September 11, 2026 · Vistelio
LookML has Git. The dashboards, Looks, folders, boards and schedules your business actually opens do not. This tutorial shows how to export and import user-created Looker content between dev, QA and production instances with gzr and Looker Deployer, how to remap folders and models, and how to keep the whole thing in a repeatable pipeline.
Read moreSeptember 9, 2026 · Vistelio
Your LookML is in Git; your permission model probably is not. This tutorial shows how to define Looker instance configuration — permission sets, model sets, roles, groups, and user attributes — in a spec file, reconcile it idempotently with the Looker Python SDK, manage the stable parts in Terraform, and run a nightly drift check in CI.
Read moreSeptember 9, 2026 · Vistelio
Dashboards are read-only. The Extension Framework lets you ship a real React app inside Looker: this tutorial covers scaffolding, the manifest entitlements, the API 4.0 SDK, Looker Components, safe write-back via fetchProxy and Actions, and deploying the bundle through Git.
Read moreAugust 26, 2026 · Vistelio
Model Context Protocol is how AI assistants get tools. This tutorial stands up a Looker MCP server, connects Gemini CLI or Claude Desktop, walks through the tools it exposes, and covers the credentials, permissions and LookML shaping that decide whether the answers are trustworthy.
Read moreAugust 25, 2026
Looker API 3.x was removed in 2023. This refreshed guide covers API keys, logging in to API 4.0, the official looker_sdk Python SDK, run_inline_query and run_look, loading results into another system, and a migration map for scripts still calling /api/3.1.
Read moreAugust 25, 2026 · Vistelio
Private versus signed versus SSO embedding, generating signed embed URLs server side, the Embed SDK 2.x in the browser, user attributes for row-level security in embeds, and cookieless embedding for browsers that block third-party cookies.
Read moreAugust 25, 2026 · Vistelio
Build a chat-with-your-data feature on a Looker explore with Google's Conversational Analytics API: prepare the model with labels, descriptions and guardrails, create a data agent, ask questions from Python, and evaluate answer accuracy.
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