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.
There is also 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.
It is necessary for one application to immediately have real-time access to another application’s updated data, 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 specific 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.
On top of all these applications, there is now 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 — they are all contributing to big data.
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.
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.
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.
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.)
