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Embedded Analytics

5 Elements of a DevOps-Friendly Embedded Analytics Solution

By Michelle Gardner | July 11, 2019
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When embedded analytics providers say their platform and tools will work with your tech stack and are DevOps-friendly, what do they really mean? Here are five important factors to consider when evaluating embedded analytics solutions with DevOps in mind.

>> Related: Bridging the Gap Between Dev and Ops (Infographic) <<

#1. Security

If the embedded analytics solution is DevOps-friendly, it will continue handling security with the same methods your DevOps team is currently using. You won’t have to recreate or replicate BI security information in two different places. The rising trend of DevSecOps teams is an indicator of how essential security is to DevOps teams everywhere. Consider the following:

  • Authorization controls. Whatever DevOps is currently using—credentials, enterprise services account, or something else—the embedded analytics platform should adapt to it.
  • Standard-based SSO. It’s important for user experience that embedded analytics applications play within a single sign-on (SSO) environment—and important for DevOps that they do it based on standards. Any custom elements divert DevOps resources to figuring out SSO peculiarities and decrease the odds of being able to quickly debug any problems.
  • Defense against known vulnerabilities. Look for an embedded analytics vendor who is already protecting against common security vulnerabilities that any web application will face.
  • Incident visibility. If there is a security incident, does the platform provide detailed event logging? DevOps should be able to quickly find the Who, What, and When of everything going on at the time.

#2. Data

A DevOps-friendly embedded analytics solution will not force you to use a proprietary data store, replicate data, or change your current data schemas. You should be able to store your data in place via relational databases, web services, or your own proprietary solution. Applications incorporating analytics should be able to use data in your existing systems and deliver strong performance across disparate sources.

#3. Environment

A DevOps-friendly embedded analytics solution will work in any environment. That means applications will work on clouds, in containers, and across mixed and changing deployment environments. These may include Windows or Linux; on-premises, cloud, and hybrid architectures; and mobile devices and browsers. It also means applications can be deployed in a container, or parts of them dispersed across multiple containers.

#4. Architecture

If the embedded analytics solution is DevOps-friendly, you should be able to deploy it into your current architecture using standard methods (with minimal architecture-specific steps). For instance, it may be as simple as installing an ASP.NET application on an IIS server or a Java application on an Apache Tomcat server.

#5. Release Cycles

A DevOps-friendly embedded analytics solution won’t drag release cycles. If you’re moving toward continuous integration and continuous delivery (CI/CD), you want to control exactly what gets deployed and when through your build pipelines. Does the embedded analytics platform allow you to deploy smaller size incremental update packages when broader changes aren’t needed?

Sustainable Innovation and Differentiation

Your embedded analytics solution will affect how frequently you release standout software, how competitive you are, and how sustainable your advantage is over time. In summary, look for one that:

  • Utilizes your technology—including security frameworks and tech stack architecture—as it is
  • Leverages your existing processes so you can build and release application updates faster
  • Offers flexible scaling so it grows with your business

To learn more about DevOps-friendly embedded analytics solutions, read our eBook: Are Your Embedded Analytics DevOps Friendly? >

 

About the Author

Michelle Gardner is the Content Marketing Manager at Logi Analytics. She has over a decade of experience writing and editing content, with a specialty in software and technology.

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