Doris
Doris is a high-performance, real-time analytical database based on MPP architecture, designed to provide sub-second queries and efficient data storage for big data.
Quix enables you to sync to Apache Kafka from Doris, in seconds.
Speak to us
Get a personal guided tour of the Quix Platform, SDK and API's to help you get started with assessing and using Quix, without wasting your time and without pressuring you to signup or purchase. Guaranteed!
Explore
If you prefer to explore the platform in your own time then have a look at our readonly environment
👉https://portal.demo.quix.io/?workspace=demo-dataintegrationdemo-prod
FAQ
How can I use this connector?
Contact us to find out how to access this connector.
Real-time data
Now that data volumes are increasing exponentially, the ability to process data in real-time is crucial for industries such as finance, healthcare, and e-commerce, where timely information can significantly impact outcomes. By utilizing advanced stream processing frameworks and in-memory computing solutions, organizations can achieve seamless data integration and analysis, enhancing their operational efficiency and customer satisfaction.
What is Doris?
Doris is an advanced MPP (Massively Parallel Processing) SQL database that facilitates real-time analytical workload handling and integrates seamlessly with big data ecosystems. Its architecture supports scalability and robust real-time analytics, making it suitable for modern data applications.
What data is Doris good for?
Doris excels in analytical queries over massive datasets with high concurrency and low-latency performance, and it is ideal for applications needing quick, real-time insights derived from extensive data sources. Its optimized storage and querying capabilities make it a perfect fit for business intelligence and data reporting tasks.
What challenges do organizations have with Doris and real-time data?
Organizations using Doris with real-time data may encounter challenges in balancing data ingestion speed and query performance, especially under high concurrency. Additionally, maintaining real-time data pipelines requires effective data synchronization and management strategies to prevent bottlenecks and ensure data quality.