Data analytics: Polypheny’s ability to handle heterogeneous data originating from different sources combined with its efficient query processing and its ability to handle large volumes of data make it well-suited for data analytics applications that require fast processing of complex queries over large datasets.

Machine learning: Polypheny’s support for in-memory storage engine, its fast query processing, and the ability to handle both structured and unstructured data make it an excellent choice for machine learning applications.

Real-time data processing: Polypheny’s inherently distributed architecture and its ability to process data from various sources in real-time make it a perfect choice for applications such as fraud detection and anomaly detection. This is complemented by support for the graph data model.

Data integration: Polypheny’s ability to accommodate and process data represented according to different data models and its ability to query this data using different query languages make it an excellent choice for applications where data from different source should be combined in a single system.

Real-time monitoring and alerting: Polypheny’s ability to integrate data from heterogeneous sources, its inherently distributed architecture, and efficient query processing make it an excellent choice for applications such as real-time monitoring and alerting. The support fur multiple data models, including the graph data model, enables Polypheny to process and connect heterogeneous data in real-time to detect patterns or anomalies.

Log analysis and troubleshooting: Polypheny’s ability to process data from various sources makes it a good choice for log- and metadata analysis and troubleshooting applications. This enables IT operations teams to troubleshoot issues and improve the reliability of their systems by identifying patterns and correlations in heterogeneous data.

Migration and integration: Polypheny’s support for multiple data models, storage engines and its support for various query languages and query interfaces allow the seamless migration between database systems. This not only allows for seamless transitions but also enables multiple applications to share data without requiring a massive rewrite of these applications.

Optimization for mixed workloads: Polypheny’s ability to combine various highly optimized and domain-specific execution engines allows massively improving the performance of applications that need to execute mixed workloads. Furthermore, its inherently distributed architecture allows seamless scaling and adaption in the future.

Data integration: Polypheny’s support for data integration and efficient query processing makes it a good choice for data engineering applications. Data engineers can use Polypheny to import data from various sources, transform the data as needed, and load it into the system for further processing or analysis.

Interface to the data lake: Polypheny’s can act as a single interface to the data lake, enabling data from various sources to be queried and combined in real-time. Its support for various query languages and applications allows for easy usage and integration into new and existing applications and processes.

Schema migrations: Polypheny’s support for multiple data models also make it a powerful tool for schema migrations. Using views and multiple namespaces, whole application stacks can seamlessly be migrated to a new schema without downtimes.