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Moving Big data in and around Azure using Azure Data Factory.

There are numerous data storage options available on Azure, each one designed and developed for different modern data storage scenarios. These storage options could be in the form of database, data warehouse, data caches and data lakes. Usage of these depends on the application and the scale that they serve. Within databases, some applications might need relational database, some might need NOSQL, or a key-value storage, or in-memory database (for caching), or blob storage (for media and large files). Another criteria to keep in mind when selecting a database for your application is the required read-write throughput and latency. Azure has a wide array of fully-managed database services which frees up the development teams valuable time in managing, scaling and configuring these databases.

Whatever database you choose, you should also keep in mind how easy or difficult it is to move the data in and out of that database. You might have a situation in future where you need to move to a new database solution because of reasons like change in application architecture, scale, performance, or even cost. Microsoft Azure has a very powerful ETL tool called Azure Data Factory to easily move data in and around Azure at scale. It has over 80 native connectors which can serve both as source and sink.  In this blog, I would like to highlight a few features and concepts of Azure Data Factory which will serve as a quick start guide for anyone looking to do data movement and transformation on Azure.

Siddharth Bhola by Siddharth Bhola

Intro to Azure Resource Management Templates

Azure resource manager allows the rapid deployment of multiple Azure resources. Unfortunately even relatively simple deployments turn into JSON files hundreds of lines long.  As a newbie to the process, I would like to provide some tips and resources that I found helpful during my quest to build a LAMP Server with load balancers. 

Leo Ling by Leo Ling