Microservices PREDICTIVE ANALYTICS REPORT

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ANALYSIS

What You Need to Know

  • Breakouts in the Microservices predictive analytics are Serverless computing, Continuous deployment, Cloud Foundry. Seriously consider these technologies to gain a strategic advantage.
  • The technologies who are at the peak of their interest are Microservices, Cloud Foundry, DevOps.
  • By far most employment needs are found in the Automation, Salesforce, DevOps technologies.
  • These 3 fields have the most active practitioners who have the specific skill set or experience: Automation, Salesforce, Computer network.
  • Salesforce, Spotify, Netflix lead in searches for information online.
  • These three technologies are receiving the highest investments to gain clients: SOA Software, Cloud Foundry, Software deployment.
  • These three technologies have the most active advertisers: Software testing, Software deployment, Continuous delivery.
  • In patents, these three technologies have the most coverage Automation, Computer network, Communications protocol.
  • The most publications are available for Unix philosophy, Uber (company), Spotify.
  • Instruction and courseware availability is highest in these technologies: SoundCloud, Spotify, Netflix.

The Microservices report evaluates technologies and applications in terms of their business impact, adoption rate and maturity level to help users decide where and when to invest.

The Predictive Analytics Scores below – ordered on Forecasted Future Needs and Demand from High to Low – shows you Microservices’s Predictive Analysis. The link takes you to a corresponding product in The Art of Service’s store to get started.

The Art of Service’s predictive model results enable businesses to discover and apply the most profitable technologies and applications, attracting the most profitable customers, and therefore helping maximize value from their investments. The Predictive Analytics algorithm evaluates and scores technologies and applications.

The platform monitors over ten thousand technologies and applications for months, looking for interest swings in a topic, concept, technology or application, not just a count of mentions. It then makes forecasts about the velocity of the interest over time, with peaks representing it breaking into the mainstream. Data sources include trend data, employment data, employee skills data, and signals like advertising spent, advertisers, search-counts, Instruction and courseware available activity, patents, and books published.

Predictive Analytics Scores:

002730 – Serverless computing
001837 – Continuous deployment
001547 – Cloud Foundry
000841 – DevOps
000821 – Microservices
000697 – Apprenda
000644 – Continuous delivery
000445 – Docker (software)
000441 – Uber (company)
000378 – Microsoft Azure
000241 – Netflix
000206 – Riot Games
000200 – Load balancing (computing)
000191 – Salesforce
000172 – Spotify
000117 – Bluemix
000086 – Network latency
000079 – Interactive Intelligence
000066 – Automation
000048 – Monolithic application
000046 – Fault tolerance
000041 – Software deployment
000040 – Software testing
000026 – Process (computing)
000021 – SOA Software
000017 – Representational state transfer
000016 – SoundCloud
000015 – Anti-pattern
000008 – HP Helion
000007 – Computer network
000000 – Unix philosophy
000000 – Service-oriented architecture
000000 – Jelastic
000000 – Fallacies of distributed computing
000000 – Digital object identifier
000000 – Conway’s law
000000 – Communications protocol
000000 – C Sharp (programming language)
000000 – Antifragility


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