Apache Kylin vs Dremio: What are the differences?

# Apache Kylin vs Dremio

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1. **Data Processing Engine**: Apache Kylin utilizes the MapReduce engine for query processing, whereas Dremio uses an Apache Arrow-based engine, providing faster query performance due to its columnar execution.
2. **Data Source Support**: Apache Kylin offers support for data sources like Hadoop, Hive, and HBase, while Dremio has broader support for various data sources including cloud storage platforms like AWS S3 and Azure Data Lake Storage, enhancing its data connectivity capabilities.
3. **Data Transformation**: Dremio has a more visual and interactive approach to data transformation through its user-friendly GUI interface, enabling users to prepare and curate data without writing complex scripts, while Apache Kylin relies more on pre-defined data models for data transformation.
4. **Real-time Data Processing**: Dremio enables real-time data processing via continuous queries and incremental data sync capabilities, providing timely insights into rapidly changing data, a feature that Apache Kylin lacks out-of-the-box.
5. **Self-Service Analytics**: Dremio empowers self-service analytics through its data virtualization capabilities, allowing users to query, join, and explore data across different data sources seamlessly, whereas Apache Kylin focuses more on OLAP cube constructions for multidimensional analysis, which may require more expertise.
6. **Scalability**: Dremio is known for its high scalability, being able to handle large volumes of data and concurrent queries efficiently, while Apache Kylin can face limitations in scaling horizontally due to its reliance on certain storage formats like HBase for cube storage.

In Summary, Apache Kylin and Dremio differ in their data processing engines, data source support, data transformation methods, real-time data processing capabilities, self-service analytics features, and scalability.