DevOps & Cloud (AWS, AZURE, GCP) Tech Free Learning
https://projects.prodevopsguytech.com // https://blog.prodevopsguytech.com • We post Daily Trending DevOps/Cloud content • All DevOps related Code & Scripts uploaded • DevOps/Cloud Job Related Posts • Real-time Interview questions & preparation guides
Show more📈 Analytical overview of Telegram channel DevOps & Cloud (AWS, AZURE, GCP) Tech Free Learning
Channel DevOps & Cloud (AWS, AZURE, GCP) Tech Free Learning (@prodevopsguy) in the English language segment is an active participant. Currently, the community unites 16 318 subscribers, ranking 7 977 in the Technologies & Applications category and 25 792 in the India region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 16 318 subscribers.
According to the latest data from 07 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 166 over the last 30 days and by 0 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 0%. Within the first 24 hours after publication, content typically collects N/A% reactions from the total number of subscribers.
- Post reach: On average, each post receives 0 views. Within the first day, a publication typically gains 0 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 0.
- Thematic interests: Content is focused on key topics such as devops, docker, terraform, kubernete, git.
📝 Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
“https://projects.prodevopsguytech.com // https://blog.prodevopsguytech.com
• We post Daily Trending DevOps/Cloud content
• All DevOps related Code & Scripts uploaded
• DevOps/Cloud Job Related Posts
• Real-time Interview questions & preparation guid...”
Thanks to the high frequency of updates (latest data received on 08 July, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Technologies & Applications category.
But If I was learning kubernetes today, then I would follow the path shown below in the diagram and never jump to K8s or docker directly.✈️ 𝗙𝗼𝗹𝗹𝗼𝘄 @prodevopsguy 𝗳𝗼𝗿 𝗺𝗼𝗿𝗲 𝘀𝘂𝗰𝗵 𝗰𝗼𝗻𝘁𝗲𝗻𝘁 𝗮𝗿𝗼𝘂𝗻𝗱 𝗰𝗹𝗼𝘂𝗱 & 𝗗𝗲𝘃𝗢𝗽𝘀!!!
Developing familiarity with these core Docker capabilities empowers you to containerize applications and streamline development workflows.✈️ 𝗙𝗼𝗹𝗹𝗼𝘄 @prodevopsguy 𝗳𝗼𝗿 𝗺𝗼𝗿𝗲 𝘀𝘂𝗰𝗵 𝗰𝗼𝗻𝘁𝗲𝗻𝘁 𝗮𝗿𝗼𝘂𝗻𝗱 𝗰𝗹𝗼𝘂𝗱 & 𝗗𝗲𝘃𝗢𝗽𝘀!!!
A Kafka producer is an entity that publishes data to topics within the Kafka cluster. In essence, producers are the sources of data streams, which might originate from various applications, systems, or sensors. They push records into Kafka topics, and each record consists of a key, a value, and a timestamp.🔴 𝗖𝗼𝗻𝘀𝘂𝗺𝗲𝗿:
A Kafka consumer pulls data from Kafka topics to which it subscribes. Consumers process the data and often are part of a consumer group. In a group, multiple consumers can read from a topic in parallel, with each consumer responsible for reading from certain partitions, ensuring efficient data processing.🔴 𝗧𝗼𝗽𝗶𝗰:
A topic is a category or feed name to which records are published. Topics in Kafka are multi-subscriber; they can be consumed by multiple consumers and consumer groups. Topics are divided into partitions to allow for data scalability and parallel processing.🔴 𝗣𝗮𝗿𝘁𝗶𝘁𝗶𝗼𝗻:
A topic can be divided into partitions, which are essentially subsets of a topic's data. Each partition is an ordered, immutable sequence of records that is continually appended to. Partitions allow topics to be parallelized by splitting the data across multiple brokers.🔴 𝗕𝗿𝗼𝗸𝗲𝗿:
A broker is a single Kafka server that forms part of the Kafka cluster. Brokers are responsible for maintaining the published data. Each broker may have zero or more partitions per topic and can handle data for multiple topics.🔴 𝗖𝗹𝘂𝘀𝘁𝗲𝗿:
