Get under the hood: This technical note describes how to transform XML response into a JavaT-usable response by creating Plain Old Java Objects (POJOs) and calling JAXB's unmarshal method. Simplify how to send requests to create new instances based on images that request parameters from Java classes.
How can you open your analytics program to all
types of programming languages and all levels of
users? And how can you ensure consistency across
your models and your resulting actions no matter
where they initiate in the company?
With todayís analytics technologies, the conversation
about open analytics and commerical analytics is no
longer an either/or discussion. You can now combine
the benefits of SAS and open source analytics
technology systems within your organization.
As we think about the entire analytics life cycle, itís
important to consider data preparation, deployment,
performance, scalability and governance, in addition
to algorithms. Within that cycle, thereís a role for
open source and commercial analytics.
For example, machine learning algorithms can
be developed in SAS or Python, then deployed in
real-time data streams within SAS Event Stream
Processing, while also integrating with open systems
through Java and C APIs, RESTful web services,
Apache Kafka, HDFS and more.
Published By: Quocirca
Published Date: Apr 09, 2008
Today, many organizations are increasingly reliant on software application development to deliver them competitive edge. Simultaneously, they are progressively opening up their computer networks to business partners, customers and suppliers and making use of next-generation programming languages and computing techniques to provide a richer experience for these users. However, hackers are refocusing their attention on the vulnerabilities and flaws contained in those applications.