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Nowadays, application development organizations must integrate and automate DevOps security tools such as SAST into the CI/CD pipeline to speed up developers.

Through DevOps, we can often implement continuous development and release of software for end users. DevOps also allows developers and operations to work together more efficiently and effectively.

In the development stage, embedding the source code in the software is both economical and efficient. With the help of other resources, DevOps can focus more on the internal code of the organization.

Security is a misunderstood element in DevOps. Breaking the information island and becoming an advocate of cybersecurity will have a beneficial impact on you, your career, and your business organization.

Depending on the nature of containers and Docker technology, developers can easily share their software and dependencies with IT operations and production environments.

I think we should first analyze what are the biggest problems encountered by our existing R&D operation and maintenance system, and gradually resolve DevOps by solving these problems or pain points.

DevOps has been proposed for almost ten years, but it has really been popular for these two years, but when it is launched, it will be found to be a very large project, and you may not know where to start.

The five basic values of the DevOps mindset described below will support our practice. These values are not new ideas, they have been reconstructed.

Dataframe is a data structure in pandas in python. A structure similar to a table.Here intoduces some of Dataframe common operations.

Spark Streaming is an extension of the Spark core API, which can realize high-throughput, real-time streaming data processing with fault tolerance mechanism.

The function of Spark Streaming is to process data from different data sources in real time and output the results to an external file system.

Spark Streaming is an important framework in the Spark ecosystem. It is built on Spark Core.

Spark Streaming is an extension of Spark's core API, which can implement high-throughput, fault-tolerant real-time streaming data processing.

Spark Streaming is an extension of Spark's core API, which can implement high-throughput, fault-tolerant real-time streaming data processing.

Spark Streaming has two characteristics of high throughput and strong fault tolerance, and Spark Streaming supports many data input sources.

Spark Streaming is one of the most commonly used components in Spark. There will be more and more users with stream processing requirements embarking on the use of Spark.

Spark Streaming discretizes streaming data to enable it to perform micro-batch processing below the second level.

Spark Streaming is an extension of the Spark core API, which can realize high-throughput, real-time streaming data processing with fault tolerance mechanism.

Spark Streaming is an extension of Spark's core API, which can implement high-throughput, fault-tolerant real-time streaming data processing.

Spark Streaming is an extension of the Spark core API, which can realize high-throughput, real-time streaming data processing with fault tolerance mechanism.