Auto Database Integration

• Application Infrastructure & Middleware - Data Exchange & Integration
• Application Infrastructure & Middleware - Database Management & Storage
• Platform as a Service (PaaS) - Data Management Platforms

• Business Operation

• Software Design & Engineering Services
• System Integration

The logistics company faced significant challenges in managing an evolving data model across a large, heterogeneous set of data sources. With almost 50 different relational databases running Oracle, MySQL, and PostgreSQL, the company needed to address performance issues and enable more interactive data exploration. The introduction of a Hazelcast in-memory data grid as a distributed cache improved data retrieval latency but added a new layer of maintenance complexity. The company needed to keep the data model of the data grid in sync with the data sources as the relational database schemas changed over time.

The customer is a logistics company that operates on a global scale, tracking logistics events and managing a complex system that has evolved over time. The company deals with a diverse set of data sources, including almost 50 different relational databases running Oracle, MySQL, and PostgreSQL. The company's system has grown in terms of functional features, geographical locality, and business arrangements, leading to a constant need for new features and data model changes. The company aims to reduce maintenance costs, improve performance, and enable more interactive data exploration.

To address the maintenance problem, the company implemented Auto Database Integration (ADBI) to streamline the process of keeping the data model of the Hazelcast IMDG in sync with the relational database sources. At design time, ADBI connects to the relational databases, analyzes the metadata of the tables, and generates the necessary Java POJOs to interact with both the databases and the in-memory grid. ADBI can analyze metadata from all major database engines, making it suitable for managing a heterogeneous set of sources. At run-time, ADBI provides the functionality to feed data from the sources to the data grid, leveraging the Hazelcast Portable interface. This allows the data grid to serve as a vessel for the data without any modifications. Java applications using ADBI can access data from both the grid and the relational databases using Java Streams, enabling seamless optimization and reducing the need for business logic rewrites.

• ADBI has significantly reduced maintenance costs by 15%, eliminating the need to manually propagate changes in the underlying database model into Java applications and the data grid.
• The time to market for new applications has been drastically reduced, particularly for complex applications that rely on data from multiple sources.
• Setting up a new application for a service is now 100x faster, enabling agile development strategies and early prototype demos.

• Maintenance costs reduced by 15%.
• Setting up new applications is 100x faster.