Assignment 5

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McMaster University *

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2D03

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Information Systems

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Dec 6, 2023

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Assignment 5 Assignment on Azure Cloud Platform Due by Dec 3, 2023 1. Note: Part B of this assignment can be done in groups of two students or individuals. Both students need to submit the assignment for both parts and provide both names, email and student ID at the top of the assignment. Submit a compressed archive (zip, tar, etc.) of your code, along with the input and output files and screenshots (output/input commands with results). Please include your Azure Machine Learning Notebook with markdown. Also, include a pdf document with answers to the questions below. Please submit all screenshots showing deployed resources in your Azure portal provide an explanation for each step, also show final output screenshots. Contact your TA for any questions related to this assignment or post clarification questions to the Piazza platform. PART A: 1. [Marks: 5] Explain below the 5 components shown in orange boxes. Explain which Azure components you will use in this big data architecture and why. 2. [Marks: 5] Explain how Stream Analytics works in Azure. Raw Data Unstructured Data Structured Data Ingest Data Data Store Prepare and transform data Model and serve data Azure Databricks Azure Data Factory Azure Synapse Analytics Azure Cosmos DB Azure Data Lake
3. [Marks: 10] Deploy all the resources in Azure Portal. Implement a Stream Analytics job by using the Azure portal. See this for reference - https://learn.microsoft.com/en- us/azure/stream-analytics/stream-analytics-quick-create-portal For query use below: SELECT * INTO BlobOutput FROM IoTHubInput HAVING Temperature > 26 See the below screenshot and show the top 50 results for your output. Part B: Data Input: Claim a dataset from Piazza - link. If the dataset is too large, you can take a subset of the data as well. No two groups can have the same dataset. You need to solve a meaningful problem using this dataset. Some problems to consider: 1. Fraud Detection System 2. Customer Churn Rate Prediction 3. Segmentation using Clustering 4. Recommendations with your Dataset 5. Sales Forecasting 6. Stock Price Predictions 7. Human Activity Recognition with Smartphones 8. Wine Quality Predictions 9. Breast Cancer Prediction 10. Sorting of Specific Tweets on Twitter etc.
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