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šŸ¤– GPT

Enterprise Architect

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Write a use case using persona + problem format and receive a customizable list of potential technology solutions. Customize the prompt with your preferred technologies!
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Updated: 1 year ago

Prompt Details

Model
Chat - GPT-4 (gpt-4)
Token size
64 ($0.00190 / call)
Example input
[Use case to solve] As a commission analyst we process 30 differently formatted files to ingest into a common format. [number of solutions]: 3 [technology]: .NET [cloud]: Azure
Example output
As an enterprise architect, I can provide you with three unique software solutions leveraging .NET on the Azure public cloud to automate the process of ingesting and processing 30 differently formatted files into a common format. These solutions aim to streamline the commission analysis process and improve overall efficiency. Here are three potential approaches: Azure Functions with Logic Apps: This solution combines Azure Functions and Logic Apps to create a serverless architecture. You can develop individual Azure Functions to handle specific file formats, parsing and transforming the data into the desired common format. Logic Apps can be used to orchestrate and automate the workflow, triggering the appropriate Azure Functions based on the file format. You can store the transformed data in Azure Blob Storage or a database for further processing. Azure Data Factory with Mapping Data Flows: Azure Data Factory provides a robust data integration service on the Azure platform. You can configure Data Factory pipelines to ingest the files from various sources, transform them using Mapping Data Flows, and store the transformed data in a common format. Mapping Data Flows offer a visual interface to design and execute data transformations, enabling you to handle the different file formats efficiently. This solution leverages the scalability and managed services provided by Azure Data Factory. Azure Databricks with Apache Spark: Azure Databricks, a collaborative Apache Spark-based analytics service, can be utilized to automate the file ingestion and transformation process. You can write Spark jobs using .NET libraries in Databricks to read the different file formats, perform the necessary data transformations, and output the standardized data. Databricks provides a scalable and high-performance environment for processing large volumes of data. Additionally, you can integrate Databricks with Azure Data Lake Storage or other data stores for efficient data handling. These solutions offer flexibility, scalability, and the ability to process various file formats efficiently using .NET on the Azure public cloud. Depending on your specific requirements and the complexity of the file formats, one of these solutions should suit your needs. It's important to consider factors such as data volume, frequency of ingestion, and desired operational costs when selecting the most appropriate solution for your commission analysis process.
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