To ace your 𝐢𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰𝐬 for a DevOps Engineer role ,following Linux related Hardware commands, Data information, Process mgmt., files mgmt, search patterns and packages commands you should be aware of:

𝟏: 𝐇𝐀𝐑𝐃𝐖𝐀𝐑𝐄 𝐈𝐍𝐅𝐎𝐑𝐌𝐀𝐓𝐈𝐎𝐍:
-cat /proc/cpuinfo # Display CPU information
-cat /proc/meminfo # Display memory information
-free -h # Display free and used memory ( -hfor human readable, -mfor MB, -gfor GB.)
-lspci -tv # Display PCI devices
-lsusb -tv # Display USB devices
-dmidecode # Display DMI/SMBIOS (hardware info) from the BIOS
-hdparm -i /dev/sda # Show info about disk sda
-hdparm -tT /dev/sda # Perform a read speed test on disk sda

𝟐. 𝐌𝐀𝐍𝐈𝐏𝐔𝐋𝐀𝐓𝐈𝐍𝐆 𝐃𝐀𝐓𝐀 :
-awk # Pattern scanning and processing language
-perl # Data manipulation language
-cmp # Compare the contents of two files
-paste # Merge file data
-sed # Stream text editor
-cut # Cut out selected fields of each line of a file
-sort # Sort file data
-diff # Differential file comparator
-split # Split file into smaller files
-expand, unexpand # Expand tabs to spaces, and vice versa

𝟑. 𝐏𝐑𝐎𝐂𝐄𝐒𝐒 𝐌𝐀𝐍𝐀𝐆𝐄𝐌𝐄𝐍𝐓:
ps # Display your currently running processes
ps -ef # Display all the currently running processes on the system.
ps -ef | grep processname # Display process information for processname
top # Display and manage the top processes
htop # Interactive process viewer (top alternative)
kill pid # Kill process with process ID of pid
killall processname # Kill all processes named processname
program & # Start programin the background
bg # Display stopped or background jobs
fg # Brings the most recent background job to foreground
fg n # Brings job nto the foreground


𝟒. 𝐀𝐑𝐂𝐇𝐈𝐕𝐄𝐒 (𝐓𝐀𝐑 𝐅𝐈𝐋𝐄𝐒) :
-tar cf archive.tar directory # Create tar named archive .tar containing dir.
-tar xf archive.tar # Extract the contents from archive.tar. tar czf
-archive.tar.gz director # Create a gzip compressed tar file name archive.tar
-tar xzf archive.tar.gz # Extract a gzip compressed tar file.
-tar cjf archive.tar.bz2 directory # Create a tar file with bzip2 compression
-tar xjf archive.tar.bz2 # Extract a bzip2 compressed tar file.

𝟓. 𝐈𝐍𝐒𝐓𝐀𝐋𝐋𝐈𝐍𝐆 𝐏𝐀𝐂𝐊𝐀𝐆𝐄𝐒:
yum search keyword # Search for a package by keyword.
yum install package # Install package.
yum info package # Display desc and summary information about package.
rpm -i package.rpm # Install package from local file named package.rpm
yum remove package # Remove/uninstall package
tar zxvf sourcecode.tar.gz # Install software from source code.

𝟔. 𝐒𝐄𝐀𝐑𝐂𝐇 :
grep pattern file # Search for pattern in file
grep -r pattern directory # Search recursively for patternin directory
locate name # Find files and directories by name
find /home/john -name 'prefix*' # Find files in /home/john that start with "prefix".
find /home -size +100M # Find files larger than 100MB in /home


😎 𝐅𝐨𝐥𝐥𝐨𝐰 @prodevopsguy 𝐟𝐨𝐫 𝐦𝐨𝐫𝐞 𝐬𝐮𝐜𝐡 𝐜𝐨𝐧𝐭𝐞𝐧𝐭 𝐚𝐫𝐨𝐮𝐧𝐝 𝐜𝐥𝐨𝐮𝐝 & 𝐃𝐞𝐯𝐎𝐩𝐬!!! // 𝐉𝐨𝐢𝐧 𝐟𝐨𝐫 𝐃𝐞𝐯𝐎𝐩𝐬 𝐃𝐎𝐂𝐬: @devopsdocs

👍 Multi-Stage Build Images used in CICD ♾

➡️ Every microservice should be its own separate container. If you only use a single-stage Docker build, you’re probably missing out on some powerful features of the build process. On the other hand, a multi-stage Docker build has many advantages over a single-stage build for deploying microservices.

