Chicken & waffles for data scientists, hello DataOps

It had to happen.

DevOps consolidated the developer and operations functions into one ‘workplace culture’ and so came together the previously separately plated chicken & waffles of the software engineering world into a new (apparently harmonious) union.

Some even talk of DevSecOps, with security as the special sauce… such has been the apparent industry acceptance of the DevOps term.

But this is the age of data analytics, big data, data governance, data compliance, data deduplication, log data, machine data (Ed – we get the point) and so on… shouldn’t data itself get a departmentally unifying portmanteau all of its own?

The answer is yes, it should… and when you add operational excellence to data you (obviously) get DataOps.

DataOps is here

Data convergence player MapR has come forward with its DataOps Governance Framework.

The ‘framework’ (well, company initiative with custom-tuned software services relating to the core MapR stack) will integrate the MapR Converged Data Platform with selected partner technologies.

It aims is to help companies meet compliance requirements for data governance beyond traditional big data environments such as Apache Hadoop.

According to a MapR press statement, this technology is tailored for organisational data transformation and data lineage requirements — further, it focuses on data quality and integrity to help meet obligatory compliance, including data privacy requirements.  

“By providing a comprehensive open approach to data governance, organisations can operate a DataOps-first methodology where teams of data scientists, developers and other data-focused roles can train machine learning models and deploy them to production. DataOps development environments foster agile, cross-functional collaboration and fast time-to-value that benefits the entire enterprise,” said Mitesh Shah, senior technologist at MapR.  

Shah finishes by claiming that the MapR DataOps Governance Framework lets organisations extend required data lineage and other governance needs across clouds, on-premises and to the edge with all data in any application – even those considered outside of the big data realm.

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