A Kafka cluster comprises one or more brokers. The cluster is the physical grouping of one or more brokers that work together to provide scalability, fault tolerance, and load balancing. The Kafka cluster manages the persistence and replication of message data.🔴 𝗥𝗲𝗽𝗹𝗶𝗰𝗮:
A replica is a copy of a partition. Kafka replicates partitions across multiple brokers to ensure data is not lost if a broker fails. Replicas are classified as either leader replicas or follower replicas.🔴 𝗟𝗲𝗮𝗱𝗲𝗿 𝗥𝗲𝗽𝗹𝗶𝗰𝗮:
For each partition, one broker is designated as the leader. The leader replica handles all read and write requests for the partition. Other replicas simply copy the data from the leader.🔴 𝗙𝗼𝗹𝗹𝗼𝘄𝗲𝗿 𝗥𝗲𝗽𝗹𝗶𝗰𝗮:
Follower replicas are copies of the leader replica for a partition. They replicate the leader's log and do not serve client requests. Instead, their purpose is to provide redundancy and to take over as the leader if the current leader fails.✈️ 𝗙𝗼𝗹𝗹𝗼𝘄 @prodevopsguy 𝗳𝗼𝗿 𝗺𝗼𝗿𝗲 𝘀𝘂𝗰𝗵 𝗰𝗼𝗻𝘁𝗲𝗻𝘁 𝗮𝗿𝗼𝘂𝗻𝗱 𝗰𝗹𝗼𝘂𝗱 & 𝗗𝗲𝘃𝗢𝗽𝘀!!!
kubectl port-forward svc/nginx 8080:80
❌ Now. Here's a problem:
1. Wonder what happens if the traffic serving pod is terminated?
2. The browser returns "refused to connect" error.
Why?
Because the tunnel is broken.
✔️ To re-establish connection:
"You need to run port-forward command again."
"Port forwarding is useful for testing only."
"For production use cases, always use deployments"
Hope you happily learned something 😎
✈️ 𝗙𝗼𝗹𝗹𝗼𝘄 @prodevopsguy 𝗳𝗼𝗿 𝗺𝗼𝗿𝗲 𝘀𝘂𝗰𝗵 𝗰𝗼𝗻𝘁𝗲𝗻𝘁 𝗮𝗿𝗼𝘂𝗻𝗱 𝗰𝗹𝗼𝘂𝗱 & 𝗗𝗲𝘃𝗢𝗽𝘀!!!Continuous integration is the practice of merging developer working copies to shared repositories multiple times per day. With CI, developers frequently commit their code changes to a shared version control repository. Each commit triggers an automated build and test process to catch integration errors as early as possible. CI helps teams avoid "integration hell" that can happen when developers work in isolation for too long before merging their changes.➡️ 𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗗𝗲𝗹𝗶𝘃𝗲𝗿𝘆 👇
Continuous delivery takes CI a step further with automated releases. CD means that at any point, you can push a button to release the latest app version to users. The CD pipeline deploys each code change to a testing/staging environment and runs automated tests to confirm the app is production ready. This ensures developers always have a releasable artifact that has passed tests. While CD enables releasing often, someone still needs to manually push the button to promote changes to production.➡️ 𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁👇
Continuous deployment fully automates the release process. Every code commit that passes the automated tests triggers an immediate production deployment. This enables teams to ship features as fast as developers write code. However, the business may not want to release daily since this could overwhelm users with constant changes. Many teams use feature flags so developers can deploy new features, but limit their exposure until the business is ready for the public launch. Adopting CI, CD, and CD practices can accelerate a team's ability to safely deliver innovation. The key is automating repetitive processes to limit manual errors, provide rapid feedback, and reduce risk. This frees up developers to focus their energy on writing great code rather than building and deploying it. The outcome is faster time-to-market and more frequent delivery of customer value.✈️ 𝗙𝗼𝗹𝗹𝗼𝘄 @prodevopsguy 𝗳𝗼𝗿 𝗺𝗼𝗿𝗲 𝘀𝘂𝗰𝗵 𝗰𝗼𝗻𝘁𝗲𝗻𝘁 𝗮𝗿𝗼𝘂𝗻𝗱 𝗰𝗹𝗼𝘂𝗱 & 𝗗𝗲𝘃𝗢𝗽𝘀!!!
