Designing and Implementing Modern Data Architecture on Azure Cloud.
Designing a modern, cloud data architecture is a critical component of the digital transformation journey of any enterprise. In this post, we cover some of the Azure Data Services used to deliver a solution designed to meet the customer's current and evolving future needs.
Data Analytics Solution - Tokenization with External Tokenization System , Synapse and PowerBI
One of the key use cases that most customers that are on their migration journey to Azure is data tokenization and masking. In this blog we will look at the use case of a centralized data control to ensure data privacy and compliance regulations at rest and transfer - how to tokenize data with a 3rd party tool - Protegrity before storing it in Synapse and an approach to access the tokenized data in PowerBI .Data Validation at Scale with Azure Synapse
In the world of Artificial Intelligence and Machine Learning, data quality is paramount in ensuring our models and algorithms perform correctly. By leveraging the power of Spark on Azure Synapse, we can perform detailed data validation at a tremendous scale for your data science workloads.
Validate data using an Azure Function and Great Expectations
Great Expectations is a great tool for validating the incoming data into your data platform, and what better way to run it then having it triggered by new files by using Azure Function! In this blog post, I will discuss what the main concepts of Great Expectations are, how to get it running in a Azure Function and how to embed that in a larger event-driven architecture. Finally, a link to the code on Github is given so you can get started yourself!Distributed ML Training for Lane Detection, powered by NVIDIA and Azure NetApp Files
Microsoft, NetApp and Run:ai have partnered in the creation of this article to demonstrate the unique capabilities of the Azure NetApp Files together with the Run:ai platform for simplifying orchestration of AI workloads. This article provides a reference architecture for streamlining the process of both data pipelines and workload orchestration for Distributed Machine Learning Training for Lane Detection, by ensuring the use of the full potential of NVIDIA GPUs.High-performance storage for AI Model Training tasks using Azure ML studio with Azure NetApp Files
This article describes how to provide enterprise grade high performance persistent storage with data protection capability for AI Model training tasks using studio compute instances with Azure NetApp Files (ANF).
