2025AICon全球人工智能开发与应用大会-深圳站：构建AI原生的全球化数据分析架构跨越实时、成本与合规鸿沟

演讲人：杨勇强●AI

时代的实时性、全球化、成本与复杂性鸿沟●

AI

时代实时数据分析进化.

AI

实时数据分析实践案例AI

时代的实时性、全球化、成本与复杂性鸿沟#1

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ContributorsApache

Doris：

Open-Source

Real-TimeAnalyticsandSearch

Database2017

Open

Source640+Contributors2013

Project

Creation2022

ASF

Top

Project13k+

Git

Hub

Stars5000+Enterprises数据技术栈臃肿，维护复杂性高

AI

时代的实时性、全球化、成本与复杂性鸿沟AI

对数据实时性和查询速度要求越来越高LLM

带来新的数据安全和合规风险海量数据存储分析成本高昂AI

时代实时数据分析进化AI-boostedexistinganalyticalworkloadsAI-drivennewanalyticalworkloads

AI

时代数据分析进化AI-boostedexistinganalyticalworkloadFaster,

more

real-time,

moredata,

more

unstructuredtextReal-Time

Analytics

Data

Warehousing

ObservabilityWhyChoose

Doris-Real-Time

Ingestion&

Update:~

1s

minimum

data

latency-Blazing-Fast

Analytics:<

100ms

average

query

latency-High-Concurrent

Queries:>

10,000QPS

maximum

query

concurrency-Agent

Native:

Doris

MCPServerReal-TimeAnalyticsFrom

User-FacingAnalyticstoAgent-FacingAnalytics,

more

real-timeandfasterUser-FacingAnalyticsAscloudcomputingand

SaaS

software

become

more

popular,embeddinganalytics

intoapplications

iscrucial.This

is

also

called

customer-facingor

user-facinganalytics.OrderAnalytics｜

AdvertisingAnalytics｜

InventoryAnalyticsAgent-FacingAnalyticsWiththe

riseofAItechnologies,especiallyAI

Agents,

moreanalyticaldecisionswill

be

madeautomatically

byAI.Thiswill

improveefficiencyandaccuracy

in

decision-making.Fraud

Detection｜

Ad

Serving｜

Personalized

RecommendationOpen

Data

LakehouseLakehouse

isdesignedfor

bothAIandanalyticsworkloadWhyChoose

DorisBuildthe

Fastest

Real-Time

DataWarehouse

inthe

Lakehouse

，

replacingTrino/Presto

，

SparkSQL

，

...-

Fast:

3xtoTrino/Presto-

Open-

Unified

DataWarehousingFromtriditionalwarehouseto

Lakehouse

+

Real-TimeAnalytics

Database(Doris)HistoryofObservability2013:Theconceptofobservabilitybegantogain

traction

as

companies

like

Twitter

started

to

adopt

it

to

managetheirincreasinglycomplexdistributedsystems.2020:Observabilitybecamea

hottopicin

DevOps,with

more

companies

recognizing

its

importance

in

managingcomplexIT

architecturesMetricsTracesLogsWhyChoose

Doris-10x

Cost

Effective

Compared

to

Elasticsearch-Flexible

Semi-Structured

DataVariant

Type-Open

Integrationfor

ELK,

OpenTelemetry,Grafana

and

more

ObservabilityFromCloud

Native

+

Microserviceto

LLM

+AIAgent,

moredataandtextAI-boostedexistinganalyticalworkloadsAI-drivennewanalyticalworkloads

AI-driven

NewAnalyticalWorkloadWhyVector-CapableGeneral-Purpose

Databasesare

Betterfor

Enterprise

GenAI？-

Lackof

Hybrid

Query

Capabilities-

Limited

Integrationwith

Structured

Data-Operational

Complexity

and

Increased

Costs-

Largescale

datavolume,

eg

PBs

Hybrid

SearchAI

Operators

in

SQLIntegratingGenAIfor

EnhancedTextAnalysisin

DatabaseLLM_SUMMARIZELLM_TRANSLATELLM_SENTIMENTLLM_CLASSIFYLLM_GENERATELLM_EXTRACTLLM_FILTERLLM_SIMILARITYLLM_FIXGRAMMARLLM_MASK实践案例“基于SelectDB

的新系统已接入

MiniMax

内部所有业务线日志数据，满足

10GB/s高吞吐实时写入和秒级查询的需求通过存算分离比自建

Doris计算资源降低40%，热数据存储资源降低

50%”“腾讯音乐内容库基于

Doris替换

ES

和

CK，同时满