Data lake architecture how to#
This walkthrough also contains a section that shows how to build and deploy a predictive model using Python with Azure Machine Learning sStudio. Hive tables are created for the data in an associated HDInsight cluster to facilitate the building and deployment of a binary classification model in Azure Machine Learning studio. It also shows how to run a U-SQL scripted job from the Azure portal. The process includes ingesting, exploring, and sampling the data. The U-SQL scripts are described here and provided in a separate file.
This walkthrough recommends using Visual Studio to edit U-SQL scripts to process the dataset. Then it outlines the data processing steps using U-SQL and concludes by showing how to use Python and Hive with Azure Machine Learning studio (classic) to build and deploy the predictive models.
Data lake architecture install#
This walkthrough begins by describing how to install the prerequisites and resources that you need to complete the data science process tasks. This combination gives you a complete cloud big data and advanced analytics platform. It works with Azure Synapse Analytics, Power BI, and Data Factory. To learn more about the design philosophy behind U-SQL, see this Visual Studio blog post.ĭata Lake Analytics is also a key part of Cortana Analytics Suite. You can also insert custom logic and user-defined functions (UDFs), and it includes extensibility to enable fine-grained control over how to execute at scale. It enables you to process unstructured data by applying schema on read. U-SQL then provides a scalable distributed query capability. Azure Data Lake Analytics includes U-SQL, a language that blends the declarative nature of SQL with the expressive power of C#. You pay on a per-job basis, only when data is actually being processed. The Microsoft Azure Data Lake has all the capabilities required to make it easy for data scientists to store data of any size, shape and speed, and to conduct data processing, advanced analytics, and machine learning modeling with high scalability in a cost-effective way. These technologies are used in this walkthrough. Then it shows you how to deploy a web service that publishes the model. It walks you through the steps of the Team Data Science Process, end-to-end, from data acquisition to model training. The sample shows you how to predict whether or not a tip is paid by a fare. This walkthrough shows how to use Azure Data Lake to do data exploration and binary classification tasks on a sample of the NYC taxi trip and fare dataset.
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