The "Serverless" computing is one of the term catching lots of attention. Linked with the advent of microservices and cloud computing, serverless probably represent the smallest unit of pay-for-use consumption model, scale on demand and code to meet a well scoped and limited purpose.
AWS, Azure and Google all have their implementation of serverless computing with support for various languages and runtime.
As a practical application of the serverless computing paradigm, I had the opportunity to automate operations for an Analytics application built on Azure Analysis Services.
As part of building a quick turnaround reporting and analytics platform for multi-national beverage company, the tables in Azure Analysis Services (AAS) tabular model had to be processed as soon as the ingestion step was completed.
The limitation we ran into was that the orchestration engine used in this case was running on Linux and could not launch Microsoft Windows native executables.
So in comes, Azure Functions with HTTP triggers to the rescue, which allow processing of the tabular model - tables and partitions on demand. Additionally automate creating and merging of partitions as the business requirement demanded.
There are other examples of a similar approach being taken, but they use Timer triggers to perform the operations. (Orsolya Gal's Azure Blog, SqlDusty) or have HTTP Triggers with specific example (Ust Oldfield's Blog)
I believe, this Azure Function App code is more comprehensive and flexible combining the approaches above and has been production tested.
It would be quite useful for developers who find themselves in this situation, so I decided to open source the foundational code which can used to build more complex operations.
The project is titled - Azure Function App for Azure Analysis Services Tabular Model Operations.
AWS, Azure and Google all have their implementation of serverless computing with support for various languages and runtime.
As a practical application of the serverless computing paradigm, I had the opportunity to automate operations for an Analytics application built on Azure Analysis Services.
As part of building a quick turnaround reporting and analytics platform for multi-national beverage company, the tables in Azure Analysis Services (AAS) tabular model had to be processed as soon as the ingestion step was completed.
The limitation we ran into was that the orchestration engine used in this case was running on Linux and could not launch Microsoft Windows native executables.
So in comes, Azure Functions with HTTP triggers to the rescue, which allow processing of the tabular model - tables and partitions on demand. Additionally automate creating and merging of partitions as the business requirement demanded.
There are other examples of a similar approach being taken, but they use Timer triggers to perform the operations. (Orsolya Gal's Azure Blog, SqlDusty) or have HTTP Triggers with specific example (Ust Oldfield's Blog)
I believe, this Azure Function App code is more comprehensive and flexible combining the approaches above and has been production tested.
It would be quite useful for developers who find themselves in this situation, so I decided to open source the foundational code which can used to build more complex operations.
The project is titled - Azure Function App for Azure Analysis Services Tabular Model Operations.
Find the source code on GitHub.
Feel free to download, fork and comment. Looking forward to hear back from the community.
1 comment:
Nice Blog Its have something we currently Needed
Create a Blog for how to call the azure function in AZure Data Factories
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