➡️ Some Advantages are :
- Optimizes the overall size of the Docker image
- Removes the burden of creating multiple Dockerfiles for different stages
- Easy to debug a particular build stage
- Able to use the previous stage as a new stage in the new environment
- Ability to use the cached image to make the overall process quicker
- Reduces the risk of vulnerabilities found as the image size becomes smaller with multi-stage builds


😎 𝗙𝗼𝗹𝗹𝗼𝘄 @prodevopsguy 𝗳𝗼𝗿 𝗺𝗼𝗿𝗲 𝘀𝘂𝗰𝗵 𝗰𝗼𝗻𝘁𝗲𝗻𝘁 𝗮𝗿𝗼𝘂𝗻𝗱 𝗰𝗹𝗼𝘂𝗱 & 𝗗𝗲𝘃𝗢𝗽𝘀!!! // Join for DevOps DOCs: @devopsdocs

In Kubernetes, readiness and liveness probes are mechanisms used to ensure that applications running inside containers are healthy and able to handle traffic properly. They are essential for maintaining the reliability and availability of applications in a Kubernetes cluster.

📢 Readiness Probe:
🎤 The readiness probe is used to determine when a container is ready to start accepting traffic. It is crucial for ensuring that services don't send requests to a container until it's ready to handle them effectively.

🎤If the readiness probe fails (returns a non-successful HTTP status code or times out), Kubernetes marks the container as not ready, and it won't receive any traffic until the probe succeeds.

🎤The readiness probe can be configured to use HTTP endpoints, TCP sockets, or custom scripts to determine the readiness of the container.

📢 Liveness Probe:
🎤The liveness probe is used to check if a container is still running properly. It helps Kubernetes determine whether a container should be restarted if it's unresponsive or in a failed state.

🎤Unlike the readiness probe, which determines if the container is ready to serve traffic, the liveness probe checks if the container is still functioning correctly after it has started.

🎤If the liveness probe fails (returns a non-successful HTTP status code or times out), Kubernetes restarts the container to try to recover it.

⚠️ Both the readiness and liveness probes are configured to perform an HTTP GET request to the /healthz endpoint on port 8080 of the container.

➡️The readiness probe will start 5 seconds after the container starts and will be performed every 10 seconds thereafter.

➡️The liveness probe will start 10 seconds after the container starts and will be performed every 15 seconds thereafter.

😎 𝐅𝐨𝐥𝐥𝐨𝐰 @prodevopsguy 𝐟𝐨𝐫 𝐦𝐨𝐫𝐞 𝐬𝐮𝐜𝐡 𝐜𝐨𝐧𝐭𝐞𝐧𝐭 𝐚𝐫𝐨𝐮𝐧𝐝 𝐜𝐥𝐨𝐮𝐝 & 𝐃𝐞𝐯𝐎𝐩𝐬!!! // 𝐉𝐨𝐢𝐧 𝐟𝐨𝐫 𝐃𝐞𝐯𝐎𝐩𝐬 𝐃𝐎𝐂𝐬: @devopsdocs

🔥 Understanding Kubernetes Logs - A Comprehensive Guide

🔣 Kubernetes logs are your essential toolkit for spotting and solving container app issues

➡️ Without that valuable insight, you'd be navigating in the dark, risking prolonged downtime and frustrated users.


😎 𝐅𝐨𝐥𝐥𝐨𝐰 @prodevopsguy 𝐟𝐨𝐫 𝐦𝐨𝐫𝐞 𝐬𝐮𝐜𝐡 𝐜𝐨𝐧𝐭𝐞𝐧𝐭 𝐚𝐫𝐨𝐮𝐧𝐝 𝐜𝐥𝐨𝐮𝐝 & 𝐃𝐞𝐯𝐎𝐩𝐬!!! // 𝐉𝐨𝐢𝐧 𝐟𝐨𝐫 𝐃𝐞𝐯𝐎𝐩𝐬 𝐃𝐎𝐂𝐬: @devopsdocs

📣 If you're in IT, learning Python 🐍 can open endless possibilities!

It's a versatile skill that can take you far, and it's known for being one of the easiest programming languages to learn and understand.

