Automation
A good DataOps platform should automate data delivery from multiple sources at the same time, supporting both structured and unstructured data across hybrid and cloud environments.
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DataOps is analogous to DevOps for data teams. It’s not quite as simple as that, though, because data teams and their platforms have unique challenges that software teams don’t have. But the ideas and motivations are still the same: apply software development best practices to data platforms to produce better data products and achieve better outcomes.
DataOps needs to solve some unique challenges: data testing, data quality monitoring, and backfilling—and that’s only the beginning. Some of the core tenants of DevOps (git repositories, version control, code reviews, isolated environments, etc.) for a data platform go a long way toward getting there.
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DataOps also enables productive collaboration, both within data teams and with other departments, and it creates a place to collaborate on projects and automate deployments.
Collaboration
Organizations can have enormous volumes of data sitting in a data lake. For business intelligence or reporting, analytics engineers depend on the data from a data lake to prepare a filtered and aggregated table.
Reduced Development Time
DataOps focuses on process-oriented methodologies and automation to improve workforce productivity. By introducing intelligent testing and observation mechanisms into the analytic pipeline, teams can stay focused on strategic tasks versus poring over spreadsheets looking for anomalies.
Improved Data Quality and Reliability
The creation of automated, repetitive processes, along with automated code testing, data quality assertions, and controlled deployments reduces the likelihood of incorrect or unexpected data downstream.
Improved Analytics
Automated reception, processing, and aggregated analytics of incoming data streams, combined with error elimination, let you instantly understand customer behavior patterns, market shifts, and price fluctuations.
