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Microsoft Azure (for Analytics)
With Microsoft Azure, Microsoft is one of the biggest cloud vendors. They have a solid offering, covering a multitude of different products and services. Aside from typical cloud solutions for business, such as Office365, Dynamics CRM Online, SharePoint Online and so on, Microsoft also has a product portfolio for business intelligence, data warehousing and data science in the cloud.
Important to understand when talking about the cloud, is that there are many different cloud types:
- SaaS - Software as a Service: You are paying a fee to use the software. You don’t become owner of the software, nor will you need to do maintenance and upgrades. Fees are most likely calculated on the number of users that have access, or based on the consumption. A good example is Azure Machine Learning: the only thing users need is a browser to access the service. All development and configuration is done through the browser. Power BI falls into this category as well.
- PaaS - Platform as a Service: It’s not specific software you are subscribing to, but a platform or a set of software or services you are subscribing. You pay based on usage and the number of services you are using. An example is the SQL Azure Database. This is a database hosted in cloud. You can connect using your local Management Studio (SSMS). There are differences between a SQL Server instance hosted on premises and a SQL Azure Database in the cloud, but there are less and less every year. Backups and high availability are done for you, you only need to focus on the database development.
- IaaS - Infrastructure as a Service: the customer is renting infrastructure. Most common example of IaaS is virtual machines that are running in the cloud. You pay for the uptime of the Virtual Machine without being bothered with the hardware behind it. But do remember that you still need licensing and maintenance of the software running on the virtual machines. Basically, this is the same thing as running virtual machines in your own data center, except that the data center is now in the cloud. You can do the same things in a virtual machine in the cloud as you can on premises, such as running Integration Services packages, hosting Analysis Services cubes or rendering Reporting Services reports. One of the biggest advantages is that you can easily scale up or down the performance of the machines depending on the demand.
element61 is actively investigating and using the opportunities the Microsoft Azure platform is offering for Business Intelligence in the Cloud. Some key products that are offered by the Azure Platform:
- The Cortana Intelligence suite. A cloud-based ecosystem for building and deploying elastic, scalable modern data warehousing and advanced analytics solutions. It encompasses a set of data-oriented PaaS offerings, such as:
- Azure Event Hub
- Azure Data Catalog
- Azure Data Factory
- Azure Data Lake
- Azure SQL Data Warehouse
- Azure Stream Analytics
- Azure HDInsight
- Azure Machine Learning
- Power BI
For more information, check out Microsoft Cortana Intelligence Suite.
- Azure SQL Data Warehouse. A cloud-based, scale-out database capable of processing massive volumes of data, both relational and non-relational. Built on a massively parallel processing (MPP) architecture, SQL Data Warehouse can handle any enterprise workload.
- Azure Analysis Services. The SaaS version of the on premises Analysis Services models. It’s one of the newest additions to the cloud stack. At the time of writing, this offering is in preview and only supports SSAS Tabular models.
element61 has already developed a best-practice approach for using the services in a Performance Management & Business Intelligence in the Cloud context.