Here's a roadmap to help you master Python:

🔢. 𝗟𝗲𝗮𝗿𝗻 𝘁𝗵𝗲 𝗕𝗮𝘀𝗶𝗰𝘀: This includes data types, control flow, functions, and object-oriented programming.

🔢. 𝗘𝘅𝗽𝗹𝗼𝗿𝗲 𝗣𝘆𝘁𝗵𝗼𝗻 𝗟𝗶𝗯𝗿𝗮𝗿𝗶𝗲𝘀: Python boasts a vast ecosystem of libraries for everything from web development to data science to machine learning. Once you're comfortable with the fundamentals, delve into popular libraries like NumPy, pandas, Matplotlib, and scikit-learn.

🔢. 𝗕𝘂𝗶𝗹𝗱 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀: The best way to solidify your Python knowledge is by doing. Start building projects that pique your interest, like a web application, a data analysis dashboard, or a machine learning model.

🔢. 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁: Once you have some projects under your belt, learn how to deploy them for others to use. Python applications can be deployed in various ways, using web servers or container orchestration platforms.

🔢. 𝗧𝗲𝘀𝘁 𝗬𝗼𝘂𝗿 𝗖𝗼𝗱𝗲: Regularly testing your code ensures it functions as expected. Python offers various testing frameworks, including unittest and pytest.

🔢. 𝗕𝘂𝗶𝗹𝗱 𝗮 𝗣𝗼𝗿𝘁𝗳𝗼𝗹𝗶𝗼: Showcase your Python development skills by building a portfolio of your work. This will be a valuable asset when demonstrating your capabilities to potential employers.

🔢. 𝗨𝗽𝗱𝗮𝘁𝗲 𝗬𝗼𝘂𝗿 𝗥𝗲𝘀𝘂𝗺𝗲: Highlight your experience with Python libraries, projects, and deployments by revamping your resume.


😎 𝐅𝐨𝐥𝐥𝐨𝐰 @prodevopsguy 𝐟𝐨𝐫 𝐦𝐨𝐫𝐞 𝐬𝐮𝐜𝐡 𝐜𝐨𝐧𝐭𝐞𝐧𝐭 𝐚𝐫𝐨𝐮𝐧𝐝 𝐜𝐥𝐨𝐮𝐝 & 𝐃𝐞𝐯𝐎𝐩𝐬!!! // 𝐉𝐨𝐢𝐧 𝐟𝐨𝐫 𝐃𝐞𝐯𝐎𝐩𝐬 𝐃𝐎𝐂𝐬: @devopsdocs

➡️ Understanding Container Runtimes in Kubernetes 👈

A container runtime in Kubernetes is the software component responsible for managing the lifecycle of individual containers within a pod. It's the engine that executes the commands and manages the processes within the container environment.

➡️ What it does:

➡️Creates and starts containers: Based on instructions from the kubelet (the Kubernetes agent on each node), the container runtime pulls the container image, sets up the necessary resources, and fires up the container process.

➡️Manages container resources: It allocates CPU, memory, and other resources as specified in the pod definition, ensuring each container gets its fair share.

➡️Monitors and manages container health: It keeps an eye on the container's health and restarts it if it crashes or becomes unresponsive.

➡️Stops and removes containers: When a container is no longer needed, the runtime gracefully stops it and cleans up its resources.

➡️ Why it's important:

➡️Isolation: Container runtimes create isolated environments for each container, ensuring applications don't interfere with each other or the host system.

➡️Security: They enforce security policies and resource limitations, providing a more secure environment for containerized applications.

➡️Portability: Container runtimes adhere to industry standards, allowing containers to be easily moved between different platforms and cloud providers.

➡️Common container runtimes in Kubernetes.

- containerd
- CRI-O
- Docker Engine
- Mirantis Container Runtime


✈️ 𝗙𝗼𝗹𝗹𝗼𝘄 @prodevopsguy 𝗳𝗼𝗿 𝗺𝗼𝗿𝗲 𝘀𝘂𝗰𝗵 𝗰𝗼𝗻𝘁𝗲𝗻𝘁 𝗮𝗿𝗼𝘂𝗻𝗱 𝗰𝗹𝗼𝘂𝗱 & 𝗗𝗲𝘃𝗢𝗽𝘀!!!

