Enterprise data architecture of complex distributed applications & services

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Enterprise data architecture of complex distributed applications & services Davinder Kohli

Transcript of Enterprise data architecture of complex distributed applications & services

Page 1: Enterprise data architecture of complex distributed applications & services

Enterprise data architecture of complex

distributed applications & services

Davinder

Kohli

Page 2: Enterprise data architecture of complex distributed applications & services

Data Metamorphosis

Process

Requirements

Architecture/Desig

nBusi

nessData App Tech

Implementation

Business/Data Transfer

Objects

Deploy/Test

Data

Provisioning

Test

Scenarios

Use Cases

Page 3: Enterprise data architecture of complex distributed applications & services

Data – lifeblood of an

organization

Data is an asset

Data has economic value

Data must be shared and easily accessible

Data must have common terminology & definitions

Data needs to be secured

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SOA Ecosystem - Use

Case

Page 5: Enterprise data architecture of complex distributed applications & services

UI

Confused? Where do I start?

Http

Session

Service A Service B Service C

Service

C1Service

C2Service

C3

Service

A1Service

A2Service

A3

Service

B1Service

B2Service

B3

Service A31

Service A32

Service A33

Service B31

Service B32

Service B33

Service C31

Service C32

Service C33

Service A2, A33, B1, B32, C1, C31

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Data Flow/Mapping - facet of Data Architecture

Why? Identify sources of data

Define data interrelationship

Flow of information through the app’s ecosystem

Public/non-public information

Data provisioning for testing

What? UI - data rendering, form submissions

Complex Services – requests, responses

Unnecessary data – movement, duplications

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Absence of Data Flow/Mapping Implementation

Longer implementation cycle

Too much information to figure out Redundant data objects Too much data movement

Testing Confusion during data provisioning Lack of coordinated datasets Longer testing cycle

Quality More unit tests More lines of code

Performance degradation

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Challenges in data mapping Getting buy-in from stakeholders

Lack of data dictionary

Silo’d resources – technology, people, process (release cycle)

Evolving interfaces – WSDL, DB Schema

Long term maintenance

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Approach/Solution Approach

Top down – Business cases

Bottom up – Existing interfaces, WSDLs

Resource alignment – people, artifacts

Artifacts

Data Mapping/Flow Sheet

Analysis of data flow/mapping

Reduce data movement

Identify redundancy of data sources

Gaps in mapping

SOT

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How to build data mapping?

Sample

UI

IxD CCLUse

Cases

Data

Mapping

WSDLsWSDLs

WSDLs

Bottom Up

Top Down

CSD

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Demo

Wanna checkout

my data

flow/mapping?

Page 12: Enterprise data architecture of complex distributed applications & services

Artifact Creation Approach

Data Model Beans

WSDL(s)

Data Mapping

File

UIWSDL(s)WSDL(s)

Data Mapper

Utility

(Apache

POI,JAXB)

Data Model Beans

WSDL to

Java

Manual

Creation

Java AdaptersXSLT Transformers

Mapping done

manually by

developer

Manual CreationManual Creation

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Questions?