2026-10-06
Choosing a Semantic Layer for AI: Comparing dbt, Looker, and Omni
Compare dbt, Looker, and Omni as foundations for AI analytics, focusing on existing models, metric access, permissions, testing, and operating ownership.
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Build the data foundation for reliable AI
Practical guides to semantic layers, AI data agents, and analytics engineering with Looker, dbt, and Omni.
2026-10-03
Build an AI Reporting Agent That Explains KPI Changes—with Evidence
Design an AI reporting agent that calculates changes, checks data quality, identifies contributing segments, and separates observations from unproven causes.
Read post →2026-09-30
When Business Definitions Change: Keeping Your Semantic Layer and AI in Sync
Manage changes to business metrics across semantic models, AI tools, examples, caches, and reports with versioning, parallel checks, and clear ownership.
Read post →2026-09-27
AI Agents and Data Permissions: Enforcing Who Can See What
Design data access for AI agents across authentication, query tools, retrieval, caches, and logs, with concrete tests for tenant and user boundaries.
Read post →2026-09-24
How to Build an AI Data Agent Over Your Existing Data Warehouse
Design a first AI data agent using a narrow business task, governed metric tools, authenticated access, traceable answers, and acceptance tests.
Read post →2026-09-21
Text-to-SQL Agents: Why Valid SQL Can Still Produce the Wrong Answer
Evaluate text-to-SQL accuracy beyond syntax, with weighted-rate examples, semantic checks, permission boundaries, and a practical regression suite.
Read post →2026-09-18
Semantic Layer vs RAG vs Knowledge Graph: What Does Your AI System Need?
Compare semantic layers, document retrieval, and knowledge graphs using business questions, failure cases, and an architecture that combines them.
Read post →2026-09-15
How to Build a Semantic Layer for AI: Metrics, Relationships, and Business Rules
Build a small, testable semantic layer for an AI data agent, from a metric contract and order-level SQL to access controls and acceptance checks.
Read post →2026-09-12
Why AI Gives Wrong Answers About Your Business Data—and How to Diagnose the Problem
Trace incorrect AI analytics answers to metric definitions, joins, time ranges, missing data, or unsupported explanations, using a worked example.
Read post →2026-09-09
What Is a Semantic Layer, and How Does It Help AI Understand Your Business?
See how a semantic layer connects business definitions to data, helps AI query consistent metrics, and where it still needs testing and human judgment.
Read post →2026-07-18
The Omni CLI Is Here: Automate Omni Analytics from the Terminal
Omni Analytics now has an official CLI, auto-generated from the API spec, JSON-first, and built for scripting and AI agents. What it does, how to install it, and what to automate first.
Read post →2026-07-17
Branch Strategies for Omni Model Development: Three Workflows That Scale
How to run Git branching for Omni Analytics model development, basic, multi-instance, and feature-based workflows, the workbook-to-model promotion flow, and CI/CD automation with Omni's APIs.
Read post →2026-07-16
Embedding Omni Analytics: How Signed-URL Embeds Actually Work
The architecture of secure Omni embeds, per-user signed URLs, server-side secrets, and user attributes for row-level security, walked through with Hawkfry Group's open-source omni-embed-demo.
Read post →2026-07-14
Omni Access Management: Row-Level, Column-Level, and Content Security Explained
How access control actually works in Omni Analytics, connection restrictions, access_filters for row-level security, required_access_grants for column-level security, and folder-based content permissions.
Read post →2026-03-10
Looker Explore Best Practices: Labels, Joins, and Field Organization
How to design Looker Explores that business users actually understand. Covers labeling conventions, join best practices, field grouping, and documentation patterns.
Read post →2026-03-08
SQL Fanouts in Looker: Why Your Measures Are Wrong and How to Fix Them
Understand SQL fanouts in Looker, why joined tables produce incorrect counts and sums, how to detect fanouts, and three patterns to prevent them.
Read post →2026-03-06
Looker Caching and Datagroups: Stop Hitting Your Database on Every Query
How Looker caching works, how to configure datagroups for optimal performance, and the caching patterns that align refresh with your ETL schedule.
Read post →2026-03-04
dbt vs Looker: Where Should Your Business Logic Live?
A decision framework for what belongs in dbt and what belongs in Looker. Stop duplicating logic across your data stack, here's where each transformation should go.
Read post →2026-03-03
5 AI Use Cases Every Business Should Implement in 2026
Five practical, high-ROI AI use cases you can implement today, from internal knowledge search to automated reporting. No hype, just what works.
Read post →2026-03-02
Migrate from Looker to Omni in Days, Not Months
A practical guide to migrating from Looker to Omni Analytics, why most migrations fail, how our AI-powered tool handles the hard parts, and a step-by-step approach.
Read post →2026-03-01
Omni vs Looker in 2026: An Honest Comparison by the Team That Knows Both
A side-by-side comparison of Omni Analytics and Google Looker, schema layer vs LookML, AI features, pricing, migration path, and when each tool makes sense.
Read post →2026-02-28
The Fractional Head of Data: Why You Don't Need a $250k Hire
Why a fractional Head of Data beats a full-time hire for most mid-market companies, and what the engagement actually looks like.
Read post →2026-02-25
Looker Dashboard Tabs: How We Cut Load Times from 2 Minutes to Under 10 Seconds
How Looker's tabbed dashboard components transformed slow, monolithic dashboards into sub-10-second experiences, implementation guide with real performance results.
Read post →2026-02-25
LookML Best Practices: The Folder Structure That Scales
How to organize your Looker project with auto-generated views, refinements, and a clean folder structure that doesn't fall apart at 500 views.
Read post →2026-02-22
Automate Looker Content Cleanup with the SDK and BigQuery
How to build an automated Looker content cleanup pipeline using the Looker SDK, identify stale dashboards, unused Looks, and orphaned content every 90 days with results stored in BigQuery.
Read post →2026-02-20
Looker Permissions Done Right: Groups, Access Grants, and Row-Level Security
A practical guide to Looker permissions, groups-first access, row-level security, column-level grants, and the folder structure that keeps content governed.
Read post →2026-02-18
Omni User Management at Scale with omni-user-manager
How to manage users, roles, and permissions in Omni Analytics at scale using Hawkfry Group's open-source omni-user-manager tool, bulk provisioning, role assignment, and lifecycle automation.
Read post →2026-02-15
The Essential Omni Analytics Resource Guide
A curated guide to the Omni Analytics ecosystem, tools, documentation, community resources, migration guides, and open-source projects for teams adopting Omni.
Read post →2026-02-12
dbt + BigQuery + Looker: Building Your Modern BI Stack in 2026
How to architect a modern BI stack with dbt, BigQuery, and Looker, layer separation, transformation best practices, LookML patterns, and the role of AI in the modern analytics pipeline.
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