🐬 6 Tips to 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐞 𝐘𝐨𝐮𝐫 𝐃𝐨𝐜𝐤𝐞𝐫𝐟𝐢𝐥𝐞

➡️𝐔𝐬𝐞 𝐌𝐮𝐥𝐭𝐢𝐬𝐭𝐚𝐠𝐞 𝐁𝐮𝐢𝐥𝐝𝐬:
- Utilize multiple stages to reduce the size of the final image.
- Keep the final image lean by copying only necessary artifacts from previous stages.

➡️𝐌𝐢𝐧𝐢𝐦𝐢𝐳𝐞 𝐋𝐚𝐲𝐞𝐫 𝐒𝐢𝐳𝐞:
- Combine multiple RUN commands using && to minimize the number of layers.
- Clean up unnecessary files and dependencies within the same RUN command.

➡️𝐋𝐞𝐯𝐞𝐫𝐚𝐠𝐞 .𝐝𝐨𝐜𝐤𝐞𝐫𝐢𝐠𝐧𝐨𝐫𝐞:
- Exclude unnecessary files and directories from the build context using .dockerignore.
- This reduces the size of the build context and speeds up the build process.

➡️𝐏𝐫𝐢𝐨𝐫𝐢𝐭𝐢𝐳𝐞 𝐄𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐭 𝐈𝐦𝐚𝐠𝐞 𝐋𝐚𝐲𝐞𝐫𝐬:
- Place frequently changing dependencies lower in the Dockerfile to leverage Docker's layer caching mechanism.
- Avoid unnecessary package installations that could bloat the image size.

➡️𝐔𝐬𝐞 𝐒𝐩𝐞𝐜𝐢𝐟𝐢𝐜 𝐓𝐚𝐠𝐬 𝐟𝐨𝐫 𝐁𝐚𝐬𝐞 𝐈𝐦𝐚𝐠𝐞𝐬:
- Specify precise version tags for base images to ensure consistency and avoid unexpected updates.
- Pinning versions mitigates the risk of breaking changes introduced by newer versions.

➡️𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐞 𝐈𝐦𝐚𝐠𝐞 𝐒𝐢𝐳𝐞:
- Use smaller base images like Alpine Linux where possible to reduce the overall size of the image.
- Remove unnecessary dependencies and files from the final image to make it as lightweight as possible.


😎 𝗙𝗼𝗹𝗹𝗼𝘄 @prodevopsguy 𝗳𝗼𝗿 𝗺𝗼𝗿𝗲 𝘀𝘂𝗰𝗵 𝗰𝗼𝗻𝘁𝗲𝗻𝘁 𝗮𝗿𝗼𝘂𝗻𝗱 𝗰𝗹𝗼𝘂𝗱 & 𝗗𝗲𝘃𝗢𝗽𝘀!!! // Join for DevOps DOCs: @devopsdocs

📌 https://prodevopsguy.xyz/posts/devops/linux_commands_for_devops_engineer/


✈️ 𝗙𝗼𝗹𝗹𝗼𝘄 @prodevopsguy 𝗳𝗼𝗿 𝗺𝗼𝗿𝗲 𝘀𝘂𝗰𝗵 𝗰𝗼𝗻𝘁𝗲𝗻𝘁 𝗮𝗿𝗼𝘂𝗻𝗱 𝗰𝗹𝗼𝘂𝗱 & 𝗗𝗲𝘃𝗢𝗽𝘀!!!

🎤 𝗛𝗼𝘄 𝗮𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝘂𝘀𝗲 𝗺𝘂𝗹𝘁𝗶-𝗰𝗹𝗼𝘂𝗱 𝘀𝗲𝗿𝘃𝗶𝗰𝗲: 𝐑𝐞𝐟𝐞𝐫𝐞𝐧𝐜𝐞 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞

Unleash the power of multiple clouds for optimal application performance, resilience, and security!

This reference architecture showcases a secure and resilient way to integrate Azure and AWS in a multi-cloud environment for:

✅ Effortless Traffic Management: Efficiently route traffic across both clouds for optimal performance.

✅ Unparalleled Uptime: Ensure app resilience with multi-cloud integration and smart traffic routing.

✅ Unbreachable Security: Maintain robust security controls across both cloud environments.


