Apache Kylin vs Apache Druid

Developers should learn Apache Kylin when building data warehousing or business intelligence solutions that require fast, interactive queries on large-scale datasets, such as in e-commerce analytics, financial reporting, or IoT data analysis meets developers should learn apache druid when building applications that require real-time analytics on massive datasets, such as monitoring systems, clickstream analysis, or iot data processing. Here's our take.

Apache Kylin

Nice Pick

Developers should learn Apache Kylin when building data warehousing or business intelligence solutions that require fast, interactive queries on large-scale datasets, such as in e-commerce analytics, financial reporting, or IoT data analysis

Pros

• It is particularly valuable in scenarios where traditional relational databases struggle with performance on big data, as it leverages Hadoop's scalability while providing OLAP-like query speeds through pre-aggregation
• Related to: apache-hadoop, apache-spark

Cons

• Specific tradeoffs depend on your use case

Apache Druid

Developers should learn Apache Druid when building applications that require real-time analytics on massive datasets, such as monitoring systems, clickstream analysis, or IoT data processing

Pros

• It is particularly useful for use cases involving time-based queries, high-cardinality dimensions, and sub-second query latencies, where traditional databases like PostgreSQL or Hadoop might struggle with performance
• Related to: apache-kafka, apache-hadoop

Cons

• Specific tradeoffs depend on your use case

The Verdict

These tools serve different purposes. Apache Kylin is a platform while Apache Druid is a database. We picked Apache Kylin based on overall popularity, but your choice depends on what you're building.

The Bottom Line

Apache Kylin wins

Based on overall popularity. Apache Kylin is more widely used, but Apache Druid excels in its own space.