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CAS complex simulations case study

Wyle’s CAS Group reduces development cycles, costs, and experiences significant performance gains...

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Software supply chain series part 1: Three ways to secure your software supply chain

Organizations are really concerned with cyber-attacks, but not all organizations know the best way to address the issue...

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Introduction to continuous integration with Klocwork

Learn how you can use continuous integration within Klocwork to find and fix defects faster.

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From false positives to trust: A story of SCA adoption

Elektrobit is at the heart of embedded, supplying HMI technologies, navigation, electronic control units, and software for over 70 million vehicles and over 1 billion embedded devices.

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Overcoming relational database limitations with NoSQL

​Relational databases are the workhorses of the modern database industry. They have limitations, however, when it comes to handling some types of data, in particular the large quantities of free-form data generated through mobile technology. NoSQL databases provide solutions for some of these problems but they introduce another problem in having no single query language that drives them.

This paper examines the problem, surveys the solutions, and answers the question of how to implement the solutions through a consistent API.

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Tech tutorial: Embedding analytics into a database using SourcePro and JMSL

There are numerous benefits to using embedded analytics including real-time analysis, faster results, better quality of data, and higher security.

This white paper describes how to implement embedded analytics within a database using SourcePro and the JMSL Numerical Library, a native Java library from Rogue Wave Software. It describes in detail how to implement embedded JMSL using a particular relational database management system (RDBMS). As well as query the embedded JMSL in RDMBS using SourcePro DB.
 

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Using SourcePro DB with Microsoft Azure SQL Database

SourcePro DB was created long before cloud computing. So it may be a surprise to some that it that SourcePro DB can simplify your use of the Microsoft Azure SQL Database.

This paper describes how to connect and use SourcePro DB with Mircosoft Azure SQL Database, a relational database-as-a-service offering on Microsoft Azure cloud. It also covers the use of the elastic database feature.
 

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Using Redis with SourcePro DB

SourcePro DB provides a unique feature to manage both relational and NoSQL data through the ODBC connection support allowing connections to industry-leading databases.

This paper describes how to connect and use with Redis, a NoSQL database specialized as a key-value, in-memory data structure store.

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Using MongoDB with SourcePro DB

SourcePro DB provides a unique feature to manage both relational and NoSQL data through the ODBC connection support allowing connections to industry-leading databases.

This paper describes how to connect and use with MongoDB, a document-based database whose document-oriented model allows it to automatically split data across multiple servers.

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Using Cassandra with SourcePro DB

SourcePro DB provides a unique feature to manage both relational and NoSQL data through the ODBC connection support allowing connections to industry-leading databases.

This paper describes how to connect and use with Cassandra, a distrubted database designed to manage large amounts of structured and unstructured data across many servers.

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Tech tutorial: Embedding analytics into a database using SourcePro and IMSL .NET

There are numerous benefits to using embedded analytics including real-time analysis, faster results, better quality of data, and higher security. This white paper describes how to implement embedded analytics within a database using SourcePro and the IMSL .NET Numerical Library, a native C# library from Rogue Wave Software. It describes in detail how to implement a server-side native stored procedure leveraging IMSL .NET using a particular relational database management system (RDBMS), and execute the procedure in RDBMS using SourcePro DB.

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Using JMSL in Apache Spark

Data in all domains is getting bigger — and because more data presumably equates to more accuracy in measurement, “big data” is generating the promise of new insights and understanding.However, the technical challenges posed by big data can overwhelm engineering teams who are
seeking ways to optimize their work effort.

This technical white paper illustrates two examples on how to use JMSL classes on Spark resilient distributed datasets (RDDs) to leverage the advanced mathematics and statistics algorithms in JMSL in distributed Spark applications written in Java.

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