😎 𝐅𝐨𝐥𝐥𝐨𝐰 @prodevopsguy 𝐟𝐨𝐫 𝐦𝐨𝐫𝐞 𝐬𝐮𝐜𝐡 𝐜𝐨𝐧𝐭𝐞𝐧𝐭 𝐚𝐫𝐨𝐮𝐧𝐝 𝐜𝐥𝐨𝐮𝐝 & 𝐃𝐞𝐯𝐎𝐩𝐬!!! // 𝐉𝐨𝐢𝐧 𝐟𝐨𝐫 𝐃𝐞𝐯𝐎𝐩𝐬 𝐃𝐎𝐂𝐬: @devopsdocs

🛡 𝗞𝘂𝗯𝗲𝗿𝗻𝗲𝘁𝗲𝘀 𝗣𝗼𝗱 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗔𝗱𝗺𝗶𝘀𝘀𝗶𝗼𝗻 🛡

➡️ Within Kubernetes, containerized applications are managed as logical units called 𝐏𝐨𝐝𝐬. In any deployment environment, these 𝐏𝐨𝐝𝐬' security is vital. Kubernetes provides various security controls, such as 𝐏𝐨𝐝 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐒𝐭𝐚𝐧𝐝𝐚𝐫𝐝𝐬 (𝐏𝐒𝐒) and 𝐏𝐨𝐝 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐀𝐝𝐦𝐢𝐬𝐬𝐢𝐨𝐧 (𝐏𝐒𝐀), to efficiently manage the permissions and capabilities of Pods. These controls ensure that Pods operate with the minimum required access. This approach minimizes the risk of a compromised Pod affecting other resources.
[ 𝐊𝐮𝐛𝐞𝐫𝐧𝐞𝐭𝐞𝐬 𝐯𝐞𝐫𝐬𝐢𝐨𝐧 𝐯𝟏.𝟐𝟏 𝐬𝐡𝐢𝐟𝐭𝐞𝐝 𝐟𝐫𝐨𝐦 𝐏𝐨𝐝𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲𝐏𝐨𝐥𝐢𝐜𝐲 𝐭𝐨 𝐭𝐡𝐞 𝐧𝐞𝐰 𝐏𝐨𝐝 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐀𝐝𝐦𝐢𝐬𝐬𝐢𝐨𝐧 𝐜𝐨𝐧𝐭𝐫𝐨𝐥𝐬 ]

➡️ While 𝐏𝐨𝐝𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲𝐏𝐨𝐥𝐢𝐜𝐲 served its purpose, the new controls offer a more streamlined and accessible approach to enforcing security policies on Pods. 𝐏𝐨𝐝 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐀𝐝𝐦𝐢𝐬𝐬𝐢𝐨𝐧 introduces predefined security contexts and customization capabilities, enhancing flexibility, control, and ease of use.Understanding the significance of Pod security is fundamental to managing and operating Kubernetes clusters effectively and securely.

➡️ 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝗣𝗼𝗱 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗔𝗱𝗺𝗶𝘀𝘀𝗶𝗼𝗻
𝐏𝐨𝐝 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐀𝐝𝐦𝐢𝐬𝐬𝐢𝐨𝐧 is a feature introduced in Kubernetes to enforce clear and consistent isolation levels for Pods. It builds upon the Kubernetes Pod Security Standards, guidelines that govern how Pods behave and interact with other resources.

By applying security restrictions at the Kubernetes namespace level when Pods are created, 𝐏𝐨𝐝 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐀𝐝𝐦𝐢𝐬𝐬𝐢𝐨𝐧 provides a mechanism to ensure that Pods operate with only the necessary permissions. This enhances security and aligns with broader best practices in software deployment, minimizing the risk of unauthorized access or compromised resources.
The importance of 𝐏𝐨𝐝 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐀𝐝𝐦𝐢𝐬𝐬𝐢𝐨𝐧 lies in its ability to make security a fundamental and integral part of the Kubernetes ecosystem. Rather than treating security as an afterthought, 𝐏𝐨𝐝 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐀𝐝𝐦𝐢𝐬𝐬𝐢𝐨𝐧 ensures that it is part of the design and operation of every Pod.

➡️ 𝗗𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝗰𝗲𝘀 𝗯𝗲𝘁𝘄𝗲𝗲𝗻 𝗣𝗼𝗱 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗔𝗱𝗺𝗶𝘀𝘀𝗶𝗼𝗻 𝗮𝗻𝗱 𝘁𝗵𝗲 𝗱𝗲𝗽𝗿𝗲𝗰𝗮𝘁𝗲𝗱 𝗣𝗼𝗱𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆𝗣𝗼𝗹𝗶𝗰𝘆
Kubernetes version 1.21 significantly shifted from PodSecurityPolicy (PSP) to Pod Security Admission. While PSP intended to enforce security settings on Pods, it was deprecated due to its complexity and lack of flexibility.
Pod Security Admission introduces a more streamlined approach, utilizing labels to define admission control modes at the namespace level. These labels dictate the action the control plane takes if a potential violation is detected, such as rejection (enforce), audit annotation (audit), or user-facing warning (warn).


