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It facilitates collaboration between a data science team and IT professionals, and thus combines skills, techniques, and tools used in dataengineering, machine learning, and DevOps — a predecessor of MLOps in the world of software development. MLOps lies at the confluence of ML, dataengineering, and DevOps.
Systems engineering and operations. GoogleCloud Platform – Professional Cloud Developer Crash Course , June 6-7. Getting Started with GoogleCloud Platform , June 24. AWS Certified Big Data - Specialty Crash Course , June 26-27. GoogleCloud Platform Security Fundamentals , July 9.
Intended for individuals who have a DevOps engineer role and two or more years of experience operating, provisioning and managing AWS environments. They are able to implement and manage continuousdelivery systems and methodologies on AWS. Azure DataEngineer Associate. Professional Cloud Architect .
Systems engineering and operations. GoogleCloud Platform – Professional Cloud Developer Crash Course , June 6-7. Getting Started with GoogleCloud Platform , June 24. AWS Certified Big Data - Specialty Crash Course , June 26-27. GoogleCloud Platform Security Fundamentals , July 9.
Data science and data tools. Practical Linux Command Line for DataEngineers and Analysts , March 13. Data Modelling with Qlik Sense , March 19-20. Foundational Data Science with R , March 26-27. What You Need to Know About Data Science , April 1. Introduction to GoogleCloud Platform , April 3-4.
Data science and data tools. Practical Linux Command Line for DataEngineers and Analysts , May 20. First Steps in Data Analysis , May 20. Data Analysis Paradigms in the Tidyverse , May 30. Data Visualization with Matplotlib and Seaborn , June 4. ContinuousDelivery with Jenkins and Docker , June 24.
This basic principle corresponds to that of agile software development or approaches such as DevOps, Domain-Driven Design, and Microservices: DevOps (development and operations) is a practice that aims at merging development, quality assurance, and operations (deployment and integration) into a single, continuous set of processes.
As 2020 is coming to an end, we created this article listing some of the best posts published this year. This collection was hand-picked by nine InfoQ Editors recommending the greatest posts in their domain. It's a great piece to make sure you don't miss out on some of the InfoQ's best content.
DataData is another very broad category, encompassing everything from traditional business analytics to artificial intelligence. Dataengineering was the dominant topic by far, growing 35% year over year. Dataengineering deals with the problem of storing data at scale and delivering that data to applications.
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