3-5 Kyligence Cloud 云上数据湖分析的竞争优势分析.pdf

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1、KYLIGENCE CLOUD云上数据湖分析的竞争优势分析马洪宾 技术合伙人、首席布道师|技术撑竞争优势业务问题About KyligenceFounded in 2016 by creators of Apache KylinLeading Open Source OLAP for Big Data1500+Global Open-source AdoptionsSuccess Enterprise cases with Industry Leaders24*7 Global SLA,ISO 9001,ISO 27001,SOC2 Type 1Dual-head quarters in Sa

2、n Jose,USA and Shanghai ChinaVenture backed by top investors:Redpoints,Cisco,CBC,Shunwei,8Roads(Fidelity arm),Coatue,SPDBI,CICC,Gopher,ASG,etc.Kyligence=Kylin+IntelligenceAnalytics on Data Lakes3 Major Pains in a Self-Serving Data LakeFor multi-dimensional analytics scenariosTrust in data?Has a lot

3、of data,but little trustSum(Dept.Revenue)Total Revenue?Muddy lake?Everyone is creating their own data setWide tables,ETLs,every where,ever growing“Dont reuse that table.What if it gets changed?!”Cost?With 100 x more users,comes 100 x IT costCheaper is always betterThe Muddy Lake from an Internet Gia

4、nt in ChinaWhen discovery,free-form,self-serving went to its extremeDBEventLogData AppReportBI/AI5.7 kODS tables1 mwide tablesWide TablesAggregated TablesODS TablesJoinsAggr2 years data construction since 2019 H2,reaches a muddy lake:-Massive data expansion:5.7k ODS tables grow into 1 m tables-Crazy

5、 linage:Table TX_ORDERS has 10k direct descendants-Many duplicated ETL and wasted computation-No unified business semantics,no trust in data-Expect-Save tens of millions RMB every year once metrics are governed-Improve analytics efficiencyHow Multi-Dimensional Data Model HelpsDBDBLogData AppReportBI

6、/AIDimensional Models&MetricsStandardizationGoverned InnovationBase&DerivedMetricsHow Multi-Dimensional Data Model HelpsOrganize your data asset5.7 kODS tablesDBDBLogData AppReportBI/AIDimensional Models&Metrics2 kData Models10 kDerived MetricsStandardizationGoverned InnovationMulti-Dimensional Data