Many enterprises have built their foundational business operations on robust transactional systems powered by Oracle Database. And with Oracle on Google Cloud, they can deploy and manage Oracle Database instances directly within Google Cloud’s highly scalable and secure infrastructure, benefiting from low-latency network connectivity and integrated services.

But in today’s digitized world, data utilization is crucial for competitive advantage. Oracle excels at Online Transaction Processing (OLTP). However, to fully leverage its analytical capabilities and integrate it with cutting-edge AI, seamless integration with a scalable, cloud-native data platform like Google Cloud’s BigQuery is often essential.

In this blog post, we discuss best practices for moving Oracle data into BigQuery. Then, once it’s there, we discuss some of the things that you can now do with that data, and how. Finally, we present some short examples. Let’s get started.

Bridging the gap: Connecting Oracle data to BigQuery

The journey to advanced analytics begins by bringing your Oracle data into an environment designed for scale and analytical workloads. Luckily, Google Cloud offers several powerful services that can help to facilitate this.

Your first thought might be to use ODBC/JDBC drivers — and in many respects, you’re not wrong. However, it’s crucial to understand their primary role in this context.

The Google Cloud documentation on ODBC/JDBC drivers for BigQuery describes how client applications and reporting tools (which might be running on Oracle, or simply need to connect to BigQuery for data access) can use these drivers to query BigQuery. These drivers establish a direct connection between an application and BigQuery, acting as an intermediary to translate SQL queries and retrieve results. For example, an application could use a JDBC driver to connect to BigQuery over TCP/IP, send a SQL query string, and receive a result set back in a structured format. In other words, these drivers are primarily designed for interactive querying and reporting, rather than large-scale, continuous data movement from transactional systems.

To truly integrate Oracle operational data into BigQuery for analytical purposes, the most efficient and recommended approach involves continuous data replication. Google Cloud’s Datastream for BigQuery stands out as a key solution. Datastream enables low-latency Change Data Capture (CDC), capturing transactional changes from your Oracle source database at the redo-log level, streaming real-time inserts, updates, and deletes from your Oracle databases directly into BigQuery. Datastream handles schema evolution and data-type conversions, helping to ensure data integrity and consistency between Oracle and BigQuery. This means your analytical datasets are always fresh and ready for immediate insights. Then, for less frequent updates or large historical loads, you can also stage data in Google Cloud Storage and then load it into BigQuery, using BigQuery Data Transfer Service or bq load commands or queried directly via BigQuery external tables; this allows BigQuery to read data directly from Cloud Storage without explicit loading.

Unlocking BigQuery Analytics and AI

Once your Oracle data is in BigQuery, a world of possibilities opens up. BigQuery provides a fully managed, serverless, and highly scalable data platform that can easily handle petabytes of data. Its columnar storage format and massively parallel processing (MPP) architecture optimize analytical query performance. You can run complex SQL queries on your consolidated datasets, combining Oracle’s transactional history with other data sources to gain a comprehensive, unified view of your business.

But the real fun begins when you integrate Gemini capabilities into BigQuery, namely:

Natural language data exploration: Gemini lets you interact with your data using natural language, regardless of your technical skill level. Features like data canvas and data insights let you ask questions in plain English, generate visualizations, and discover patterns — all without writing a single line of code. This is powered by large language models (LLMs) that understand natural language queries and translate them into SQL.

AI-assisted SQL and Python: For data professionals, Gemini powers intelligent assistance for writing, explaining, and debugging SQL and Python code within BigQuery, dramatically increasing productivity and reducing development time. Gemini’s code generation and debugging capabilities are trained on vast code repositories, and can provide context-aware suggestions and error explanations.

