The people, process, and technology required to deliver data & analytics solutions faster, better, cheaper.
Report
Best Practices in DataOps: How to Create Robust, Automated Data Pipelines
June 17, 2019 - DataOps is an emerging set of practices, processes, and technologies for building and automating data pipelines to meet business needs quickly. It speeds cycle times while reducing defects.
Blog
Diving into DataOps: The Underbelly of Modern Data Pipelines
Report
DataOps: Industrializing Data and Analytics
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The Ultimate Guide to DataOps: Product Evaluation and Selection Criteria
Blog
DataOps: More Than DevOps for Data Pipelines
Shop Talk Webinar - DataOps: How to Get Started
DataOps uses automated testing, orchestration, collaborative development, containerization, and monitoring to continuously accelerate output and improve data quality. It builds upon the software engineering principals espoused in DevOps, but in a manner more specifically tailored for data. How do you get started on obtaining these benefits? We explore how in our "shop talk" webinar.
Blog
DataOps for Machine Learning: A Guide for Data Engineers and Data Scientists
Report
Self-Driving Data: A Critical Enabler for DataOps
Podcast
Shakeeb Akhter: DataOps in Action - Implementing Agile and Automation
Report
DataOps Deep Dive: Different Approaches to the DataOps Platform
December 1, 2020 - DataOps is a new practice that is gaining adoption as organizations recognize the importance of transitioning from artisanal to industrial-scale processes for developing and running data pipelines. DataOps enables organizations to evolve from slow, one-off development efforts to a team-based development approach that can build, change, and manage thousands of pipelines with high speed and accuracy.
Blog
Blog
Data Engineering Coming of Age
Report
Trends in DataOps: Bringing Scale and Rigor to Data and Analytics
Blog
Why and How Data Engineers Realize DataOps Benefits with Apache Kafka Streaming
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