Improving production deployment with Azure tools
1 min read
Azure Databricks is a robust solution to modern analytics, data engineering, and machine learning platform requirements. Based on Apache Spark, Azure Databricks is an analytics and artificial intelligence (AI) service that provides a workspace for data-focused workers to collaborate on projects. It connects with a broad range of tools including scripting languages and a diverse range of Azure services. One of these tools is Azure Data Factory; when combined, these two Azure tools enable data scientists and engineers to improve deployment of production.
To satisfy cloud conversion needs from customers, Azure technologies are a fantastic option. Local SQL databases can transfer data using a Databricks notebook, working on a set schedule to create a Data Factory. This Data Factory can then populate a cloud SQL database, pass through Databricks, and output to Azure storage, a process repeatable as many times as necessary before data is submitted to a PowerBI interface. So how do Data Factory and Databricks improve the process?
Automation of subprocesses is key. With Databricks notebooks, data can be scheduled to run on a nightly basis, and when paired with embedded machine learning tools, predictions can be stored back into Azure. In addition, the Data Factory can manage production pipeline scheduling, workflows, and monitoring.
For more information, check out Microsoft's Azure blog here.