😎 𝐅𝐨𝐥𝐥𝐨𝐰 @prodevopsguy 𝐟𝐨𝐫 𝐦𝐨𝐫𝐞 𝐬𝐮𝐜𝐡 𝐜𝐨𝐧𝐭𝐞𝐧𝐭 𝐚𝐫𝐨𝐮𝐧𝐝 𝐜𝐥𝐨𝐮𝐝 & 𝐃𝐞𝐯𝐎𝐩𝐬!!! // 𝐉𝐨𝐢𝐧 𝐟𝐨𝐫 𝐃𝐞𝐯𝐎𝐩𝐬 𝐃𝐎𝐂𝐬: @devopsdocs

📌 https://palak-bhawsar.hashnode.dev/cicd-pipeline-for-terraform-project

🔗 More DevOps Blogs : HERE

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⭐️ 𝗙𝗼𝗹𝗹𝗼𝘄 @prodevopsguy & @devopsdocs 𝗳𝗼𝗿 𝗺𝗼𝗿𝗲 𝘀𝘂𝗰𝗵 𝗰𝗼𝗻𝘁𝗲𝗻𝘁 𝗮𝗿𝗼𝘂𝗻𝗱 𝗰𝗹𝗼𝘂𝗱 & 𝗗𝗲𝘃𝗢𝗽𝘀!!!

🔔 https://prodevopsguy.xyz/posts/devops/deploying-an-application-on-kubernetes-a-complete-guide/


😎 𝐅𝐨𝐥𝐥𝐨𝐰 @prodevopsguy 𝐟𝐨𝐫 𝐦𝐨𝐫𝐞 𝐬𝐮𝐜𝐡 𝐜𝐨𝐧𝐭𝐞𝐧𝐭 𝐚𝐫𝐨𝐮𝐧𝐝 𝐜𝐥𝐨𝐮𝐝 & 𝐃𝐞𝐯𝐎𝐩𝐬!!! // 𝐉𝐨𝐢𝐧 𝐟𝐨𝐫 𝐃𝐞𝐯𝐎𝐩𝐬 𝐃𝐎𝐂𝐬: @devopsdocs

✡️ Terraform Interview Question
❓ What is a data block in Terraform, and how it differs from a resource block?

🌀 In Terraform, a data block is used to fetch and read information from a data source, such as AWS, Azure, or any other provider. It allows us to query existing resources or data from external systems without actually creating or modifying them. Data blocks are primarily used for retrieving information that is necessary for configuring resources but doesn't need to be managed by Terraform itself.

🌀 Here's a basic example of a data block querying an AWS AMI :

data "aws_ami" "example" {
most_recent = true
filter {
name = "name"
values = ["ubuntu/images/hvm-ssd/ubuntu-focal-20.04-amd64-server-*"]
}

owners = ["099720109477"] # Canonical
}

In the above example :
🔯 data "aws_ami" "example" declares a data block named "example" that retrieves information about an AWS AMI.
🔯 most_recent = true ensures that the most recent AMI matching the specified criteria is selected.
🔯 The filter block specifies criteria for filtering AMIs, in this case, by name and owner.
🔯 Finally, owners specifies the AWS account IDs that own the AMIs.

On the other hand, a resource block in Terraform is used to define the infrastructure components that Terraform manages.


😎 𝐅𝐨𝐥𝐥𝐨𝐰 @prodevopsguy 𝐟𝐨𝐫 𝐦𝐨𝐫𝐞 𝐬𝐮𝐜𝐡 𝐜𝐨𝐧𝐭𝐞𝐧𝐭 𝐚𝐫𝐨𝐮𝐧𝐝 𝐜𝐥𝐨𝐮𝐝 & 𝐃𝐞𝐯𝐎𝐩𝐬!!! // 𝐉𝐨𝐢𝐧 𝐟𝐨𝐫 𝐃𝐞𝐯𝐎𝐩𝐬 𝐃𝐎𝐂𝐬: @devopsdocs

📌 https://harshhaa.hashnode.dev/devsecops-gitlab-cicd-amazon-app-deployment-on-aws-eks

🔗 More DevOps Blogs : HERE

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⭐️ 𝗙𝗼𝗹𝗹𝗼𝘄 @prodevopsguy & @devopsdocs 𝗳𝗼𝗿 𝗺𝗼𝗿𝗲 𝘀𝘂𝗰𝗵 𝗰𝗼𝗻𝘁𝗲𝗻𝘁 𝗮𝗿𝗼𝘂𝗻𝗱 𝗰𝗹𝗼𝘂𝗱 & 𝗗𝗲𝘃𝗢𝗽𝘀!!!

