More than 70% of organizations have migrated some workloads to cloud, according to Gartner, and this momentum is continuing. Yet, many migrations fail to meet expectations, whether they run over-budget, don’t meet deadlines or don’t meet expected performance. Artificial intelligence (AI) is helping to change this, and Google Cloud’s built-in AI and machine learning capabilities have the ability to accelerate database modernization projects by streamlining migrations, providing ongoing management and innovating databases in the cloud.
Unlocking Innovation with Google Cloud's Database Services
Google Cloud’s database services can help you build cloud-native apps while increasing agility, reducing complexity, and boosting cost-efficiencies. That means you have more time to focus on innovation and transform your business with cloud database features like serverless management and auto-scaling. From there, you can build out advanced capabilities, such as data analytics and AI-based applications with Google BigQuery and Google Cloud AI.
Powering assessments with AI in Google Cloud
Different workloads require different approaches to migration and modernization, whether it’s a modification to an existing workload to adopt a cloud-native approach or re-writing an application to take advantage of cloud-native services. Pythian uses Google Cloud AI tools such as AutoML and BigQuery ML for analyzing existing databases and identifying migration and modernization opportunities.
AI can be leveraged for automated dependency analysis and generating optimized migration paths. For example, AI algorithms can analyze on-premise workloads to understand dependencies and predict potential migration challenges based on these dependencies that could cause compliance or security risks. It can also be used to better understand resource usage to help with planning, including estimated costs, and determine the optimal cloud platform for different workloads to optimize a multi-cloud approach.
Accelerating data mapping and migration with AI
Pythian leverages Google Cloud’s AI services like Dataflow and Cloud AI for automated data mapping and transformation, while AI-driven tools help to reduce manual data handling, enhance accuracy, and speed up migration timelines. Using AI-driven tools, you can reduce repetitive tasks like workload provisioning and configuration management, which helps to reduce human error and speed up the migration process.
If you’re migrating from Oracle/SQL Server to PostgreSQL, Google’s Database Migration Assessment (DMA) tool is a valuable asset. This no-cost database assessment tool analyzes your on-premises workload, providing recommendations for your Google Cloud target database, estimating migration effort, and right-sizing cloud resources.
Optimizing database performance with AI
AI plays a critical role in optimizing resource allocation, workload performance, and dynamic scaling on Google Cloud. At Pythian, we utilize AI tools like AI Platform and Vertex AI to predict performance bottlenecks and fine-tune cloud database performance. AI Platform supports the entire machine learning lifecycle, from ideation to deployment, while Vertex AI empowers businesses to train machine learning models and customize large language models (LLMs) for AI-powered applications.
Gemini, integrated throughout the database experience, enhances migration and application development. Whether you need SQL code generation, proactive monitoring, or simpler code conversions, Gemini simplifies these processes. Its features, like Database Studio Guidance, database fleet management insights powered by conversational AI, and AI-powered database code conversion, accelerate development and migration tasks.
Unleashing the benefits of AI
Each database strategy will be different, but Google Cloud’s AI capabilities can help to accelerate and enhance migration and modernization projects. To truly unleash these benefits, our team of Google Cloud experts at Pythian can help you leverage the right Google tools to determine the technical difficulty of modernization and create a roadmap that includes future state architecture, potential risks, timelines, and estimated cloud consumption costs, as well as recommendations for next steps.
Reach out to Pythian for expert guidance on leveraging AI in Google Cloud for your next database modernization project.
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