Data Integration and Synchronization Solutions

Application Capacity Expansion (ACE)

Application Capacity is Key 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.

Related Content

Articles: Scale-up is Dead, Long Live Scale-out!

Use Cases: Asymmetric Capacity Expansion for Nuclear Plant Management

Use Cases: Asymmetric Capacity Expansion for Real-Time Cell Phone Fraud Detection

Use Cases: Offload Querying/Reporting from the Host to Commodity Systems

Use Cases: Scale Look-to-Book Ratio for Online Travel Agent System

Big Data Solutions

Information is Exploding at Unprecedented Rate

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.

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Big Data: Crunching Big Data

Briefs: HPE Shadowbase Streams for Data and Application Integration Solution Brief

White Papers: HPE Shadowbase Total Replication Solutions Product Datasheet

Data Transformation Solutions

Transform Data between Source and Target Database Formats

Shadowbase software can transform the data between source and target database formats either automatically or via Shadowbase User Exit customizations. Data may be aggregated, disaggregated, and/or transformed.

• Database Specification (DBS) Mapping for specifying which source tables need to be transformed into which target tables
• Shadowbase Map (SBMAP) for mapping a data source and target in a “scripting” like language (also known as creating a data transformation stream). No programming is required!
• Shadowbase Data Definition Language Utility (SBDDLUTL) for database schema conversions
• Shadowbase User Exits, extending Shadowbase replication to perform additional processing with either scripting or via embedding custom code
• Limitless Transformation for extracting even more value from your data

Discovering Meaningful Patterns Using Data Integration

Applications Interoperate at Event-driven Level in Real-time

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.

Related Content

Articles: Data Replication and Integration – Applications in Financial Services

Presentations: Prescription Drug Fraud Prevention – An HPE Shadowbase Data Integration Case Study

HPE Shadowbase Streams

Diverse Applications Interoperate at the Data Level

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.

Related Content

Articles: Easily Modernize and Expose Valuable Enscribe Data – Eliminate Data Silos

Case Studies: Large International Tour Operator Uses HPE NonStop to Optimize Look-to-Book Processing

Use Cases: Paper Manufacturer Offloads Querying and Reporting to Commodity Servers

Case Studies: Two Merged Retailers Integrate HPE NonStop SQL and Oracle RAC