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.
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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