🚀 Excited to share some insights on Kubernetes architecture! 🌟

Kubernetes has revolutionized the way we deploy and manage containerized applications, but understanding its architecture can sometimes feel like navigating a complex labyrinth. Fear not! I've simplified it into bite-sized pieces for you. 🎉

🔍 Visual Breakdown: Check out the image below for a simplified visualization of Kubernetes architecture. It's like having a map to guide you through the Kubernetes landscape! 🗺

🧩 Key Components: Let's break it down:

Nodes: Think of them as the workers and managers in your application orchestra.

Pods: Your application's smallest building blocks, neatly packed containers.

Services: Gateways to your applications, ensuring seamless communication.

Controllers: The brains behind the operation, ensuring everything runs smoothly.

etcd: The reliable memory bank, storing all cluster data securely.

API Server, Scheduler, Controller Manager: The command center, orchestrating every move.

🔄 Interactions and Flow: Discover how these components interact with each other, forming a well-choreographed dance of scalability and resilience.

🌱 Continuous Learning: Kubernetes is a vast ecosystem, and there's always more to explore! Dive deeper into its intricacies to unlock its full potential.


✈️ 𝗙𝗼𝗹𝗹𝗼𝘄 @prodevopsguy & @devopsdocs 𝗳𝗼𝗿 𝗺𝗼𝗿𝗲 𝘀𝘂𝗰𝗵 𝗰𝗼𝗻𝘁𝗲𝗻𝘁 𝗮𝗿𝗼𝘂𝗻𝗱 𝗰𝗹𝗼𝘂𝗱 & 𝗗𝗲𝘃𝗢𝗽𝘀!!!

🐬 Docker Networking:

When Containers Talk: Decoding the Secret Language of Docker Networking.

🔢: Bridge (Default):
➡️ The default, isolated network where containers get unique IP addresses and can communicate with other containers on the same host.

➡️Use Cases:
➡️ Ideal for most containerized applications that need network isolation and communication within the host.
➡️ Well-suited for development, testing, and staging environments.

🔢: None:
➡️ Disables networking entirely, isolating the container from any network.

➡️Use Cases:
➡️ Niche use cases where no network access is required (e.g., headless tasks, security-sensitive processes).
➡️ Not recommended for general-purpose container usage.

🔢: Host:
➡️ Shares the host's network namespace, allowing containers to directly use the host's IP address and ports.

➡️Use Cases:
➡️ Exposing container services to the host's network without complex port mappings.
➡️ Useful for testing and development, but exercise caution due to potential security risks in production.

🔢: Overlay:
➡️ Description: Creates a virtual network across multiple Docker hosts, enabling container communication regardless of their physical location.

➡️Use Cases:
➡️ Orchestrated deployments with Docker Swarm or Kubernetes where containers need to communicate across hosts.
➡️ Highly scalable network for distributed applications.


😎 𝗙𝗼𝗹𝗹𝗼𝘄 @prodevopsguy 𝗳𝗼𝗿 𝗺𝗼𝗿𝗲 𝘀𝘂𝗰𝗵 𝗰𝗼𝗻𝘁𝗲𝗻𝘁 𝗮𝗿𝗼𝘂𝗻𝗱 𝗰𝗹𝗼𝘂𝗱 & 𝗗𝗲𝘃𝗢𝗽𝘀!!! // Join for DevOps DOCs: @devopsdocs

🎙 Lets talk about "lifecycle hooks in Kubernetes" and how you can use them to make your pods intelligent.

Pod lifecycle hooks, such as preStart and preStop, are features within Kubernetes that allow you to specify commands that should be executed at specific points in a container's lifecycle. Think of it like a subsystem that is monitoring and managing the task that you specify for these lifecycle stages.

