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From "Xiaoyuan Gu" <>
Subject Re:[Discuss] Reposition Kylin as "Analytical Data warehouse for big data"
Date Mon, 13 Jan 2020 03:06:56 GMT
Agreed. As Kylin has been equipped with plenty of "new" functionalities and is capable to
suit various roles in analyzing data in large scale, a proper tag will definitely be much
helpful not only to new users who are seeking for a suitable analyzing tool, but also to old
users who want to explore possibilities in handling new scenarios with Kylin.

Xiaoyuan Gu

At 2020-01-12 20:32:12, "ShaoFeng Shi" <> wrote:
>Hello, Kylin developers and users, HAPPY NEW YEAR 2020!
>In last month, we released Kylin 3.0, with the new Real-time streaming
>feature and a Lambda architecture. This allows our users to host only one
>system for both batch and real-time analytics, and then can query batch and
>streaming data together.
>If you look at Kylin's home page, its slogan is still the "OLAP Engine for
>Big data", which was made 5 years ago when it was born. While today,
>Kylin's capability has been verified beyond an "OLAP engine". I visited
>many Kylin users in China, US, Euro in last year, and have got many
>different scenarios:
>1. eBay initiated the Kylin project to offload analytical workloads from
>Teradata to Hadoop; Kylin serves the online queries with high performance
>and high availability. Till today, Kylin serves millions of queries every
>day, most are in < 1 seconds;
>2. China Unionpay and CPIC use Kylin to replace IBM Cognos cubes. One Kylin
>cube replaced more than 100 Cognos cubes, with better building performance
>and query performance.
>3. China Construction Bank uses Hadoop + Kylin to offload the Greenplum.
>Some systems have been migrated to Kylin successfully.
>4. Yum (KFC) and several other users are using Kylin to replace Microsoft
>5. Meituan, Ctrip, JD, Didi, Xiao Mi, Huawei, OLX group,,
>Xactly, and many others are using Kylin as the platform of their DaaS (Data
>as a Service), providing data service to their thousands of internal
>analysts and tens of thousands of external tenants.
>Now let's look at the definition of Data warehouse [1]:
>"*A data warehouse is a subject-oriented, integrated, time-variant and
>non-volatile collection of data in support of management's decision-making
>In Kylin, each model/cube is created for a certain subject; Kylin
>integrates well with Hive, Hadoop, Spark, Kafka, and other systems; Kylin
>incremental loads the data by time, build the cube and then save as
>segments (partitions), and they are non-volatile unless you refresh them;
>During the analysis (roll-up, drill-down, etc), the data is always
>consistent. Kylin provides SQL interface and JDBC/ODBC/HTTP API for you to
>easily connect from BI/visualization tools like Tableau and others.
>All in all, you can see that users are using Kylin not just as a SQL
>engine, but also as an Analytical Data Warehouse, for very large scale data
>(PB scale). In the world of big data, Kylin is unique. Its design is
>elegant, its architecture is scalable and pluggable.  In order to give
>Kylin more visibility and can be discovered by more people, I propose to
>change Kylin's position/slogan from the "OLAP engine for big data" to
>"Analytical Data warehouse for big data".
>Please feel free to share your comments.
>Best regards,
>Shaofeng Shi 史少锋
>Apache Kylin PMC
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