In this section, we will discuss how AWS Blu Age can help meet your needs for mainframe migration and modernization projects. AWS Blu Insights through the lifecycle of a Modernization Project: In the next section, we will learn about the features of AWS Blu Insights with the use case of a common modernization project. Technical tools help with inventory analysis, code assessment, code transformation, and test case capture. These transformed applications may rely on Angular, Java/Spring, PostgreSQL, or other databases such as Amazon Aurora, Amazon RDS for PostgreSQL, Oracle database, IBM Db2.ĪWS Blu Insights provides tools that support the full process of modernizing source code from legacy applications and their databases. Applications in COBOL, generated COBOL, PL/1, NATURAL, RPG/400, COBOL/400 and their respective underlying databases and data files DB2, DB2/400, VSAM, IMS, IDMS are transformed into modern distributed applications. AWS Blu Age Refactor automatically creates modern applications from legacy monolithic mainframe or midrange source code. It does this while expediting the reliable transition to Java, new data stores, and web frameworks. It relies on fully automated refactoring by preserving the investment in business functions. It can convert languages like COBOL, PL/1, NATURAL, RPG/400, and COBOL/400 into agile Java services and web frameworks. It also helps them to gain access to a growing pool of candidates with experience running and automating workloads with AWS.ĪWS Blu Age uses automated refactoring patterns within the AWS Mainframe Modernization service. These advantages help them to increase their agility, their capacity to innovate, and to benefit from the continual trends with AWS of improving cost/performance ratios. Customers are looking to modernize their mainframe-based applications to take advantage of the AWS Cloud. This becomes important if the server has no fixed location or it moves in its location.Īs always, the source code for this article is available over on Github.According to Reuters, there are 220 billion lines of code in customers’ production environments running Cobol and other legacy languages. Make the config server available at Spring Netflix Eureka service discovery and enable automatic server discovery in config clients.Therefore, we must set some properties and/or we must remove the annotation, which depends on the use case. Embed the config server into an application, where it configures itself from a Git repository, instead of running as a standalone application serving clients.For instance, this can be useful to provide an environment-dependent logging-configuration. Serve plain text configuration files in turn, optionally with resolved placeholders.This can be useful when using it in non-Spring environments, where the configuration isn't directly mapped to a PropertySource. Serve configuration in YAML or Properties format instead of JSON, also with placeholders resolved.There are also a few other things we can do with such a server. Now we're able to create a configuration server to provide a set of configuration files from a Git repository to client applications. To test if our setup works correctly, we'll modify the ConfigClient class and restart our client: class ConfigClient String password įinally, a query against our client will show us if our configuration value is being correctly decrypted: $> git commit -am 'Added encrypted password' The main part of the application is a config class, more specifically a which pulls in all the required setup through the auto-configure annotation class ConfigServer $PASSWORD" > config-client-development.properties
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