Generic Data Storage Component for .Net

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This component allows you to save data objects by its XML-Serialization easily in generic data storages (e.g. XML-File, SQL Server database ...)



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Simple data storage and serialization system for .net

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In today’s highly competitive and connected environment, modern businesses can no longer survive with generic, static online content. Furthermore, marketing strategies using traditional tools are often expensive, hard to implement, and do not produce the desired return on investment. These systems often fail to take full advantage of the data collected to create a more personalized experience for the user. Surfacing offers that are customized for the user has become essential to build customer loyalty and remain profitable. On a retail website, customers desire intelligent systems which provide offers and content based on their unique interests and preferences. Today’s digital marketing teams can build this intelligence using the data generated from all types of user interactions. By analyzing massive amounts of data, marketers have the unique opportunity to deliver highly relevant and personalized offers to each user. However, building a reliable and scalable big data infrastructure, and developing sophisticated machine learning models that personalize to each user is not trivial.Cortana Intelligence provides advanced analytics tools through Microsoft Azure — data ingestion, data storage, data processing and advanced analytics components — all of the essential elements for building an demand forecasting for energy solution. This solution combines several Azure services to provide powerful advantages. Event Hubs collects real-time consumption data. Stream Analytics aggregates the streaming data and updates the data used in making personalized offers to the customer. Azure DocumentDB stores the customer, product and offer information. Azure Storage is used to manage the queues that simulate user interaction. Azure Functions are used as a coordinator for the user simulation and as the central portion of the solution for generating personalized offers. Azure Machine Learning implements and executes the product recommendations and when no user history is available Azure Redis Cache is used to provide pre-computed product recommendations for the customer. PowerBI visualizes the activity of the system with the data from DocumentDB.

PySpark-Predictive-Maintenance - Predictive Maintenance using Pyspark

Predictive maintenance is one of the most common machine learning use cases and with the latest advancements in information technology, the volume of stored data is growing faster in this domain than ever before which makes it necessary to leverage big data analytic capabilities to efficiently transform large amounts of data into business intelligence. Microsoft has published a series of learning materials including blogs, solution templates, modeling guides and sample tutorials in the domain of predictive maintenance. In this tutorial, we extended those materials by providing a detailed step-by-step process of using Spark Python API PySpark to demonstrate how to approach predictive maintenance for big data scenarios. The tutorial covers typical data science steps such as data ingestion, cleansing, feature engineering and model development.The input data is simulated to reflect features that are generic for most of the predictive maintenance scenarios. To enable the tutorial to be completed very quickly, the data was simulated to be around 1.3 GB but the same PySpark framework can be easily applied to a much larger data set. The data is hosted on a publicly accessible Azure Blob Storage container and can be downloaded from here. In this tutorial, we import the data directly from the blob storage.

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storage - An improvement over flat text files for simple serialization of data.

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