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Data Integration and Synchronization Solutions

Integrate Data for Competitive Advantage

Over time, the number of legacy applications developed to support an enterprise’s operations grows significantly. Applications maintain databases of information, but the database contents are not typically exposed for use by other independently developed applications.

As companies grow more reliant on their IT resources, it is apparent that many are storing data in silo legacy databases. As companies merge, it is often necessary to join disparate databases into a common repository for the new corporation.

Application Capacity Expansion

Application capacity is a key aspect of IT resource scaling

Application capacity defines how many users an IT application service can support in a given response time. Another critical factor is the application’s ability to scale, which is dependent upon the application’s architecture.

The two most important scaling factors for an application are increasing the number of users that can be processed:

  • With the current environment (scaling up)
  • By scaling across environments (scaling out)

Scaling up usually consists of replacing existing hardware with more powerful versions, whereas scaling out usually consists of adding more processing power by spreading the load across multiple environments. Scaling up is often limited by the maximum capacity that a single monolithic environment can achieve.

Learn More About Application Capacity Expansion

Big Data

The amount of information being generated each year is exploding at an unprecedented rate

It is estimated that 80% of the world’s data was generated in the last two years, and this rate is increasing. Social media such as Twitter and Facebook, articles and news stories posted online, blogs, emails, YouTube and other videos are all contributing to big data.

In today’s 24×7 online environment, having query access to a remote database is not sufficient. Querying for data is a lengthy and complex process, and applications must react far more quickly to data changes than querying allows.

HPE Shadowbase Streams

What is needed is a way for one application to immediately have real-time access to the data updated by another application, just as if that data were stored locally

Furthermore, big data analytics engines require a large network of tens, hundreds, or even thousands of heterogeneous, purpose-built servers, each performing its own portion of the task.

Since these systems must intercommunicate with each other in real-time, they must share an integrated high-speed, flexible, and reliable data distribution network.

Discovering Meaningful Patterns Using Data Integration

Applications that once were isolated can now interoperate at event-driven level in real-time

Critical data generated by one application is distributed and acted upon immediately by other applications, enabling the implementation of powerful Event-Driven Architectures (EDA).

Several production use cases are included that illustrate how this data distribution technology brings new opportunities and value to various enterprises.

Shadowbase User Exits


Users have the option to write a Shadowbase User Exit to extend Shadowbase replication to perform additional processing with either scripting or via embedding custom code.  These user exits allow Shadowbase to handle any data manipulation, filtering, or cleansing required.

Shadowbase User Exits

  • Transformation and mapping facility
    • ASCII text file of Transformation Commands. Read at Start up.
  • User Exits C/C++, COBOL, or TAL APIs
    • Non-native and native code supported
    • Data scrubbing and cleansing
    • Field splits and merges
    • Aggregation and summations
    • Integrate data from other databases
    • Any target environment with an API that Shadowbase User Exits can access
    • Sophisticated data selection, filtering and transformation

Note: Shadowbase replication can feed data directly into a target database or into a target environment via an API (e.g., MQ Series, Java Messaging Services, ODBC, etc.)

HPE Shadowbase Streams for Data Integration and Synchronization Supports

Data Transformation
  • Database Specification (DBS) Mapping
  • Shadowbase Map (SBMAP)
  • Shadowbase Data Definition Language Utility (SBDDLUTL)
  • Shadowbase User Exits
  • Miscellaneous Additional Transformation Methods
Zero Data Loss (ZDL)
  • Elimination of data loss in the event of an outage
  • Elimination of data collisions in active/active architectures (future release)
Master Data Management
  • Uni-directional and bi-directional data synchronization
  • Transformation, filtering, cleansing, and consolidation of data
  • Same (homogeneous) or different (heterogeneous) source and target databases and platforms
  • Data warehouse feeds
  • Real-time data replication
  • Offline and online loading/integration
  • Trickle-feed and batch refreshing
Build Real-Time Event-Driven Applications
  • Rapid and reliable reformation and transference of large data amounts between heterogeneous databases and applications in real-time
  • Data distribution backbone for a big data analytics system
  • Elimination of middleware and application modification