A blog site on our Real life experiences with various phases of DevOps starting from VCS, Build & Release, CI/CD, Cloud, Monitoring, Containerization.
In this blog, we will explore the world of Jenkins job DSL and learn how to leverage its capabilities to streamline and automate job configuration management. We will walk through the process of setting up the Job DSL environment, writing Job DSL scripts to define different types of jobs, managing job configurations as code, and integrating Job DSL with your CI/CD pipelines. Continue reading “Jenkins Job Creation using Multibranch Job DSL”
Let’s say you are the DevOps lead for a large e-commerce platform that runs on a microservices architecture with hundreds of services. You need to monitor the logs of all these services to quickly identify issues, troubleshoot problems, and optimize the system’s performance. You also want to be able to search and analyze logs across all services in real time and be alerted when any critical issues arise.
To address this scenario, you could use Grafana Loki as your centralized logging system. Loki is a lightweight and cost-effective solution that can handle high volumes of logs and store them in a distributed manner. You can configure each service to send logs to Loki, which will automatically index them and make them available for search and analysis.
Loki and Grafana are two open-source projects that are commonly used together for log aggregation, analysis, and visualization.
Loki is a horizontally-scalable, highly-available, multi-tenant log aggregation system inspired by Prometheus. It is designed to be a cost-effective solution for storing and querying logs and uses a unique indexing approach to provide fast and efficient searching of log data. Loki is also highly extensible, allowing users to easily add custom logging drivers and integrate with other systems.
Grafana, on the other hand, is a popular open-source platform for visualizing and analyzing time-series data, including logs. It provides a powerful and flexible dashboarding system that allows users to create customized visualizations and alerts based on their log data. Grafana also integrates with many different data sources, including Loki, which makes it a great choice for log analysis and visualization.
Continue reading “Basic Logging Setup of Loki Grafana”Generative AI in enterprise applications is advancing rapidly—so much so that by 2030, it’s predicted that 90% of a major blockbuster film could be created entirely by AI, compared to 0% in 2022. From text to video, this shift signals the transformative power of generative AI across industries.
Marketing and media are already feeling the impacts of generative AI. Gartner expects:
In today’s rapidly evolving technological landscape, artificial intelligence (AI) has emerged as a game-changer for businesses. Among the many branches of AI, generative AI stands out as a remarkable field that holds immense potential for revolutionizing enterprise applications.
Generative AI, a subset of machine learning, goes beyond traditional AI models that are designed to analyze and interpret existing data. Instead, it focuses on the creation of new, original content by learning patterns and generating outputs that mimic human-like creativity. This remarkable ability to generate realistic and coherent data has opened up exciting possibilities for enterprises. This empowers them to innovate, streamline operations and enhance customer experiences.
In this blog, we’ll delve into the future of generative AI and its implications for enterprise applications. We will explore the transformative power of generative AI in various sectors, including marketing, product development, customer service and more. By harnessing the potential of generative AI, businesses can unlock new opportunities, gain a competitive edge and navigate the complex challenges in tomorrow’s business landscape of AIOps and Managed Cloud Services.
Let’s dive in and explore the endless possibilities that lie ahead as we uncover “The Future of Generative AI in Enterprise Applications.”
Continue reading “The Future of Generative AI in Enterprise Applications”
Estimating costs can be a nightmare for many enterprises. Cloud cost estimation is important for organizations to plan their budget and forecast expenses accurately. It is essential to monitor and analyze cloud usage regularly to optimize cloud spending and avoid unexpected expenses.
This process is very time-consuming, so there was a need for change. With Terraform, you can easily estimate cloud costs by leveraging Infracost, and you can easily compare potential bills between different vendors.
Working with cloud providers and DevOps is all about speed, efficiency, and cost management.
Infracost is a tool that is used to figure out how much the cloud resources will cost.
Infracost is a super cool tool that lets you calculate the cost of your Terraform resources on AWS, GCP, or Microsoft Azure before you even hit deploy. This enables you to see cloud cost estimates for Terraform in pull requests.
Continue reading “Cloud Cost Estimation with Infracost”
Azure Content Delivery Network (CDN) is a CDN service provided by Azure Cloud Platform that enables storing and accessing data on different content servers and locations – used by online or cloud services. A CDN store the content cached on the edge servers that are available in the POP locations to reduce latency. Azure CDN is important for us which requires multiple hits to boost up the process of our applications.
