I'm Adarsh, A Software Engineer with 13 years of experience, have worked with a few firms with ThoughtWorks and Rapido being my recent gigs. Currently I lead the Infrastructure platform team at Rapido, leading a team of 5 and handling Infra for India's largest bike Taxi at Scale with focus on Architecture, Scalability, Resilience, Security and DevX. I have experience with: - Microservice architecture, - Cloud native patterns and technologies. - Handling a Kubernetes cluster with 100+ microservices and 800+ Nodes with frequent releases ensuring uptime and owning Incident management and reliability from Infra side. - Self Managing Databases at Scale - Kafka, MongoDB, Redis - CI/CD
My Mentoring Topics
- Getting Started with Infrastructure/Platform Engineering (DevOps)
- Kubernetes | Containers | Container Orchestration
- CI/CD
- Observability
- Scaling Distributed datastores - Kafka | Redis | MongoDB
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Designing Data-Intensive Applications - The Big Ideas Behind Reliable, Scalable, and Maintainable Systems
Martin Kleppmann
Key Facts and Insights The book explores the underlying principles of data systems and how they are used to build reliable, scalable, and maintainable applications. It outlines the importance of distributed systems in handling data-intensive applications and how to deal with the challenges associated with them. The book emphasizes on the trade-offs involved in choosing particular data structures, algorithms, and architectures for data-intensive applications. It provides a detailed explanation of the three main components of data systems: storage, retrieval, and processing. It presents an in-depth understanding of consistency and consensus in the context of distributed systems. The book discusses various data models, including relational, document, graph, and many more, along with their suitable use cases. It also examines the concept of stream processing and batch processing, their differences, and when to use each. It underlines the significance of maintaining data integrity and the techniques to ensure it. It offers comprehensive coverage of the replication and partitioning strategies in distributed systems. The book provides a balanced view of various system design approaches, explaining their strengths and weaknesses. Lastly, the book does not recommend one-size-fits-all solutions. Instead, it equips the reader with principles and tools to make informed decisions depending on the requirements of their projects. In-Depth Analysis of the Book "Designing Data-Intensive Applications" by Martin Kleppmann is a comprehensive guide to understanding the fundamental principles of data systems and their effective application in designing reliable, scalable, and maintainable systems. It provides an exhaustive account of the paradigms and strategies used in data management and their practical implications. Understanding Data Systems The book begins by introducing the basics of data systems, explaining their role in managing and processing large volumes of data. It delves into the three main components of data systems: storage, retrieval, and processing. Each component is explored in detail, providing the reader with a clear understanding of its functionality and importance in a data system. Data Models and Query Languages The book delves into the various data models used in data-intensive applications, such as relational, document, and graph models. It provides a comparative analysis of these models, highlighting their strengths and weaknesses, and the specific use cases they are best suited for. Additionally, it discusses the role of query languages in data interaction, explaining how they facilitate communication between the user and the data system. Storage and Retrieval The book explains the techniques and data structures used for efficiently storing and retrieving data. It underlines the trade-offs involved in choosing a particular approach, emphasizing the importance of taking into account the specific requirements of the application. Distributed Data The book delves into the complexities of distributed data. It outlines the significance of distributed systems in handling data-intensive applications and discusses the challenges associated with them, such as data replication, consistency, and consensus. It also provides solutions to these challenges, equipping the reader with strategies to effectively manage distributed data. Data Integrity The book underscores the significance of maintaining data integrity. It provides an in-depth understanding of the concept and discusses techniques to ensure it, such as atomicity, consistency, isolation, and durability (ACID) and base properties. Stream Processing and Batch Processing The book examines the concept of stream processing and batch processing. It discusses their differences, the challenges associated with each, and the scenarios where one would be preferred over the other. Conclusion In conclusion, "Designing Data-Intensive Applications" is a comprehensive guide that provides readers with a deep understanding of data systems. It equips them with the knowledge to make informed decisions when designing data-intensive applications, based on the specific requirements of their projects. The book's strength lies in its balanced view of various system design approaches, offering a holistic understanding of the dynamics involved in managing data. It is an essential read for anyone seeking to delve into the world of data systems.
ViewKubernetes: Up and Running - Dive into the Future of Infrastructure
Brendan Burns, Joe Beda, Kelsey Hightower, Lachlan Evenson
In just five years, Kubernetes has radically changed the way developers and ops personnel build, deploy, and maintain applications in the cloud. With this book's updated third edition, you'll learn how this popular container orchestrator can help your company achieve new levels of velocity, agility, reliability, and efficiency--whether you're new to distributed systems or have been deploying cloud native apps for some time. Brendan Burns, Joe Beda, Kelsey Hightower, and Lachlan Evenson--who have worked on Kubernetes at Google and beyond--explain how this system fits into the life cycle of a distributed application. Software developers, engineers, and architects will learn ways to use tools and APIs to automate scalable distributed systems for online services, machine learning applications, or even a cluster of Raspberry Pi computers. This guide shows you how to: Create a simple cluster to learn how Kubernetes works Dive into the details of deploying an application using Kubernetes Learn specialized objects in Kubernetes, such as DaemonSets, jobs, ConfigMaps, and secrets Explore deployments that tie together the lifecycle of a complete application Get practical examples of how to develop and deploy real-world applications in Kubernetes
ViewTest-driven Development - By Example
Kent Beck
Clean code that works--now. This is the seeming contradiction that lies behind much of the pain of programming. Test-driven development replies to this contradiction with a paradox--test the program before you write it. A new idea? Not at all. Since the dawn of computing, programmers have been specifying the inputs and outputs before programming precisely. Test-driven development takes this age-old idea, mixes it with modern languages and programming environments, and cooks up a tasty stew guaranteed to satisfy your appetite for clean code that works--now. Developers face complex programming challenges every day, yet they are not always readily prepared to determine the best solution. More often than not, such difficult projects generate a great deal of stress and bad code. To garner the strength and courage needed to surmount seemingly Herculean tasks, programmers should look to test-driven development (TDD), a proven set of techniques that encourage simple designs and test suites that inspire confidence. By driving development with automated tests and then eliminating duplication, any developer can write reliable, bug-free code no matter what its level of complexity. Moreover, TDD encourages programmers to learn quickly, communicate more clearly, and seek out constructive feedback. Readers will learn to: Solve complicated tasks, beginning with the simple and proceeding to the more complex. Write automated tests before coding. Grow a design organically by refactoring to add design decisions one at a time. Create tests for more complicated logic, including reflection and exceptions. Use patterns to decide what tests to write. Create tests using xUnit, the architecture at the heart of many programmer-oriented testing tools. This book follows two TDD projects from start to finish, illustrating techniques programmers can use to easily and dramatically increase the quality of their work. The examples are followed by references to the featured TDD patterns and refactorings. With its emphasis on agile methods and fast development strategies, Test-Driven Development is sure to inspire readers to embrace these under-utilized but powerful techniques. 0321146530B10172002
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