The data analytics migration services support migrating data warehouses, data lakes, and Spark workloads to Google Cloud with AI-powered analysis, translation, and validation.
What Are the Data Analytics Migration Services?
With Google Cloud’s data analytics migration services, AI is woven into every step of the migration, creating an intelligent partner that works across the entire migration process, from discovery to validation. Google Cloud describes this AI-first approach as accelerating migrations by dramatically reducing manual effort, providing a predictable migration plan, and reducing project risks.
Google Cloud’s product catalog lists the data analytics migration services as free-to-use, cloud-native, and AI-powered data migration services, and as streamlined data warehouse and data lake migration.
Core Features
- Automated TCO and value analysis: The migration assessment service models true operational costs on Google Cloud based on your specific query patterns and data usage
- Gemini-powered SQL and PySpark translation: SQL translation from 18 dialects to BigQuery SQL plus conversion of Databricks notebooks, Spark SQL, and PySpark
- Gemini-powered validation: Verification across schema, data, and semantic logic, comparing business outcomes
- Agentic migration workflows: Gemini CLI integrates migration services and selects the optimal tools for the task directly within existing codebases
- Databricks migration assessment: Analysis of an existing Databricks environment to determine migration effort and estimated TCO savings
Typical Use Cases
Enterprise Data Warehouse Migration
Automated discovery and assessment analyze existing EDW environments such as Teradata, Snowflake, and Redshift to understand data lineage, dependencies, and query patterns. Planning follows with quick wins and migration waves, then migration, validation, and optimization.
Data Lake and Spark Migration
Lift and shift to minimize risk for Spark and Hadoop workloads, or migrate from Cloudera to a fully managed ecosystem, then modernize Spark workloads and optimize operations.
Modernizing to an Open Lakehouse
Migrate from Delta Lake to Lakehouse for Apache Iceberg with an automated path for data, metadata, and permissions.
Migrating Hive and Iceberg Tables from Hadoop
Automated migration of Cloudera or Hadoop environments, moving tables and metadata into Cloud Storage and a managed lakehouse catalog.
Benefits
- Less manual effort: AI-powered translation and validation instead of fully manual migration
- A solid basis for decisions: Assessment with cost comparison and roadmap before the project starts
- Focus on business outcomes: Validation compares results rather than code structures
- Partner and services ecosystem: Global system integrators, specialized migration partners, and Google Cloud Professional Services
Integration with innFactory
As a certified Google Cloud partner, innFactory supports you with the data analytics migration services: assessing your existing data platform, planning migration waves, and executing and validating the migration.
Typical Use Cases
Technical Specifications
Frequently Asked Questions
What are the data analytics migration services?
Google Cloud describes them as migration services with AI woven into every step of the migration, from discovery to validation. This AI-first approach is intended to accelerate migrations by dramatically reducing manual effort, provide a predictable migration plan, and reduce project risks. The product catalog lists them as free-to-use, cloud-native, and AI-powered data migration services.
Which source systems are supported?
For discovery and assessment, Google Cloud names existing enterprise data warehouses such as Teradata, Snowflake, and Redshift. For data lakes, Cloudera and Hadoop environments are named, and there is also a migration assessment for Databricks. The BigQuery migration service supports SQL translation from 18 dialects to BigQuery SQL.
What role does Gemini play in the migration?
According to Google Cloud, Gemini translates complex procedural SQL that standard tools miss, analyzing the full schema and code to create translations that are functionally identical rather than merely syntactically correct. Gemini also provides automated analysis and conversion of Databricks notebooks and translates Spark SQL and PySpark; the service handles library dependencies and configuration adjustments.
How is a migration validated?
Google Cloud describes holistic verification across schema, data, and semantic logic. By comparing legacy and modernized query outcomes, the focus is on consistent business results rather than code structure, which is intended to eliminate false alarms. The automated approach is said to reduce user acceptance testing from months to weeks.
Who helps with execution?
Google Cloud points to a partner ecosystem of global system integrators and specialized migration partners, as well as Google Cloud Professional Services, which help plan and execute EDW or data lake migrations.
Note: All product information on this page has been compiled with care, but is provided without guarantee and may be outdated or incomplete. Cloud services evolve rapidly — features, pricing, SLAs, and availability change frequently. Authoritative and up-to-date information can only be found on the official product page of Google Cloud (official documentation). This page does not represent an offer by Google Cloud.
