Today’s push for transforming with AI means evolving from passive “systems of intelligence” to proactive “systems of action” driven by AI agents. To power these agents effectively, however, they need seamless access to your entire data estate along with deep business context.

Your reality is naturally distributed. While you might have petabytes of data natively stored in Google Cloud Storage and analyzed in BigQuery, it is completely normal for a significant portion of your estate to also thrive in Amazon S3, Azure, and various SaaS applications.

The good news is that regardless of whether your primary data stores are in Google Cloud, AWS, Azure, or enterprise SaaS apps, you can unlock cross-cloud analytics and AI. This allows you to turn your distributed data into a strategic advantage, without the operational friction of duplicating data or the financial anxiety of unpredictable egress fees.

What makes Google Cloud Lakehouse for Apache Iceberg unique? 

It is an AI-native, borderless architecture that optimizes your Google Cloud workloads while extending Google’s analytics and AI engines directly to your external data estates. You can activate your data wherever it lives, without moving it, and generating business value from your industry and domain specific corpus of knowledge.

Here is how Google Cloud Lakehouse bridges the gap between your hybrid data storage and Google’s AI capabilities.

Deep Analysis: Unifying Google Cloud, AWS, and SaaS

By unifying your distributed data estate, Google Cloud Lakehouse delivers on four key pillars that make cross-cloud AI a reality:

1. The Borderless Foundation

The borderless Lakehouse reduces the overhead of traditional ETL pipelines, allowing you to activate your remote data in place.

• Partner Cross-Cloud Interconnect: By configuring a private, dedicated link between Google Cloud and AWS, you bypass the public internet. This delivers consistent bandwidth, predictable latency, and predictable flat-rate economics that eliminate variable egress costs for your AWS data.
• Intelligent Cross-Cloud Caching: As queries read data from your remote environments, the Lakehouse temporarily caches those data segments locally within Google Cloud on specialized storage. Subsequent queries use this local cache, dramatically reducing repeated data transfers and the associated cross-cloud egress charges.

2. Seamless Federation and Zero-Copy Integration

Google Cloud Lakehouse embraces your existing hybrid investments.

• Iceberg REST Catalog Federation: The Lakehouse runtime catalog natively federates with AWS Glue, Databricks Unity Catalog, and Snowflake Horizon. Through OpenID Connect (OIDC) token federation or Secret Manager, Google securely authenticates with your remote environments without requiring long-lived access keys.
• Unified Read/Write Interoperability: Your data engineering teams can run BigQuery, Managed Service for Apache Spark, Trino, and Flink on the exact same copy of Apache Iceberg data—whether it sits in GCS or S3.
• Zero-Copy SaaS: Break down application data silos with secure, zero-copy integrations into core enterprise systems like SAP, Salesforce, and Workday, allowing BigQuery to query live transactional data without data duplication.

3. Agentic Readiness: Always-On Context for Hybrid Data

AI agents require rich business context to unlock the full value of your raw data. To bridge the trust gap, Google integrates your entire data estate with its advanced governance suite.

• Knowledge Catalog: This AI-driven context engine automatically synchronizes with Google Cloud, partner platforms, and third-party catalogs. It extracts and indexes the metadata, translating raw schemas into searchable business terminology and column-level lineage.
• Gemini-Powered Enrichment: Knowledge Catalog leverages Gemini to automate data enrichment—mining schemas, files, and logs to auto-tag assets across your multi-cloud footprint, establishing semantic guardrails that eliminate AI hallucinations.
• Conversational Analytics: You can empower your business users to query and visualize your datasets instantly using natural language directly within the BigQuery console or Gemini Enterprise interface.

4. Differentiated Compute on Open Formats

Once your hybrid data is connected, you can leverage Google’s vertically integrated compute engines to process it at unprecedented speeds.

• Spark Lightning Engine: Supercharge your data science workloads with a managed Apache Spark service that utilizes a vectorized Lightning Engine, accelerating execution by up to 4.9x over open-source alternatives—requiring zero code changes.
• Advanced BigQuery Workloads: Combine structured Iceberg metrics with unstructured assets (like PDFs or images) using BigQuery Object References, unlocking multimodal analytics via single-SQL inference.

Google Cloud Lakehouse for Apache Iceberg empowers a seamless expansion strategy. By utilizing Cross-Cloud Interconnects, intelligent caching, and direct catalog federation, you can unlock BigQuery’s performance and Gemini’s agentic AI capabilities across your entire data estate securely, instantly, and cost-effectively.