Advanced analytics with BigQuery ML: If you’re a data analyst, get ready to integrate Gemini models directly into your BigQuery ML workflows. Perform tasks like sentiment analysis, entity extraction, text generation, or leverage advanced forecasting models (like TimesFM) on your integrated data, all within the familiar BigQuery environment. BigQuery ML lets you create and execute machine learning models using standard SQL queries, democratizing ML for data analysts. Gemini models can be invoked as user-defined functions (UDFs) within BigQuery ML, facilitating complex AI tasks directly on your data.

Multimodal Capabilities: BigQuery moves beyond traditional text and numbers by integrating multimodal capabilities, powered by models like Gemini 1.5 Pro. This allows you to analyze diverse unstructured assets—such as images, audio, and video—directly alongside your structured data. The result is richer, context-aware analysis across your complete enterprise dataset.

Agents: AI agents enable complex, multi-step data operations that go beyond simple querying. Leveraging frameworks like the Agent Developer Kit (ADK), these specialized agents autonomously plan, reason, and orchestrate steps like querying BigQuery tables, running BigQuery ML models, and generating comprehensive, natural language reports based on a single high-level goal. Google’s Gemini for Enterprise (previously Agentspace)** **acts as the central AI agent hub, bridging your structured BigQuery data with various work applications and data sources. Agents deployed here leverage Gemini’s reasoning to perform end-to-end workflows. For example, an agent could synthesize sales trends from BigQuery (including modernized Oracle data), interact with a CRM to identify at-risk customers, and automatically generate and send personalized outreach emails, turning data insights into automated action.

Real-world impact: Industry use cases

With BigQuery and Google’s Gemini for Enterprise, the possibilities are endless. Here are some early examples of how, together, they can transform raw data into actionable intelligence:

Retail: Retailers can stream sales transactions, customer purchase histories, and inventory data from Oracle ERP and POS systems into BigQuery. With Gemini AI and Google’s Gemini for Enterprise, they can then build sophisticated customer recommendation engines, develop highly accurate demand forecasts, optimize inventory levels, and create personalized marketing campaigns with a 360-degree view of each customer. For instance, you could use BigQuery ML to build collaborative filtering models for recommendations, and time-series models like ARIMA or Prophet for demand forecasting. AI agents could automate the generation of personalized product catalogs or even initiate reorder processes based on demand forecasts derived from this data.

Education Tech and universities: Institutions can integrate student enrollment data, course histories, financial aid information, and administrative records from Oracle systems into BigQuery. Leveraging Google’s Gemini for Enterprise, they can predict student success rates, identify students at risk of dropping out, tailor course recommendations to individual student interests and career aspirations, and optimize resource allocation for academic programs. Predictive models in BigQuery ML can identify students at risk using historical academic performance and engagement data. AI agents could assist faculty with curriculum development, learning plans based on student interest trends, content preference or help administrators streamline student support processes. Pearson is partnering with Google Cloud to create Next-Generation AI Tools across various data sources to develop agentic AI-powered study tools that enable personalized learning that adapts to each student’s unique pace and progress, keeping learners engaged, supported, and on track for academic success.

In conclusion, integrating Oracle Database with Google Cloud services like BigQuery, Datastream, Google’s Gemini for Enterprise** **is more than just a technical migration; it’s a strategic transformation. By moving transactional data from a robust system of record into a powerful analytical and AI-driven ecosystem, enterprises can unlock unprecedented value. This fusion allows businesses to not only gain a comprehensive, real-time view of their operations but also to infuse their data with advanced intelligence. From natural language queries and AI-assisted coding to predictive modeling and automated workflows, the combination of Oracle on Google Cloud empowers organizations to turn historical data into actionable insights, driving innovation, efficiency, and a significant competitive edge in a data-centric world.

Further resources:

Explore AI Solutions**:** Google Cloud AI Products

Official guide on Oracle to BigQuery, covering architectural differences, migration strategies, and AI-powered BigQuery Migration Services: Oracle to BigQuery migration

Evaluate BQ Agents using Agent Development Kit (ADK)

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• Databases
• AI & Machine Learning
• Data Analytics