Quick Answer
Google's Looker 26.10 (June 2026) introduces agentic BI powered by Gemini, self-service Explores on Snowflake, and advanced observability metrics. BigQuery Graph and Snowflake semantic views now integrate directly with Looker's in-database analytic models. For Indian analysts, BigQuery SQL skills are growing in value as Google Cloud adoption expands.
5 Key Updates from Google Looker & BigQuery in June 2026
- Looker 26.10 (rolling out June 22, 2026) includes advanced observability metrics and enhanced dashboard capabilities.
- In-database analytic models now support BigQuery Graph and Snowflake semantic views in preview.
- Self-service Explores now work on Snowflake connections — removing a previous major limitation.
- Gemini in Looker enables natural language analytics, anomaly detection, and automatic insight generation.
- Google Next '26 showcased how Looker and BigQuery are becoming the foundation of Google's agentic analytics vision.
What Is Google Looker?
Looker is Google Cloud's enterprise business intelligence platform. Google acquired Looker in 2019 for $2.6 billion, and it has since become the BI tool of choice for organizations building their analytics on Google Cloud.
What makes Looker distinctive is its semantic layer — a data model written in LookML (Looker's proprietary modeling language) that defines how business metrics are calculated, what data joins look like, and how data should be presented to end users.
The semantic layer ensures that when the Finance team says "revenue" and the Marketing team says "revenue," they are both looking at the same calculation — with the same rules applied consistently.
Looker vs Tableau vs Power BI — Where Does It Fit?
- Looker: Best for enterprise governance, data teams needing a strong semantic layer, Google Cloud shops
- Tableau: Best for visual analytics and data storytelling
- Power BI: Best for Microsoft ecosystem, cost-effective enterprise deployments, Indian market
Looker 26.10: What Changed in June 2026
Looker 26.10 started rolling out on June 22, 2026. This is a substantial update — not just minor bug fixes.
Key New Capabilities
- Advanced Observability Metrics: New dashboards showing data freshness, query performance, and user adoption patterns — so data teams can monitor and optimize their Looker deployment
- Enhanced Dashboard Capabilities: Improved layout options, conditional formatting for dashboard tiles, and new drill-through navigation patterns
- In-Database Analytic Models (Preview): Support for BigQuery Graph and Snowflake semantic views
- Self-Service Explores on Snowflake: Admins can now enable Self-service Explores on Snowflake connections
In-Database Analytic Models: A Technical Deep Dive
This is the most technically significant update in Looker 26.10, and I want to explain why it matters.
Traditionally, if your company used both BigQuery and Looker, you had to maintain your metric definitions in two places:
- LookML in Looker (for Looker users)
- BigQuery Analytics Hub / dbt models / etc. (for other tools)
With in-database analytic models, Looker can now read semantic definitions directly from BigQuery Graph or Snowflake Cortex Analyst semantic views. One source of truth — maintained in the database, consumed by Looker and other BI tools simultaneously.
Why This Is Important for Data Teams
- Metric definitions stay consistent across Looker, Tableau, and other tools
- Data engineers define metrics once — BI tools consume them
- Less duplication, less drift, fewer "why do Finance and Marketing show different numbers?" arguments
"Keeping semantic definitions consistent across Looker and other BI tools is one of the biggest operational challenges for enterprise data teams. In-database analytic models are a real step toward solving it." — Analytics Engineering community feedback on Looker 26.10
Self-Service Explores on Snowflake
Previously, Looker's Self-service Explores feature — which lets business users drag and drop dimensions and measures to explore data without knowing LookML — was not supported on Snowflake connections.
This was a significant limitation because many companies use Snowflake as their primary data warehouse and Looker as their BI layer.
Looker 26.10 removes this restriction. Admins can now set Snowflake as the Default Connection for Self-service Explores, enabling true self-service analytics for non-technical Snowflake users through Looker.
Gemini in Looker: AI-Powered Analytics
Google's Gemini AI model is now deeply integrated into Looker. The integration enables:
Natural Language Querying
Ask questions in plain English: "Show me which products had declining margins last quarter broken down by supplier." Gemini understands your LookML semantic layer and generates the correct query.
Automatic Anomaly Detection
Gemini proactively identifies statistical anomalies in your data and surfaces them in your dashboards — before someone has to ask "what happened on Tuesday?"
Smart Narrative Generation
Gemini can write plain-English summaries of what your data shows, suitable for executive reports and non-technical stakeholders.
Predictive Insights
Gemini can suggest what your data might look like in the next 30/60/90 days based on historical patterns — useful for sales forecasting and inventory planning.
BigQuery: The Engine Behind Looker
Google BigQuery is a fully managed, serverless cloud data warehouse. It handles analytics at massive scale — processing terabytes of data in seconds.
