For designing a real-time data processing system for monitoring network traffic, key technologies like Apache Kafka and Apache Storm are essential for handling high-throughput data streams. Utilizing a microservices architecture allows for scalability and fault isolation. Horizontal scaling using containerization platforms such as Docker and Kubernetes ensures flexibility and resource efficiency. Caching solutions like Redis can enhance performance by storing frequently accessed data.

Data completeness assessment involves ensuring that all required data elements or attributes are present and populated within a dataset. It verifies that no essential data fields are missing or left empty, which is essential for maintaining the integrity and usefulness of the data for analysis and decision-making purposes. Ensuring data completeness is a fundamental step in data quality management, particularly in scenarios where missing data can lead to biased or inaccurate analyses.

Apache Spark is a distributed storage system designed for high-performance analytics and machine learning workloads. Spark provides an in-memory computing engine that allows for processing large-scale data sets with high speed and efficiency. It supports various programming languages and offers rich libraries for diverse data processing tasks, making it a popular choice for big data analytics applications.

Challenges during the data loading phase of the ETL process often include managing large data volumes efficiently, ensuring data quality, addressing performance issues, and adapting to schema changes.

Git rebase is used to integrate changes from one branch to another. It works by applying each commit from the source branch onto the target branch. This can result in a cleaner, more linear commit history as compared to git merge.

In complex projects, 'topic branches' can be effectively utilized to facilitate parallel development. Each branch can focus on a specific feature or bug fix, allowing team members to work concurrently without interfering with the main codebase. This promotes a more efficient and organized development process.

In-depth Rebasing rewrites commit history, creating a cleaner and more straightforward timeline. Merging retains the commit history but may result in a more complex branch structure.

To recover a deleted branch that was not merged, you can use git reflog to view the history, find the commit hash where the branch was deleted, and then recreate the branch using git branch branch_name commit_hash.

In a DevOps workflow, Git branches are often aligned with Testing environments to facilitate continuous testing and integration before changes are deployed to Production.

Git Forking is a suitable approach for rapidly prototyping features in a large project while maintaining main branch stability. It allows developers to work on isolated copies (forks) of the main repository and propose changes through pull requests, ensuring a controlled integration process.

The Git command used to view the status of files and potential conflicts after a merge attempt is git status. This command provides information about modified, untracked, and conflicted files, allowing the user to understand the current state of the repository. git log shows the commit history, git diff displays the differences between commits, and git merge --status is not a valid command for checking the status after a merge attempt.

The git rm command is used to remove a file from both the working directory and the staging area. However, when used with the --cached option, it only removes the file from the staging area, leaving the working directory and the file system unchanged. This is useful for untracking a file without deleting it.