A Business and Technologies journey

Some time ago I wrote the first part of this blog . In this second part I will detail how the temporal query engine is actually implemented in MarkLogic. The objective of the engine is to identify period of time during the Tests when multiple conditions are met and especially when a temporal pattern is realised. This blog is quite technical with some advanced concepts which are not that easy to explain, especially by a non native English speaker :) . Don't hesitate to contact me if you want more details. The data loaded in MarkLogic Context sources First we have data sources which provide metadata about the context of the Tests. Depending on the use case it could be: Weather data Test conditions Crew/expert reports: documents/unstructured content Configuration of the product during the test Etc. In MarkLogic these sources can be structured or unstructured data and could eventually contain geospatial shapes. Data is stored in documents (xml or json). The time s...

The challenge As part of the GDPR, a client can ask for its records to be deleted. In a typical large organization, client records can be spread across multiple different systems from CRM, to eCommerce, to Campaign management solutions. Finding this records can quickly become a nightmare. In this article we will describe how MarkLogic and the datahub pattern can help finding the customer records across these systems. The Operational Data Hub pattern The Operational Data Hub (ODH) pattern, powered by MarkLogic is designed to ease the integration of data from siloes, link and enrich them and create actionable 360 view across all demands. The Data Hub consolidates data from siloes so if we enrich the process with lineage, we can then track all data from the 360 view to their source records.  Data dictionaries meet the triple store Usually organizations maintain data dictionaries for their databases. It's quite easy to get a CSV or an Excel file representing th...