How is it different from Databricks and Snowflake?

All three platforms move data and run analytics, but they package the agentic layer differently. The Agentic Data Cloud bakes Vertex AI agents and AI in SQL into BigQuery itself, ships Knowledge Catalog with continuous profiling by default, and bundles the Looker semantic layer. Databricks and Snowflake offer comparable building blocks, but as separately purchased products that customers stitch together.

Will it cost more or save money than our current setup?

It depends on how the controls are designed. Continuous Dataplex profiling, per token Vertex AI calls, and per row BigQuery AI functions can add real cost on heavy workloads. Well built workflows save enough engineer time and ETL spend to offset the compute increase. Poorly built ones tend to spiral fast.

Which Google Cloud services has your team worked with?

BigQuery (warehouse, BigQuery ML, BigQuery Pipelines), Vertex AI and Gemini, Looker and Looker Studio, Dataplex and Knowledge Catalog, BigLake and Iceberg, AlloyDB, Cloud SQL, and the AI in SQL functions like AI.PARSE_DOCUMENT and ML.GENERATE_TEXT. Multiple production deployments across these.

What kind of teams should consider this?

Teams with mature BigQuery or wider Google Cloud data platforms who feel their pipelines have become hard to maintain, or teams under pressure to adopt AI inside their data work. This is not a starting point for a brand new data platform.

How long until we see results?

A useful diagnostic in 2 to 4 weeks. A working pilot in 3 to 5 weeks. Broader rollout depends on how many workflows you want to convert.

Do you replace our existing engineers?

No. Our model embeds with your team. Skilled engineers are still required, especially when Vertex AI agents are involved. Our work makes them more effective, not redundant.

How do you stay vendor neutral?

We do not take vendor commissions. Our recommendations are based on your workload, your team, and your existing stack. We tell customers when their current platform is the right answer and when something else fits better.

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Google Agentic Data Cloud, Built for Production

Google Cloud has bundled BigQuery, Vertex AI, Looker, and Dataplex into one autonomy focused platform called the Agentic Data Cloud. Agents observe what is happening in your data, decide what to do, and act on their own. The pieces are real, the savings are real, and the controls you need around it matter just as much.

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See it in action

A short walkthrough of how an agentic workflow runs across BigQuery, Vertex AI, Knowledge Catalog, and Looker. From spotting a trend to a revenue forecast, with no manual orchestration in between.

See it in action

A short walkthrough of how an agentic workflow runs across BigQuery, Vertex AI, Knowledge Catalog, and Looker. From spotting a trend to a revenue forecast, with no manual orchestration in between.

Agentic Data Cloud

Wealth Management · Fraud Detection

Knowledge Catalog

Cross-Cloud Lakehouse

Agentic Orchestration

What Google's Agentic Data Cloud actually is

Google Cloud has bundled its data stack into one autonomy focused platform. These are the building blocks underneath, with notes on where our engineers have shipped them in production.

What Google's Agentic Data Cloud actually is

Google Cloud has bundled its data stack into one autonomy focused platform. These are the building blocks underneath, with notes on where our engineers have shipped them in production.

BigQuery

The data warehouse at the center. Our team has shipped BigQuery deployments across analytics, ML, and AI workloads, including BigQuery ML, BigQuery Pipelines, and the AI in SQL functions like AI.PARSE_DOCUMENT and ML.GENERATE_TEXT.

Vertex AI and Gemini

The agent and LLM layer. We have built autonomous workflows on Vertex AI agents, custom embeddings, and Gemini calls embedded inside production pipelines.

Knowledge Catalog and Dataplex

The governance and profiling layer. Multiple deployments where Dataplex handles lineage, data quality scans, and access control across BigQuery and Cloud Storage.

Looker and Looker Studio

The semantic and visualization layer. Custom LookML modeling for executive dashboards, embedded analytics, and natural language query workflows.

BigLake and Iceberg

The open table format layer. Lakehouse architectures with BigLake serving Iceberg tables for cross engine reads from Spark and other compute.

AlloyDB, Cloud SQL, and Spanner

The operational data sources. Zero ETL replication into BigQuery for real time analytics on transactional data, without dedicated pipelines.

Built across regulated and data intensive industries

Financial services

Healthcare and life sciences

Retail and consumer

Industrial and supply chain

Experience with clients backed by

Built across regulated and data intensive industries

Financial services

Healthcare and life sciences

Retail and consumer

Industrial and supply chain

Experience with clients backed by

How it differs from Databricks and Snowflake

All three platforms move data and run analytics. The Agentic Data Cloud is the most ambitious attempt to bake autonomy directly into the platform layer rather than ship it as separate products.

How it differs from Databricks and Snowflake

All three platforms move data and run analytics. The Agentic Data Cloud is the most ambitious attempt to bake autonomy directly into the platform layer rather than ship it as separate products.

Autonomy is native, not bolted on

Vertex AI agents and AI in SQL functions live inside BigQuery itself. Databricks and Snowflake offer comparable tooling, but as separate products customers wire together.

Continuous profiling ships by default

Knowledge Catalog runs profiling, lineage, and quality scans as part of the platform on day one. Databricks Lakehouse Monitoring and Snowflake data catalogs are opt in features that teams configure separately.

