LookML and dashboards. Having a Looker deployment on a single instance has many challenges which include: a dashboard can get edited when someone else is presenting it to a client or a stakeholder someone testing a large dashboard can slow down Looker which will affect other dashboards Looker users cannot test the effects of model changes to their dashboards without having to request for developer access which has cost implications Multiple Looker instances We have set up 3 Looker instances: a development instance where LookML updates are made and dashboards created and reviewed before being released to production, and two production instances. One of the production instances is for internal looks and dashboards and the other for creating dashboards that can be shared with external clients. All three Looker instances have a standardized data connection to read from the same data warehouse. This is to ensure that dashboards and looks work the same access all environments. To ensure all development actually occurs on the development instance, both production instances enforce view only access for all users. This set up allows us to test both data models and dashboards without affecting production content. It also leads to standardized data models across all production instances. Linking development to production To take advantage of the benefits of multiple Looker instances, while limiting the operational maintance, we have linked all three looker instances using GitHub, and use GitHub Releases to update the production instances whenever changes have been tested and approved in development. To release dashboards from development to production we use Backstage, which is also our developer portal. We have created a template which dashboard builders