The Manta platform is a unified commercial product for data flow analysis and vi- sualization. Manta platform supports many technologies. One category of technologies is intermediate languages. These include, for example, Java or C#. There is a scanner for each of these technologies. This thesis focuses on extending the C# Scanner with support for database and ORM related plugins. First, we provide a brief description of how the Manta platform works. Then we quickly introduce the C# Scanner, some of its important parts, and explain some of the concepts and algorithms that are used to analyze C# code. This is followed by an analysis of the context of ORM frameworks and Entity Framework Core from the perspective of data flow analysis. After explaining the context, we describe the C# Scanner extensions, solutions and troubleshooting. Towards the end of the thesis we describe support for one specific ORM framework - Entity Framework Core. Again, we discuss problem solving and solution description. At the end of the thesis it is possible to find limitations of the solution and possible extensions. 1

The data delivery architectures in most enterprises are complex and under documented. Conceptual business models and business analytics applications are created to provide a simplified, and easy to navigate view of enterprise data for analysts. But the construction of such interfaces is tedious, manually intensive to build, requiring specialized technical expertise, and it is especially difficult to map exactly where data came from in the organization. In this paper we investigate how two aspects (lineage and requests for data i.e. semantics and new reports) can be addressed by tying metadata documentation to a systematic data delivery architecture in order to support business analytics applications. We propose a tool framework that includes a metadata repository for each step in the data delivery architecture, a web based interface to access and manage that repository and mapping tools that capture data lineage to support step by step automation of data delivery.

The data delivery architectures in most enterprises are complex and under documented. Conceptual business models and business analytics applications are created to provide a simplified, and easy to navigate view of enterprise data for analysts. But the construction of such interfaces is tedious, manually intensive to build, requiring specialized technical expertise, and it is especially difficult to map exactly where data came from in the organization. In this paper we investigate how two aspects (lineage and requests for data i.e. semantics and new reports) can be addressed by tying metadata documentation to a systematic data delivery architecture in order to support business analytics applications. We propose a tool framework that includes a metadata repository for each step in the data delivery architecture, a web based interface to access and manage that repository and mapping tools that capture data lineage to support step by step automation of data delivery.

The data delivery architectures in most enterprises are complex and under documented. Conceptual business models and business analytics applications are created to provide a simplified, and easy to navigate view of enterprise data for analysts. But the construction of such interfaces is tedious, manually intensive to build, requiring specialized technical expertise, and it is especially difficult to map exactly where data came from in the organization. In this paper we investigate how two aspects (lineage and requests for data i.e. semantics and new reports) can be addressed by tying metadata documentation to a systematic data delivery architecture in order to support business analytics applications. We propose a tool framework that includes a metadata repository for each step in the data delivery architecture, a web based interface to access and manage that repository and mapping tools that capture data lineage to support step by step automation of data delivery.

The data delivery architectures in most enterprises are complex and under documented. Conceptual business models and business analytics applications are created to provide a simplified, and easy to navigate view of enterprise data for analysts. But the construction of such interfaces is tedious, manually intensive to build, requiring specialized technical expertise, and it is especially difficult to map exactly where data came from in the organization. In this paper we investigate how two aspects (lineage and requests for data i.e. semantics and new reports) can be addressed by tying metadata documentation to a systematic data delivery architecture in order to support business analytics applications. We propose a tool framework that includes a metadata repository for each step in the data delivery architecture, a web based interface to access and manage that repository and mapping tools that capture data lineage to support step by step automation of data delivery.

Manta Flow is a tool for analyzing data flow in enterprise environment. It features Java scanner, a module using static analysis to determine the flows through Java applications. To analyze an application using some framework, the scanner requires a dedicated plugin. Although Java scanner provides plugins for several frameworks, to be usable for real applications, it is essential that the scanner supports as many frameworks as possible, which requires implementation of new plugins. Application using Apache Spark, a framework for cluster computing, are increasingly popular. Therefore we designed and implemented Java scanner plugin that allows the scanner to analyze Spark applications. As Spark focuses on data processing, this presented several challenges that were not encountered in other frameworks. In particular it was necessary to resolve the data schema in various scenarios and track the schema changes throughout any operations invoked on the data. Of the multiple APIs Spark provides for data processing, we focused on Spark SQL module, notably on Dataset, omitting the legacy RDD. We also implemented support for data access, covering JDBC and chosen file formats. The implementation has been thoroughly tested and is proven to work correctly as a part of Manta Flow, which features the plugin in...

Large information systems are typically implemented using frameworks and libraries. An important property of such systems is data lineage - the flow of data loaded from one system (e.g. database), through the program code, and back to another system. We implemented the Java Resolver tool for data lineage analysis of Java programs based on the Symbolic analysis library for computing data lineage of simple Java applications. The library supports only JDBC and I/O APIs to identify the sources and sinks of data flow. We proposed some archi- tecture changes to the library to make easily extensible by plugins that can add support for new data processing frameworks. We implemented such plugins for few frameworks with different approach for accessing the data, including Spring JDBC, MyBatis and Kafka. Our tests show that this approach works and can be usable in practice. 1

Data lineage forms an important aspect of today's enterprise environment. MANTA Flow is a data lineage analysis platform that already has basic support for analysis of Java programs, provided by one of its components called Bytecode Scanner. Neverthe- less, there are very few applications in today's enterprise environment that do not use dependency injection at least in a very limited way. Therefore, we present an extension of Bytecode Scanner in the MANTA Flow platform to support data lineage analysis of dependency injection frameworks as well. The extension is able to process even complex definitions of standard dependency injection containers. Since the dependency injec- tion influences a selection of method call targets, we also provide a description of call graph structure and its modification to support dependency injection. Last, we use this infrastructure to design and implement a plugin into Bytecode Scanner for the Spring Framework, a popular dependency injection framework targeting Java Platform. The plugin has been successfully tested on a small but realistic software system that can read data from a file, transform them, and write them into a database. 1