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- Best practices for Azure data architecture include1234:
- Use the Azure Well-Architected Framework, which is divided into five pillars of architectural best practices: cost management, operational excellence, performance efficiency, reliability, and security.
- Store and process data in volumes too large for a traditional database.
- Transform unstructured data for analysis and reporting.
- Capture, process, and analyze unbounded streams of data in real time, or with low latency.
- Orchestrate data ingestion using a data workflow or pipeline solution such as those supported by Azure Data Factory or Oozie.
- Process data in place using a distributed data store, a big data approach that supports larger volumes of data and a greater variety of formats.
- Scrub sensitive data early as part of the ingestion workflow to avoid storing it in your data lake.
- Enable secure integration between products/tools/services to allow for efficient running, scalability, and support for a variety of data formats.
- Enable insights by simplifying the ability to build analytics dashboards and operational reports.
- Unify data, analytics, and ML workloads.
Learn more:✕This summary was generated using AI based on multiple online sources. To view the original source information, use the "Learn more" links.The Azure Well-Architected Framework is divided into five pillars of architectural best practices: cost management, operational excellence, performance efficiency, reliability, and security.azure.microsoft.com/en-us/blog/introducing-the-mic…Store and process data in volumes too large for a traditional database. Transform unstructured data for analysis and reporting. Capture, process, and analyze unbounded streams of data in real time, or with low latency. Use Azure Machine Learning or Azure Cognitive Services.learn.microsoft.com/en-us/azure/architecture/guide…At the platform level, the following big data best practices contribute to trustworthy analytics on Azure:
- Orchestrate data ingestion using a data workflow or pipeline solution such as those supported by Azure Data Factory or Oozie.
learn.microsoft.com/en-us/azure/architecture/soluti…The architecture should enable secure integration between products/tools/services to allow for efficient running, scalability, and support for a variety of data formats. It should enable insights by simplifying the ability to build analytics dashboards and operational reports. It should unify data, analytics, and ML workloads.techcommunity.microsoft.com/t5/azure-architectur… - People also ask
Big data architectures - Azure Architecture Center | Microsoft Learn
- When working with very large data sets, it can take a long time to run the sort of queries that clients need. These queries can't be performed in real time, and often r…
- One drawback to this approach is that it introduces latency — if processing takes a few hours, a query may return results that are several hours old. I… See more
- A drawback to the lambda architecture is its complexity. Processing logic appear…
- The kappa architecture was proposed by Jay Kreps as an alternative to the lambd…
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- This article is maintained by Microsoft. It was originally written by the following contributors.
- Principal author: See more
- From a practical viewpoint, Internet of Things (IoT) represents any device that is connected to the Internet. This includes …
- Event-driven architectures are central to IoT solutions. The following diagram shows a possible logi… See more
- See the following relevant Azure services:
- •Azure IoT Hub
- •Azure Event Hubs
- •Azure Stream Analytics See more
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