The semantic layer comes bundled

Looker is integrated as the semantic and visualization layer for the agentic stack. Databricks AI/BI Genie and Snowflake Cortex Analyst are newer entrants playing catch up.

WHAT WE ENABLE

From dark data to decisions, automatically.

The Agentic Data Cloud removes the barriers between data and action. Insights buried inside PDFs, contracts, ticketing systems, and cloud silos surface without manual engineering, and stay queryable through the same stack you already use.

Unlock hidden insights from dark data

Vertex AI agents and AI in SQL functions like AI.PARSE_DOCUMENT extract structured knowledge from PDFs, contracts, manuals, scanned forms, and support tickets sitting in Cloud Storage.

Query across clouds without migration

BigLake federation reads BigQuery, AWS S3, and Azure storage simultaneously. Zero ETL, zero copy operations, one unified result set for the agent and the analyst.

Automate end to end data pipelines

Data Engineering Agents plan, build, and execute multi step workflows. From ingestion through transformation to forecasting, with no boilerplate code written by hand.

Human in the loop control

Agent plans surface for review before execution. Engineers approve, modify, or reject each step, keeping a human on the call for anything that touches production data.

Where the Agentic Data Cloud actually saves money

Cost outcomes depend on how the controls are designed. These are the specific conditions where the platform pays for itself, and where the savings show up most clearly on a real workload.

Engineer hours on routine pipelines

Data Engineering Agents auto generate dbt, Spark, and Airflow code. Teams reclaim significant engineer time on boilerplate transformation work, often the largest single cost line in a data team's budget.

Manual schema and lineage upkeep

Knowledge Catalog handles profiling and lineage automatically. Less time spent on documentation, less drift sneaking in unnoticed, fewer hours triaging where a column came from.

Separate AI infrastructure spend

Gemini and AI functions run inside BigQuery. No second platform for inference, no second bill, no separate ops layer for an AI stack that lives next to your data anyway.

Cross system data movement

Zero ETL CDC from AlloyDB, Cloud SQL, and Spanner replaces dedicated ETL pipelines. Fewer moving parts, fewer engineering hours, and a smaller compute footprint for the same freshness guarantees.

Engineer hours on routine pipelines

Data Engineering Agents auto generate dbt, Spark, and Airflow code. Teams reclaim significant engineer time on boilerplate transformation work, often the largest single cost line in a data team's budget.

Manual schema and lineage upkeep

Knowledge Catalog handles profiling and lineage automatically. Less time spent on documentation, less drift sneaking in unnoticed, fewer hours triaging where a column came from.

Separate AI infrastructure spend

Gemini and AI functions run inside BigQuery. No second platform for inference, no second bill, no separate ops layer for an AI stack that lives next to your data anyway.

Cross system data movement

Zero ETL CDC from AlloyDB, Cloud SQL, and Spanner replaces dedicated ETL pipelines. Fewer moving parts, fewer engineering hours, and a smaller compute footprint for the same freshness guarantees.

Planning to bring agents into your data work? Let us help you do it without surprises.

Assess your AI readiness

How we work with you

Our engagements follow a sequence that has worked across industries on Google Cloud and beyond.

How we work with you

Our engagements follow a sequence that has worked across industries on Google Cloud and beyond.

Diagnostic and readiness review

Architecture and cost design

Pilot implementation

Embedded delivery

2 to 4 weeks. We map your current BigQuery footprint, your Dataplex governance state, your planned agentic workflows, and the cost surfaces that need attention before any agent goes live. Timing depends upon connectors, data volume, and quality.

Architecture and cost design

3 to 5 weeks. We design the reference architecture across BigQuery, Vertex AI, Looker, and Dataplex, alongside the cost controls and governance policies.

Pilot implementation

3 to 5 weeks. One workflow built properly from start to finish, with data contracts, quality gates, and observability across the stack from day one.

Embedded delivery

Ongoing. Our engineers work alongside your team to extend the pattern across more workflows, more datasets, and more use cases.

Diagnostic and readiness review

Architecture and cost design

3 to 5 weeks. We design the reference architecture across BigQuery, Vertex AI, Looker, and Dataplex, alongside the cost controls and governance policies.

Pilot implementation

3 to 5 weeks. One workflow built properly from start to finish, with data contracts, quality gates, and observability across the stack from day one.

Embedded delivery

Ongoing. Our engineers work alongside your team to extend the pattern across more workflows, more datasets, and more use cases.

Diagnostic and readiness review

Architecture and cost design

3 to 5 weeks. We design the reference architecture across BigQuery, Vertex AI, Looker, and Dataplex, alongside the cost controls and governance policies.

Pilot implementation

3 to 5 weeks. One workflow built properly from start to finish, with data contracts, quality gates, and observability across the stack from day one.

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Recognized by Platform Leaders. Trusted in Production.

Frequently asked questions

A bundle of products from Google Cloud, centered on BigQuery, Vertex AI, Knowledge Catalog, and Looker. The pitch is that agents observe what is happening in your data, decide what to do, and act on their own. Less manual orchestration, more autonomous workflow.

Adopt agentic systems without the chaos

Pick one workflow where you want agents involved. We will help you design and ship it with the controls in place from day one.

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