- DataOps - Wikipedia
DataOps is a set of practices, processes and technologies that combines an integrated and process-oriented perspective on data with automation and methods from agile software engineering to improve quality, speed, and collaboration and promote a culture of continuous improvement in the area of data analytics [1]
- What is DataOps? - GeeksforGeeks
DataOps (Data Operation) is an Agile strategy for building and delivering end-to-end data pipeline operations Its major objective is to use big data to generate commercial value
- What is DataOps? - IBM
DataOps is a set of collaborative data management practices to provide maximum value from data by focusing on automating the data management and data analytics process
- Definition of DataOps - Gartner Information Technology Glossary
DataOps is a collaborative data management practice focused on improving the communication, integration and automation of data flows between data managers and data consumers across an organization
- Beyond Analytics: The DataOps Conference - astronomer. io
Learn how modern DataOps is enabling advanced use cases beyond analytics in a half-day of virtual sessions where data leaders share how they leverage orchestration to power AI, ML, and production-grade data products
- What Is DataOps? Definition, Role, and Responsibilities
DataOps, or data operations, is a modern practice in data management at the crossroads of DevOps and data science This practice, critical to digital transformation and the growth of data-driven companies, provides better data lifecycle management to optimize and improve data quality
- Understanding DataOps: Benefits, Processes, Tools and Trends
DataOps, which stands for data operations, is a modern data management practice to streamline and optimize the design, deployment and management of data flows through a data analytics pipeline, between data managers and consumers
- Understanding DataOps - Coursera
DataOps is a data approach that integrates Agile development, statistics, and DevOps principles to automate data pipelines and improve quality With DataOps, you treat data management as a collaborative and iterative process, allowing you to streamline your data workflows with existing data sources and evolve your methodologies as data sources
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