Google integrates AI natively across our entire technology stack—from TPUs and our global network to models and the Data Cloud. By connecting Gemini models and AI developer tools directly to your data infrastructure, we eliminate extra network hops and simplify cost, scaling, and security management.

Google Data Cloud supports an open data lakehouse, blending open formats (Apache Iceberg) with enterprise-grade engines like BigQuery and Google Cloud Managed Service for Apache Spark. Coupled with Knowledge Catalog for comprehensive governance and agent context, this infrastructure allows AI agents to securely access and activate data across your organization.

Google Cloud activates data by embedding AI, vector search, and graph reasoning directly into BigQuery, enabling models to execute where data lives. This architecture supports an agent-first lifecycle, using natural language tools and serverless computing to rapidly deploy agentic workflows. Antigravity further streamlines this by introducing autonomous automation to discover, author, and optimize Spark and SQL workloads with minimal manual intervention.

The migration assessment service models true operational costs on Google Cloud based on your specific query patterns and data usage. It defines financial impact taking into account speed, agility, and AI leverage to help you build a strong business case for modernization.

The BigQuery migration service features SQL translation, supporting migration from 18 dialects to BigQuery SQL. Gemini translates the complex procedural SQL that standard tools miss, pushing automation toward 100%. By analyzing the full schema and code, Gemini creates translations that are functionally identical, not just syntactically correct. Gemini also provides automated analysis and conversion of Databricks notebooks, translating Spark SQL and PySpark code to be fully compatible with Google Cloud. The service handles library dependencies and configuration adjustments.

Gemini streamlines migration validation by providing holistic verification across schema, data, and semantic logic to ensure complete accuracy. By comparing legacy and modernized query outcomes, it focuses on consistent business results rather than code structure, effectively eliminating false alarms. This automated approach drastically reduces user acceptance testing from months to weeks, accelerating time-to-value while freeing up technical experts for higher-priority tasks.

Gemini CLI transforms migrations into agentic workflows by providing a developer-friendly environment where AI intelligently integrates migration services and selects the optimal tools for the task. By working directly within existing codebases, it allows teams to "clone and go," drastically increasing efficiency and accelerating the transition to Google Cloud.

Start by building a data-driven business case and a clear roadmap. The automated discovery and assessment helps you analyze your existing EDW, like Teradata, Snowflake, and Redshift, to understand data lineage, dependencies, and query patterns. You get a detailed report comparing your current costs to a projected BigQuery TCO, highlighting potential savings and ROI.

Meticulous planning is the key to a successful migration. Identify "quick wins," map dependencies and group workloads into logical migration waves to deliver value quickly and reduce risk.

Gemini-powered code translations, automated data and metadata transfer, and end-to-end validation provide an AI-driven approach to migrations that dramatically reduce time, cost, and human error.

Minimize risk and disruption to Spark and Hadoop workloads or kickstart a fast migration from Cloudera to a fully managed enterprise ecosystem. If you don’t want to rebuild your on-premises data lake in the cloud, lift and shift your data to Google Cloud to unlock cost savings and scale.

As you become comfortable with Google Cloud, introduce more agility and velocity to your data teams. Google Managed Service for Apache Spark leverages the Lightning Engine to run Spark workloads 4.5x faster than standard benchmarks.

Simplify management and operations. After you've moved your legacy data lake, begin introducing cloud native optimizations like automated storage-tiering for TCO, ease of management, speed and scale.

The new migration assessment for Databricks allows you to analyze your existing Databricks environment to determine the migration effort and estimated TCO savings so you can feel confident in your choice to migrate.

Maintain the advantages of open data and table formats while gaining multi-engine access and benefiting from an integrated lakehouse architecture that delivers superior price-performance. Get a simple, automated path for migrating data, metadata and permissions from Delta Lake to Lakehouse for Apache Iceberg so you get the full benefits of unified governance, enterprise scale, leading price-performance and access to AI models and tools.

Automate the migration of your Cloudera or Hadoop environments directly to Google Cloud to save significant time and manual effort. By seamlessly moving your tables and metadata into Cloud Storage and a managed Lakehouse catalog, your data becomes instantly accessible for SQL, Spark, and Python workloads the moment it lands.

Google Cloud has deep partnerships with Global System integrators like Accenture, Capgemini, Deloitte, KPMG, PwC, and Tata Consultancy Services, to strategically plan and drive end-to-end migrations.

Work with a specialized migration partner: Cognizant, Datatonic, Devoteam, EPAM Systems, HCLTech, Infosys, Kyndryl, LTIMindtree, Onix, Persistent Systems, PublicisSapient, Qodea, Quantiphi, Searce, TechMahindra, TEKsystems, Tredence, Wipro, and Zencore.

Google Cloud Professional services can help you plan and execute your EDW or data lake migration. We have migration blackbelts that offer deep technical expertise. Learn more about Google Cloud Consulting services.