3 Data Integration Technologies, 1 Common Foundation

I’ll be speaking with Eric Thoo of Gartner on a webcast on June 13 entitled “Data Integration Styles: Choosing an Approach to Match your requirements.”  Click here to register – http://bit.ly/KLhZ7J 

In the webcast, we will go into detail on three styles of integration: bulk data movement, real-time, and federation.  Bulk data integration involves the extraction, transformation, and loading of data from multiple sources to one or more target databases.  One of the key capabilities of bulk integration is extreme performance and parallel processing.  Batch windows continue to shrink and data volumes continue to grow; and the new wave of big data puts even more emphasis on batch integration performance.  Real-time integration involves replication and low-latency integration.  It is often uses to syncrhonize operational databases and to power real-time reporting and analysis.  Federation is a completely different approach – it leaves data in place and allows users to access it via federated queries.  This style of integration is very important for operational systems and it is a cost-efficient complement to batch integration – only move what is necessary, leave the rest in place and access it as required.   In the webcast Eric Thoo will provide details on each style and the uses for each.

These three styles of integration should not be independent and discrete from one another.  They should share something in common – a foundation that establishes trust in information.  A foundation that profiles data quality, improves the accuracy and completeness of data, tracks its lineage, and exposes enterprise meta data to facilitate integration.  Client’s derive real value from a common approach to all three styles because they leverage a common foundation for information trust – common rules for data quality, meta data, lineage, and governance. 

On the webcast we will explore the specific requirements for which each style is suited.  If you look at the larger IT project, typically all three styles are required.   For example, supplying trusted information to a data warehouse will require bulk data integration, but for specific reporting needs it may also need real-time integration, and potentially even federation to access other data sources.  Building and managing a single view with MDM will again require bulk integration to populate MDM, real-time integration both to and from the MDM system, and federation to augment MDM’s business services to blend data stored within MDM and data stored in other source systems. 

The common foundation of trust and governance, and the need to use multiple technologies are the keys to making a strategic choice of technology – one that you can leverage across the life of a project and into other projects that require integration.

Please join us on June 13, when we will share more details on this topic.

Register here http://bit.ly/KLhZ7J 


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About David Corrigan

I’ve spent my entire career helping clients utilize emerging technology to solve their customer data problems. I've always enjoyed solving abstract problems. I've worked with hundreds of companies to utilize new technology, plan and drive to a roadmap, and evangelize and drive momentum for their information projects. During the day, I work on product strategy and marketing for @InfoTrellis, and I'm busy trying to disrupt the customer data and analytics market so that organizations can finally understand every single one of their customers. After hours, I like to take photographs, read, write, practice yoga, or watch soccer - Manchester United and Toronto FC are my teams of choice. Follow me on Twitter @DCorrigan or on LinkedIn at http://ca.linkedin.com/pub/david-corrigan/3/aa3/92.

One response to “3 Data Integration Technologies, 1 Common Foundation”

  1. Eric Thoo says :

    When business requirements change and applications require new and different data, or integrated views of data, and as pressures surmount, an organization’s data integration focus is compelled to adapt. Before the various crucial aspects we will talk about in the upcoming webcast, a prelude to moving forward — it is possible to achieve a data integration environment that is adaptable — dynamically changing to optimize the delivery of data for the business services and applications that will consume it. Data integration capabilities are increasingly driven to support enterprise-scale deployment, address comprehensive range of use cases, and provide an architecture that can deliver data at variable degrees of latencies, granularities and virtualization. On the other hand focusing only on a single approach to data integration will limit agility and success. Along the way, a mainstay of the discipline is in gaining clarity in the data integration problem space — that is foundational to setting a strategic vision for data integration.

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