With no inherent means of adhering to governance and security protocols, data lakes are akin to the Wild West in that they are devoid of order and consistency. Each user manipulates his or her own data at the risk of the reuse of that data for others.
Unstructured data is all around us: in news stories, web pages, journal articles, social media posts, patents, research reports, presentations, and a variety of other sources. These items are unstructured in that they don’t start out with a predefined, explicit schema or structure. Historically, these documents have been read by humans looking to find information relevant to their particular tasks or roles. In today's deluge, however, the need for scalable reading, repeatability, traceability, and speed has driven the advent of text analytics platforms.
Data lakes are no longer anomalies. Consolidating all of an organization’s data—unstructured, semi-structured, and structured—into a single repository for integration, access, and analytics purposes is rapidly emerging as the preferred way to manage big data initiatives.
In large Java projects that use OSGi, there can arise a well-defined problem known as a "uses constraint violation". This problem arises when the classes available to a certain bundle - its class space - contains two versions of the same package. An OSGi system's bundles, packages, and dependencies can be modeled as a graph inside of Anzo Smart Data Platform (Anzo SDP), thus providing a framework for visualization and complex analysis. This article provides multiple methods for using this framework to determine the class space of a bundle. It also provides a SPARQL query and technique for determining the causes and possible solutions of a uses constraint violation, and discusses a concrete example.