7/17/2019 The Game Changers of Integration

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I N T E G R AT I O N 

C H AN G E R S  O F   T H E

  G A M E

I N S I D E  &  O U T 

7/17/2019 The Game Changers of Integration

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There are several disruptive market forces at play that are dramatically increasing the amount and complexity

of data that organizations must deal with. These game changers are compelling organizations to rethink their

integration strategies.

The world of data is changing rapidly...

THE GAME CHANGERS OF INTEGRATION

MARKET FORCES DISRUPTING INTEGRATION

Enterprises are being ooded with new types of unstructured and semi-

structured data such as social media interactions; video; images; and

the sensors and devices that comprise the rapidly expanding Internet

of Things. This abundance of data is ultimately a boon, but the sheer

variety, volume, and velocity of it is disrupting every aspect of traditional

data handling—including integration. What’s more, big data has raisedexpectations and, as a result, integration operations are being asked to do

more than ever before such as process streaming data or materialize data

on demand.

The benets of cloud delivery—

speed of implementation,

exibility, scalability, and lower

costs to name a few—are spurring

on the breakneck adoption of

Software as a Service (SaaS). But

as some applications make the

 journey to the cloud while othersremain on premises, more and more enterprises are nding themselves

straddling hybrid environments. This divide between on-premises and

cloud infrastructure adds yet another layer of complexity to the already

complex task of integration.

As the cloud increases its foothold

as the preferred delivery method

for applications, the role of the

once-monolithic ERP system

is steadily diminishing. Cloud

delivery eases development

barriers, allowing providers totarget very specic business

problems with highly specialized

SaaS applications that often run circles around the legacy-bound ERP

modules they replace. Classic integration models such as the enterprise

service bus (ESB) are too resource intensive to keep pace with this

proliferation of endpoints.

BIG DATA

CLOUD

DECONSTRUCTION

OF THE ERP

Global SaaS software revenues

are forecasted to reach $106B in

2016, with over 20% growth in

2015 and 2016.1

“By 2020, fewer than 20% of

multinational organizations will

continue to plan and adopt an

ERP strategy based on a single-

instance megasuite.” 

2

1Forrester Research, The Global Tech Market Outlook For 2015 To 2016, January 2015

2 Gartner, Postmodern ERP Operations Management Best Practices, May 2015

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One good game changer deserves another. Transformative new integration technologies and approaches are

emerging to provide the highly agile and adaptive integration required to satisfy the demands—and realize the

full potential—of today’s exciting data landscape.

Are your integration operations ready?

TRANSFORMATIVE NEW INTEGRATION TECHNOLOGIES AND APPROACHES

THE GAME CHANGERS OF INTEGRATION

One of the biggest trends inintegration is the shift from atraditional ESB (enterprise servicebus) architecture, which reliesheavily on network logic and

processing, to a microservicesarchitecture that employs small,independently deployable,modular services as applicationbuilding blocks. A key advantageof microservices is the ability toscale or modify specic application functions without having to rebuildthe entire application. When you consider the proliferation of applicationsand data sources enterprises must account for today, it’s easy to see whythe resiliency, scalability, and agility aorded by this modular architecturepattern is highly desirable.

Polyglot persistence is the use of multiple data persistence technologiesto optimally store and assimilate varying types of raw data (i.e., a datastore might consist of a relational database for nancial data, a documentdatabase for product information, and a key-value database for social mediacontent). Schema-on-read is the process of applying a schema to this rawdata at the time it is being pulled out of the data store (rather than priorto it being written to the data store), allowing the schema—and its outputformat—to be dened on the y through self-service data managementtools. By persisting data in native form and allowing for easy visualization atread time, data can be reused across a variety of use cases—even those yetto be determined.

Traditionally, integration has been focused purely on moving data from one

system to another and the data is left “as is” once it reaches its destination.In this application-centric approach to integration, the data exists to servethe applications and integration is little more than a wire connecting oneapplication to another. A data-centric approach to integration, on the otherhand, recognizes that the data itself oers the most value and takes greatcare to persist all iterations of the data, along with the event data of thedata (i.e., metadata) as it ows across the enterprise. This approach not onlymoves the data about as necessary, but, in perfect alignment with the needsof big data, also stores it for future materialization in whatever format isrequired by the use case at hand.

MICROSERVICES

ARCHITECTURE

POLYGLOTPERSISTENCE &

SCHEMA-ON-READ

DATA-CENTRIC

INTEGRATION

Netfix, PayPal, Twitter, eBay and

many other large-scale websites

and applications have evolved

 from monolithic to microservicesarchitecture to be able to better

respond to ever-shifting demands.

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Contact Us / info@liaison.com   / 1-866-336-7378  / www.liaison.com

Today’s data initiatives require both integration and data managementif quality data is an expected outcome. Historically, integration and datamanagement functions have operated in silos—and largely continue todo so today. But considering that both disciplines must work with thesame varied data sources and APIs, and both are required to solve most

of today’s data problems, this separation introduces many redundancies.By unifying integration and data management onto a single platform orsolution, enterprises can solve more business problems than before—suchas streaming data processing—with less overhead.

As big data, cloud delivery, and the proliferation of applications continueto complicate the integration landscape, enterprises can no longer aordto be in the integration business. Considerable eort and resources areput into maintaining integration’s basic extract, transform, and load (ETL)functions alone, tasks that in and of themselves provide no business value.In the newly emerging managed services model, these integration functionsare assumed by the provider, allowing the customer to focus solely oncontrolling the data, which is where the business value lies. In addition,

a managed services model allows enterprises to stay relevant in today’sfast-changing data environment without having to invest in the integrationtechnologies themselves.

THE GAME CHANGERS OF INTEGRATION

UNIFIED

INTEGRATION

& DATA

MANAGEMENT

MANAGED

SERVICES

DELIVERY MODEL

Data Platform as a Service (dPaaS) is a new market category of cloud-based platforms that embodies all of the

transformative integration technologies and approaches discussed here. Reconceived from the ground up to

directly address the disruptive technologies complicating today’s integration operations, it’s a better way of

integrating the enterprise. Learn more about dPaaS and the industry’s rst ever dPaaS solutions, the Liaison

ALLOY™ Platform, at www.liaison.com/liaison-alloy-platform.

About Liaison Technologies

As a leader in cloud integration solutions since 2000, Liaison Technologies is shaping the integrationmarketplace with innovative solutions designed to meet today’s toughest data challenges. From complex

integration to data management to the brave new frontiers of big data, our secure solutions break down data

silos, reduce ineciencies, and uncover actionable insights.

INTRODUCING dPaaS