Continuous analytics is a data science process that abandons ETLs and complex batch data pipelines in favor of cloud-native and microservices paradigms. Continuous data processing enables realtime interactions and immediate insights with fewer resources.
Analytics is the application of mathematics and statistics to big data. Data scientists write analytics programs to look for solutions to business problems.
The continuous approach runs multiple stateless engines which concurrently enriches, aggregate, infer and act on the data. Data scientists, dashboards and client apps all access the same raw or real-time data derivatives with proper identity-based security, data masking and versioning in real-time.
Traditionally data scientists have not been part of IT development teams, like regular Java programmers. This is because of their skills in the field of IT, ie, math, statistics, and data science. It is logical to conclude that their approach to writing software does not require the same efficiency as the traditional programming team. In particular, traditional programming has adopted the Continuous Delivery approach to writing the code and the agile methodology. That releases software in a continuous circle, called iterations.
Continuous analytics then is the extension of the continuous delivery software development model to the big data analytics development team. The goal of the continuous analytics practitioner in the agile development model of automated systems and automated systems.
To make this work in the process of being able to write the code in the same code, it is important to know that software can be used to build the software. It also means the configuration of the big data cluster (sets of virtual machines) in some kind of repository as well. That facilitates sending out analytics in the same way as the continuous integration process.  
- Jump up^ “Continuous Analytics Defined” . Southern Pacific Review . Southern Pacific Review . Retrieved 17 May 2016 .
- Jump up^ Pushkarev, Stepan. “Tear down the Wall between Data Science and DevOps” . LinkedIN . LinkedIN . Retrieved 17 May 2016 .