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Enterprise Information Integration

Integrate Data Using a Virtual Paradigm

Few will argue the value of data integration. The business value that results from timely integration of disparate data is significant and growing.

Many Options for Data Integration

Given the business value, it is not surprising that IT and IT vendors have provided a range of solutions to address the data integration challenge.

"Data Integration provides a unified view of the business data that is scattered throughout an organization. This unified view can be built using a variety of different techniques and technologies.”

Data Integration: Using ETL, EAI, and EII Tools to Create an Integrated Enterprise
Nov 2005
Colin White, President, BI Research
Copyright © 2005 BI Research, Inc

These techniques may include:

• Federation. A virtual federated view of disparate data assembled dynamically at data access time
• Consolidation. A physical view of data captured from multiple disparate data sources and consolidated into an integrated data store like a data warehouse or operational data store
• Propagation. A propagated view of data created by synchronizing data from one database to another such as product data across manufacturing, supply chain, and order management systems.

As a replacement to hand coded data integration, three data integration middleware technologies have evolved to support these three techniques including:

• Enterprise Information Integration (EII) for federating data
• Extract, Transform, and Load (ETL) for consolidating data
• Enterprise Application Integration (EAI) for propagating data.

EII: Leveraging Data Virtualization, Abstraction, and Federation

In the same article, Mr. White defines EII in more detail highlighting how it can be used to virtualize, abstract, and federate data.

"EII provides a virtual business view of dispersed data. This view can be used for demand-driven query access to operational business transaction data, a data warehouse, and/or unstructured information. EII supports a data federation approach to data integration.

The objective of EII is to enable applications to see dispersed data as though it resided in a single database. EII shields applications from the complexities of retrieving data from multiple locations, where the data may differ in semantics and formats, and may employ different data interfaces.”

EII Avoids Unnecessary Data Replication

The key distinction of EII compared to other integration technologies is that data is not permanently moved or replicated into a new location or server; rather the source data remains where it lives and results persist in the EII information server only as needed for caching.

The advantages of this approach are several.

• Real-time information. Turn diverse data into up-to-the-minute insight. Federate operational and historical data. Stop perpetuating uncontrolled data extracts and additional marts.
• Build in days, not weeks. Develop new solutions quickly and easily. Reuse abstracted data services. Minimize low productivity hand coding. No need to build and test marts.
• Eliminate unnecessary data replication. Use your data where it lives. No replication required. Stop perpetuating uncontrolled data extracts and additional marts.

On the other hand, because the data remains in the source systems, impact on production system performance can sometimes be an issue. However, with advancements in network bandwidth, available memory and CPU power, as well as optimized queries techniques and algorithms, EII has overcome most performance concerns and can now support nearly any “less than warehouse scale” use case.

EII Complements Other Data Integration Technologies

Because of these advantages, EII is often used to complement other data integration techniques.

EII is especially effective and delivering real time information. So in cases where you need to combine real-time and historical data, for example a single view of customer activity, you can use EII to federate real-time operations data with ETL-driven data warehouse data.

EII is often used to integrate data for data mart scale projects, providing a faster, lower cost complement to larger scale, multidimensional, ETL-driven data warehouses.

Because EII avoids physical warehouse builds and testing, EII is also useful as a rapid development technique for large data warehouse projects. Getting the data right and running in EII can be four times faster than ETL. Subsequent translation of EII views into ETL plans and warehouse schemas is a snap.

Due to its powerful query and abstraction capabilities, EII complements EAI by simplifying access to frequently required business process data. This lets the analysts and programmers focus on getting the business process right, and not worry about getting the right data.

EII: Providing Value Beyond BI and Reporting

At this point, EII is a widely accepted data integration technique typically used in Business Intelligence and Reporting initiatives to provide real-time information, sooner, for less.

In the past two years, this value has extended beyond initial Business Intelligence and Reporting use, and has now become the key technology enabler in the following areas:

• Data Virtualization
• SOA Data Services
• CDI-MDM

Composite: Best of Breed for EII

Composite was an early leader in the EII market, arriving on the scene in 2003 and gaining wide acceptance including integration of the Composite Information Server inside leading software vendors products including Cognos, Informatica, and BMC and strong customer adoption on Wall Street.

While other early competitors have gone out of business or have been acquired, Composite remains the EII product of choice, continuing its rapid revenue and functionality growth by helping our business and IT customers to:

• Virtualize data silos. All your data appears in one logical location. Up to the minute. Readily available on demand.
• Abstract away complexity. Data the way your business solutions want to consume it. Easy to understand. Reusable.
• Federate heterogeneous data. Securely access and combine diverse operational and historical data. Provide single views and other composites. Query optimization for high performance.