Introduction: Let the Battle Begin!
When it comes to Big Data processing, the selection of the right tool can make all the difference. Two popular tools for Big Data analytics are Apache Kylin and Apache Druid. While both have their own strengths and weaknesses, it's essential to evaluate them to determine which one is best suited for your needs.
In this blog post, we'll provide an unbiased comparison of Apache Kylin and Apache Druid, pitting the two against each other in a data analytics showdown. So sit back, grab a cup of coffee, and let's dive into the world of Big Data analytics.
Round #1: Architecture
Apache Kylin is built on top of Apache Hadoop, which provides distributed storage and processing capabilities. It is designed to enable OLAP queries with Hadoop, so data can be both stored and processed at scale. In contrast, Apache Druid is designed as a distributed, column-oriented data store that is primarily optimized for OLAP queries. Apache Druid was designed for multi-tenancy, so it's great for powering user-facing analytics applications.
Looking at the architecture of the two platforms, we can see that Apache Kylin and Apache Druid were designed with different use cases in mind. If you are working with a traditional OLAP data cube and require exceptional query performance on your Hadoop cluster, Apache Kylin might be a great choice. However, if you are looking to create a high-performance analytics platform with multi-tenancy support, Apache Druid might be the better option.
Round #2: Querying
When it comes to querying performance, both Apache Kylin and Apache Druid provide excellent results. However, performance can differ based on the type of query and the size of the data.
Apache Kylin has a more straightforward query interface, as it supports full SQL access, and its support for cube designs provides excellent results on aggregation queries. However, it is not very effective with streaming data and query performance might be limited for aggregations over a large amount of data.
Apache Druid, on the other hand, is designed for fast querying performance. It can support SQL queries, but its primary strength lies in its support for OLAP-style queries, where it excels in scanning and filtering large sets of data in real-time. It's especially effective with streaming data and can deliver sub-second query response times.
Round #3: Scalability and Maintenance
Both Apache Kylin and Apache Druid are designed to scale horizontally across commodity hardware. They can also handle massive amounts of data and ingest data at high rates.
However, while Apache Kylin is straightforward to deploy, it can be challenging to maintain its performance across the nodes. Conversely, Apache Druid, being built on modern principles, such as automation and monitoring, is relatively easy to deploy and maintain. Scaling up and down is also easier than Apache Kylin.
Round #4: Community Support
Finally, both Apache Kylin and Apache Druid have large supportive communities actively developing and extending the platform.
Apache Kylin started in 2014, and since then it has grown dramatically due to its exceptional performance on Hadoop. In contrast, Apache Druid started in 2012, and its primary community around Druid over the years has grown substantially.
Final thoughts
When comparing Apache Kylin and Apache Druid, they both have strengths in different areas, and it's essential to pick the one that fits your requirements and skillset better realistically.
Are you looking for high-performance querying at scale? Then Apache Druid is a more logical choice. On the other hand, if you are looking for a straightforward SQL query interface and have a traditional OLAP data pattern, Apache Kylin might be a better fit.
While this blog post provides an unbiased comparison between these two giants of Big Data, we encourage you to test both tools extensively to determine which one is right for your needs.
References
- Apache Kylin: https://kylin.apache.org/.
- Apache Druid: https://druid.apache.org/.
- Apache Hadoop: https://hadoop.apache.org/.