Databricks GTP-AI-powered data processing

Example

Designing a microservices architecture using Spring Boot and defining a Maven project structure with multiple modules for modularity.

Scenario

An organization needs to build a scalable e-commerce platform. Databricks GTP helps in architecting the system, ensuring modularity, and seamless integration with Databricks for analytics.

Example

Developing RESTful APIs using Spring MVC and managing database interactions with Spring Data JPA.

Scenario

A financial services company wants to expose its services through RESTful APIs. Databricks GTP assists in creating the APIs, handling data persistence, and optimizing database interactions for performance.

Example

Creating notebooks in Databricks to prototype Spark jobs and utilizing Databricks' job scheduling features for automation.

Scenario

A retail chain needs to process large volumes of sales data for insights. Databricks GTP guides in setting up Spark jobs in Databricks, automating workflows, and ensuring reliable data transfer between the Java application and Databricks.

Example

Writing unit and integration tests using Spring’s testing support and optimizing SQL queries for Spark SQL.

Scenario

A healthcare provider is deploying a patient management system. Databricks GTP helps in ensuring the application is thoroughly tested, performs well in a distributed environment, and queries are optimized for quick data retrieval.

Example

Containerizing the Java application using Docker and defining a CI/CD pipeline with Maven for automated testing and deployment.

Scenario

A tech startup wants to rapidly deploy new features to their platform. Databricks GTP aids in setting up a CI/CD pipeline, containerizing the application, and addressing scalability to handle increasing user traffic.