➡️ postStart: Executed after a container has started. It can be used for tasks that need to be performed once the container is up and running. Initialization and Configuration, Dependency check, Health Checks are some common usecases that are configured here.

➡️ preStop: Executed just before a container is terminated. It provides an opportunity to perform cleanup tasks or gracefully shut down the application before the container stops. Graceful Shutdown, Connection Draining, Notification of Termination are all the things that can be configured here.

If your application needs customization for different environments like dev,stage, production, then you should definitely check these hooks out.


🔵 𝗙𝗼𝗹𝗹𝗼𝘄 @prodevopsguy 𝗳𝗼𝗿 𝗺𝗼𝗿𝗲 𝘀𝘂𝗰𝗵 𝗰𝗼𝗻𝘁𝗲𝗻𝘁 𝗮𝗿𝗼𝘂𝗻𝗱 𝗰𝗹𝗼𝘂𝗱 & 𝗗𝗲𝘃𝗢𝗽𝘀!!!

🐬 https://prodevopsguy.xyz/posts/devops/ultimate_docker_commands/


✈️ 𝗙𝗼𝗹𝗹𝗼𝘄 @prodevopsguy & @devopsdocs 𝗳𝗼𝗿 𝗺𝗼𝗿𝗲 𝘀𝘂𝗰𝗵 𝗰𝗼𝗻𝘁𝗲𝗻𝘁 𝗮𝗿𝗼𝘂𝗻𝗱 𝗰𝗹𝗼𝘂𝗱 & 𝗗𝗲𝘃𝗢𝗽𝘀!!!

☄️ Real-world Prometheus Deployment: A Practical Guide for Kubernetes Monitoring ☄️

🔗 Source Link: https://github.com/NotHarshhaa/Learning-Prometheus

🔗 Blog Link: https://harshhaa.hashnode.dev/real-world-prometheus-deployment-a-practical-guide-for-kubernetes-monitoring


✈️ 𝗙𝗼𝗹𝗹𝗼𝘄 @prodevopsguy 𝗳𝗼𝗿 𝗺𝗼𝗿𝗲 𝘀𝘂𝗰𝗵 𝗰𝗼𝗻𝘁𝗲𝗻𝘁 𝗮𝗿𝗼𝘂𝗻𝗱 𝗰𝗹𝗼𝘂𝗱 & 𝗗𝗲𝘃𝗢𝗽𝘀!!! // Join for DevOps DOCs: @devopsdocs

🚀 𝗥𝗲𝗮𝗱𝘆 𝘁𝗼 𝘁𝗮𝗸𝗲 𝘆𝗼𝘂𝗿 𝗗𝗲𝘃𝗢𝗽𝘀 𝗲𝘅𝗽𝗲𝗿𝘁𝗶𝘀𝗲 𝘁𝗼 𝘁𝗵𝗲 𝗻𝗲𝘅𝘁 𝗹𝗲𝘃𝗲𝗹?

Here are six cutting-edge software deployment strategies every engineer should have in their toolkit: 🖥

1️⃣ Shadow Deployment: Deploy new versions of software alongside existing production systems, directing a small portion of traffic to the new version for validation before full rollout.

2️⃣ Blue-Green Deployment: Mitigate downtime by maintaining two identical production environments, allowing seamless switchovers for updates.

3️⃣ Canary Deployment: Safely roll out changes by gradually introducing them to a subset of users, monitoring for any issues before full release.

4️⃣ Rolling Deployment: Update one server or a subset at a time, ensuring minimal disruption while continuously delivering new features.

5️⃣ Feature Toggle Deployment: Enable or disable specific features at runtime, providing flexibility to release updates without impacting users.

6️⃣ A/B Testing Deployment: Experiment with different versions of software simultaneously, gathering valuable insights into user preferences and behaviors to inform future development decisions.

Mastering these strategies empowers DevOps engineers to streamline processes, enhance reliability, and deliver value to users efficiently. Keep learning, keep innovating!


😎 𝗙𝗼𝗹𝗹𝗼𝘄 @prodevopsguy & @devopsdocs 𝗳𝗼𝗿 𝗺𝗼𝗿𝗲 𝘀𝘂𝗰𝗵 𝗰𝗼𝗻𝘁𝗲𝗻𝘁 𝗮𝗿𝗼𝘂𝗻𝗱 𝗰𝗹𝗼𝘂𝗱 & 𝗗𝗲𝘃𝗢𝗽𝘀!!!