Praveen Singampalli

I joined Infosys and the six months of training into Java development made me strong enough to understand that software engineering is very tough and it's not an easy game to survive.

After the training i was mapped to the ENGNE unit of Infosys and to my surprise it was the Devops team.

📍The first two months of Journey

Devops was again very tough because it was completely new domain and my manager used to keep daily exams on various tools of Devops and again to my surprise I was the only one who got less marks in all exams😞

📍The project kickout journey

I was called to the cabin and my manager informed me that you are getting released from the team due to low Performance

📍 The Come back

I cried, I fumbled , I was struck on what to do but then with my friends support made my resume and got selected into TOYOTA in Infosys

📍 The Red Escalations

TOYOTA was completely on new teech and AWS it was really difficult to come up and daily i used to get escalations emails in red colour

📍 The learnings

With the help of my team lead I survived in Toyota and the project was completed with many ups and downs

📍 The Testing Journey - 6 months

I was mapped to the Testing project after the AWS project where i performed various testing activities like regression, progression, unit, functional etc.

📍 The DBA journey - 6 months

After the testing project completion I was mapped to DBA where I handled the Toyota production Databases

📍 The Java Journey - 1 year

It was again a red project filled with many escalations and development activities, i struggled a lot to come back to dev but made it go through with god's grace.

I am grateful to all my Managers, team leads, friends who supported me in my initial career journey.

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Forever grateful ❤️

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1. Terraform 2.0

Terraform has been the industry standard for Infrastructure as Code (IaC), but in 2025, Terraform 2.0 has introduced enhanced features like multi-cloud orchestration, improved state management, and faster provisioning. The tool is essential for automating infrastructure setup and ensuring repeatable environments across various cloud providers.

2. Kubernetes (K8s) Ecosystem Tools

While Kubernetes itself is already a dominant tool for container orchestration, the ecosystem around Kubernetes continues to grow, including the following:
Helm 4: Helm remains the package manager for Kubernetes, and the 2025 version comes with more advanced version control, security scanning, and dependency management.
Kustomize: A Kubernetes-native way to customize configurations, it is growing in use alongside Helm to handle more complex setups.

3. GitOps with Argo CD and Flux

Argo CD: A continuous delivery (CD) tool for Kubernetes, Argo CD has become increasingly popular for its ability to manage declarative configurations and track the state of apps in Git repositories.
Flux: As a GitOps tool, Flux enables you to manage deployments in Kubernetes using Git repositories as the source of truth. It continues to gain momentum in organizations practicing GitOps.

4. Packer by HashiCorp

Packer is increasingly important for creating machine images for use in various cloud environments. It integrates well with Terraform and is becoming a core tool in multi-cloud infrastructure strategies, helping teams create consistent machine images for automation pipelines.

5. Grafana Cloud & Prometheus

Grafana: Continues to evolve as the go-to tool for monitoring, dashboards, and visualization. Its cloud offering is becoming more popular in managing infrastructure metrics, logs, and traces.
Prometheus: As a monitoring and alerting toolkit, Prometheus is still integral in the DevOps toolchain, especially with Kubernetes. It's used alongside Grafana for enhanced observability.

6. CI/CD with GitLab

GitLab continues to gain popularity as an all-in-one DevOps platform, providing integrated CI/CD pipelines, source code management, and security features.

7. Cortex by Cortexica (AI-based DevOps Tools)
As AI continues to shape DevOps, tools like Cortex leverage machine learning to automate tasks such as log analysis, anomaly detection, and root cause analysis in real-time, helping organizations detect and fix issues faster.

8.AI & ML for Automation
9.Security (DevSecOps)
10. Multi-cloud and Hybrid Cloud
11. Serverless and Event-driven Architectures

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1. Automation of Tasks

Scripting and Automation: Python is often used for automating repetitive tasks like file manipulation, backups, deployment processes, server provisioning, and more. DevOps engineers frequently write Python scripts to automate infrastructure provisioning, configuration management, and deployment pipelines.

2. Infrastructure as Code (IaC)

Managing Infrastructure: With tools like Ansible and Terraform, DevOps engineers can define infrastructure in code. Python scripts can be used to write automation scripts that manage and provision infrastructure, making deployments faster and more consistent.
Integration with Cloud Providers

3. Configuration Management

Many DevOps tools, such as Ansible, SaltStack, and Fabric, use Python or allow Python-based scripts for configuration management and orchestration. Python scripts can help automate configurations for large numbers of servers or cloud instances.

4. CI/CD Pipelines

Continuous Integration and Delivery: DevOps relies heavily on continuous integration/continuous delivery (CI/CD). Python can be used to script and automate CI/CD pipelines, integrating with tools like Jenkins, GitLab CI, CircleCI, and more. For example, Python can interact with APIs of CI/CD tools to automate testing, deployment, and monitoring.
Testing: Python has excellent support for testing libraries (e.g., unittest, pytest), which are essential for ensuring that code deployments are reliable. Python can also automate the running of tests as part of CI/CD pipelines.

5. Monitoring and Logging

Python is often used to write custom monitoring scripts that track the health of infrastructure, check logs for specific errors, and alert teams about performance issues. Libraries like psutil (for system and process monitoring) and logging (for structured logging) are very useful in this domain.
Log Parsing: Python can parse, analyze, and correlate logs from various sources (e.g., web servers, application logs, system logs) to help identify issues and bottlenecks in systems.

6. Integration with DevOps Tools

Python can be integrated with many DevOps tools and services. For example:
Docker: Python can automate the management of Docker containers using libraries like docker-py.
Kubernetes: Python can interact with Kubernetes clusters to manage deployments, scale pods, or perform health checks, using the Kubernetes Python client.

7. Cross-Platform Support

Python is cross-platform, which means DevOps engineers can write scripts and automation tools that work across different operating systems (Linux, Windows, macOS). This is particularly important for managing diverse environments, as DevOps often deals with heterogeneous systems.

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