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In this section, let's understand the different job/position roles and skills required for each role so that DevOps professionals can adapt to the grand plan.

In the article, I incorporated a lot of my own ideas. Of course, more than half of the landing plans for our company's DevOps have been implemented, and I'm still working on the blueprint for the rest.

As a method, DevOps transfers traditional team collaboration to development and operation and maintenance teams, and automates by using stackable virtual expansion tools.

DevOps has developed into one of the most popular software development methods. EDR is a network security technology that meets the needs of continuous monitoring and response to various advanced threats.

Is the so-called DevOps platform equivalent to an automated operation and maintenance platform or a continuous delivery platform? What skills are required in the job description of DevOps engineer?

DevOps construction seems to have become a corporate consensus. Java is a strongly typed language that allows extensions to check for potential type mismatch problems at compile time.

The scope of test development or development testing is very large, Today I want to discuss how to be a more qualified test development engineer on the DevOps pipeline.

DevOps is a mixed role, requiring skills and experience in software development and system management and operations. Software developers are programmers who develop software.

Data set provides a unified view of data stored in multiple data sources, and extraction, transformation and loading (ETL) technology is an early attempt of data integration.

GNU HTTP tunnel is an open source HTTP tunnel project, including tunnel server (HTS command) and tunnel client (HTC command), with (x) Nix and windows versions.

Through the tunnel, IPv6 packets can cross the IPv4 network to realize the interworking of isolated IPv6 network. Before IPv6 is fully implemented, there are always some

Data quality is the basis of validity and accuracy of data analysis and data mining conclusions, and also the premise of data-driven decision-making.

Data monitoring is relatively a background system, not an external business system, and its general importance may be challenged. However, it is still worth doing.

Data quality is the basis of validity and accuracy of data analysis and data mining conclusions, and also the premise of data-driven decision-making.

At the beginning of the establishment of big data, it is necessary to consider the monitoring of data quality, and identify fake data in time through multi-dimensional and multi angle data detection rules.

Data quality monitoring is one of the most easily sacrificed and ignored functions in the rapid development of business, but it is really crucial.

In order to develop a set of real-time monitoring data quality, add another layer of centralized monitoring at the data push interface.

Data monitoring is relatively a background system, not an external business system, and its general importance may be challenged. However, it is still worth doing.

Data quality is the basis of data application. Its evaluation criteria mainly include four aspects, integrity, consistency, accuracy and timeliness.

For many organizations, how to successfully build a cloud data warehouse for the first time is a difficult problem, so we need to know some knowledge and skills.