Apache Kylin vs Druid: What are the differences?

Introduction

In this Markdown code, I will present the key differences between Apache Kylin and Druid, two popular open-source projects for big data processing and analytics.

1. Data Processing Approach: Apache Kylin is an online analytical processing (OLAP) engine that uses columnar storage to accelerate query performance. It builds and maintains pre-calculated cubes to provide fast query responses. On the other hand, Druid is a distributed, real-time analytics data store designed to process high volumes of event-driven data in real-time. It organizes data in memory for fast data ingestion and query execution.

2. Query Capabilities: Apache Kylin supports complex OLAP queries with advanced features like group-by, distinct count, and top-N. It offers dimensional modeling and allows users to explore multi-dimensional data sets efficiently. Druid, on the contrary, focuses on ad-hoc querying and provides sub-second query response times for real-time data exploration. It excels at filtering, aggregating, and slicing and dicing data based on time-based dimensions.

3. Data Ingestion and Storage: Apache Kylin primarily relies on Apache Hadoop and HBase for data ingestion and storage. It leverages the distributed file system for storing and processing large volumes of data. In contrast, Druid has its own data ingestion engine that supports a wide range of data sources, including streaming platforms like Apache Kafka. Druid stores data in a specialized in-memory columnar format for fast queries.

4. Scalability and Performance: Apache Kylin offers high scalability and can handle large data volumes efficiently. It uses distributed processing to parallelize query execution and achieve high performance. However, it requires additional hardware resources to support high throughput and quick response times. Druid, on the other hand, is designed to scale horizontally, with the ability to handle petabytes of data and thousands of nodes. It can deliver near real-time analytics even at massive scale.

5. Data Model Flexibility: Apache Kylin supports traditional star and snowflake schemas commonly used in OLAP systems. It enables users to define and build data cubes that optimize query performance for specific use cases. In contrast, Druid follows a denormalized, flat-table data model. It focuses on real-time analytics and provides flexible schemas that suit ad-hoc querying and multidimensional analysis.

6. Ecosystem Integration: Apache Kylin integrates well with the Apache Hadoop ecosystem and other big data tools like Hive, HBase, and Spark. It leverages the benefits of these technologies for data processing and storage. On the other hand, Druid has extensive integrations with various data sources, including Kafka, Hadoop, and cloud storage systems like Amazon S3. It also provides connectors for popular analytics and visualization tools like Apache Superset and Tableau.

In summary, Apache Kylin is an OLAP engine that focuses on complex OLAP queries and dimensional modeling, while Druid is a real-time analytics data store that excels at ad-hoc querying and real-time data exploration. Kylin leverages Hadoop and HBase for data processing and storage, while Druid has its own ingestion engine and relies on in-memory columnar storage. Both projects offer high scalability and performance but differ in data model flexibility and ecosystem integrations.