Data Operations
Jun 18, 2019
Written by Todd Goldman
EDO2 refers to the systems and processes that enable businesses to organize and manage data from disparate sources and process the data for delivery to analytic applications.


Data Operations
Apr 07, 2019
Written by Todd Goldman
There is no Gartner Magic Quadrant for DataOps but inquiring minds want to know the 5 characteristics that define a good DataOps solution.


Data Operations
Nov 14, 2018
Written by Todd Goldman
“Agile” was officially used to describe the iterative method of engineering by a group of software developers in their “Agile Manifesto”. Now it is making its way into the data engineering world ...


Data Operations
Aug 06, 2018
Written by Todd Goldman
DataOps is an automated, process-oriented methodology that plays off of the DevOps idea but used by analytic and data teams to reduce the delivery time of data analytics projects.


Data Operations
May 08, 2018
Written by Todd Goldman
The main characteristic of DataOps is to strongly advocate automation and monitoring at all steps of data pipeline construction, from data integration, testing, releasing to deployment and infrastructure ...


Data Operations
Mar 21, 2018
Written by Ramesh Menon
We walk through what you can do to use big data automation to overcome the top 5 technical challenges that block organizations from fully taking advantage of big data.


Thanks for checking out this compilation of articles from the Infoworks blog about key principles, industry insights, and changes happening in the realm of data operations, more commonly known as DataOps.

By uniting all data professionals (e.g. developers, data scientists, and data engineers) DataOps is a unique approach to data analytics. It borrows certain philosophies from agile software development, DevOps, and statistical process controls with the sole purpose of reducing development cycle times, increasing deployment frequency, and vastly improving overall data quality. This new approach to the end-to-end data lifecycle is an automated methodology, placing a strong emphasis on structure and processes.

Those who embrace DataOps are continuously seeking ways to optimize their analytics pipelines with repeatable results. Organizations employ DataOps tools which handle aspects such as data pipeline orchestration, automated testing, production, and quality alerts, deployment automation, development sandbox creation, and data science model deployment.

Correct implementation of DataOps can allow teams to consistently deliver value upon each iteration, release new iterations in rapid succession for greater flexibility, and improve schedule forecasting. In the end, the methodology behind DataOps empowers companies to receive better insights at a much faster pace, giving a much-needed edge against the competition.

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This blog archive is the best place to find articles that cover best practices, unique insights from industry professionals, and all of the aspects surrounding data operations you need to know.

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