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During the epidemic, I was idle and passed Alibaba Cloud Computing and Big Data ACA \ ACP certification exam in one breath, and obtained a certificate.

The use of container technology supports the current three major DevOps practices: workflow, timely feedback, and continuous learning.

The source of ideas for DevOps is very broad. The main sources are several aspects: the first aspect is derived from the lean production model; Click to learn more.

The 7 habits of DevOps framework has been proven to successfully help individuals and teams improve their interpersonal skills. These skills are at the core of any cultural transformation.

Under the guidance of kubernetes, container-centric deployment of microservices has become a de facto standard, and has continuously accelerated the implementation of microservice architecture models.

This lesson will tell you how to use a private image warehouse and how to deploy local docker image to kubernetes.

Before you really become a DevOps engineer, you need to understand that DevOps is a big category, and it can be divided into three categories in specific positions.

DevOps has taken center stage in the software industry. Today, I want to share with you some ideas about how to become an excellent DevOps engineer.

Data cleaning is the first step of the entire data analysis process. In this article, I summarize the 7-step process of using Python to do data cleaning.

How to clean the noise in the data? In this article, we will introduce you the methods of data cleaning.

The data cleaning process includes missing data processing, noisy data processing, inconsistent data processing. Here introduces the main processing methods of data cleaning.

Data cleaning-the process of re-examination and verification of data. Python provides a lot of algorithm analysis to further assist us in data analysis and cleaning.

Data cleaning usually occupies 50% -80% of the analysis process. In this article, I will introduce the data cleaning process at a very shallow level for your reference.

In the era of big data, data cleaning is becoming more and more important and complex. Here introduces the experience of data cleaning.

The result quality of data cleaning is directly related to the model effect and final conclusion. Here shows the two purpose of data cleaning.

Data cleaning application areas include data cleaning in data warehouse applications and data mining applications, data cleaning in data quality management.

Before data analysis and visualization, it is often necessary to clean the data. This article introduces two free data cleaning tools.

SELECT query statement is the most frequently used and considered the most important among the database operation statements.

SELECT statement is the most frequently used and considered the most important among the database operation statements.

Select statement is the most frequently used and considered the most important among the database operation statements.