BigQuery Key Facts for Analysts
| Feature | BigQuery |
|---|---|
| Query Language | Standard SQL (ANSI compliant) |
| Pricing Model | Pay-per-query (per TB scanned) or flat-rate slots |
| Max Table Size | Effectively unlimited (PB scale) |
| ML Capabilities | BigQuery ML (BQML) — run ML models in SQL |
| Integration | Looker, Tableau (V2 connector), Power BI, Vertex AI |
| Storage Format | Columnar (Capacitor) — optimized for analytics |
BigQuery SQL for Data Analysts — What You Need to Know
BigQuery uses standard ANSI SQL, which means if you know SQL already, you can query BigQuery with minimal effort.
A few BigQuery-specific features worth knowing:
-- BigQuery: Partitioned tables (filter by _PARTITIONDATE for cost savings)
SELECT *
FROM `project.dataset.events`
WHERE DATE(_PARTITIONTIME) >= '2026-06-01'
AND event_type = 'purchase';
-- BigQuery: Array and STRUCT types (nested data)
SELECT
user_id,
purchase.product_name,
purchase.amount
FROM `project.dataset.users`,
UNNEST(purchases) AS purchase
WHERE purchase.status = 'completed';
-- BigQuery ML: Train a model in SQL
CREATE OR REPLACE MODEL `project.dataset.churn_model`
OPTIONS (
model_type = 'logistic_reg',
input_label_cols = ['churned']
) AS
SELECT
days_since_last_order,
total_orders,
total_spend,
churned
FROM `project.dataset.customer_features`;
The BigQuery ML feature is particularly powerful for analysts: you can train machine learning models using SQL, without needing to write Python or use a separate ML platform.
Looker vs Looker Studio: Understanding the Difference
This is one of the most common points of confusion for students new to Google's analytics ecosystem.
| Feature | Looker | Looker Studio (Free) |
|---|---|---|
| Price | Enterprise (significant cost) | Free |
| Semantic layer | LookML (powerful) | None |
| Target user | Enterprise data teams | Marketers, small teams |
| Data governance | Strong (row-level security, etc.) | Limited |
| AI features | Gemini integration | Basic |
| Best for... | Enterprise BI with governed metrics | Marketing dashboards, quick reports |
For most students learning data analytics, Looker Studio is the free entry point to practice Google-ecosystem dashboarding. It connects to Google Sheets, Google Analytics, BigQuery, and many other sources.
Google Cloud Analytics in India
Google Cloud adoption in India is accelerating. Companies using Google Cloud for their data infrastructure need analysts who understand BigQuery, Looker, and the broader Google analytics ecosystem.
Indian Companies Known to Use Google Cloud / BigQuery
- Ola, Rapido, and other mobility platforms
- Several fintech startups in the Google for Startups ecosystem
- Media and entertainment companies using Google Analytics 4 + BigQuery
- EdTech platforms
- IT services companies building Google Cloud data practices
Career Implications for Indian Analysts
The Google ecosystem — BigQuery + Looker + Vertex AI — is growing in Indian IT services companies and startups. Here is how to position yourself:
Skills to Add for Google Cloud Analytics Roles
- BigQuery SQL — Leverage your existing SQL knowledge; BigQuery is mostly ANSI SQL
- Looker Studio — Free to practice; demonstrates Google BI knowledge
- Google Analytics 4 + BigQuery export — Very common in e-commerce and media companies
- LookML basics — Advanced, but needed for data analyst roles at Looker-heavy companies
Google Cloud Certifications Worth Considering
- Google Cloud Professional Data Engineer
- Google Cloud Professional Data Analyst (relatively new)
- Google Cloud Associate Cloud Engineer (foundational)
Frequently Asked Questions
What is agentic BI in Google Looker?
Agentic BI in Looker uses Gemini AI to proactively surface insights, answer natural language questions, and suggest next analytical steps — without users needing to build queries manually.
What is Looker 26.10?
The June 22, 2026 release featuring advanced observability metrics, enhanced dashboards, in-database analytic models (preview), and self-service Explores on Snowflake.
What is the difference between Looker and Looker Studio?
Looker is enterprise BI with a LookML semantic layer. Looker Studio is a free lightweight reporting tool primarily for marketers and small teams.
How does Gemini integrate with Looker?
Gemini enables natural language queries, automatic anomaly detection, smart narrative generation, and predictive insights within Looker.
What is BigQuery?
Google Cloud's fully managed, serverless data warehouse for large-scale SQL analytics. Processes terabytes in seconds, with built-in ML capabilities (BigQuery ML).
Should data analysts learn BigQuery SQL?
Yes. Standard SQL knowledge transfers directly to BigQuery. Knowing BigQuery adds value as Google Cloud adoption grows in Indian companies.
Is Looker or Power BI better for Indian analysts?
Power BI has broader Indian job demand. Looker is preferred in Google Cloud shops. Start with Power BI; add Looker Studio for free Google ecosystem practice.
Ready to Build Your Analytics Career in a Cloud-First World?
At Linkskill Academy in Salem, we teach the foundational skills — SQL, Python, Power BI, Excel — that make you effective regardless of which cloud platform your employer uses.