Build fundamentals first — load balancing, caching, CDNs, SQL vs NoSQL, sharding, replication, message queues, and CAP theorem — then study 10–15 classic designs such as a URL shortener, chat app, and payment system. Since most product-based companies in India include a design round from SDE-2 onwards, give yourself 4–6 weeks of focused preparation alongside your regular DSA practice, and rehearse explaining trade-offs out loud, because that is what interviewers actually evaluate.

Pick well-known problems, set a 40–45 minute timer, and design end-to-end on a whiteboard or drawing tool while speaking your reasoning aloud. Do at least 6–8 timed mock interviews, ideally 1:1 with an experienced engineer who can interrupt, question your assumptions, and give honest feedback the way a real interviewer does — passively reading design articles rarely builds this muscle.

Follow a repeatable framework: clarify functional and non-functional requirements, do quick back-of-the-envelope capacity estimates, define the API, sketch the high-level architecture, then deep-dive into database schema, caching, and partitioning, and finish by calling out bottlenecks and trade-offs. Interviewers judge how you reason under constraints, not whether your design matches one "correct" answer.

The classics are designing a URL shortener, rate limiter, chat app, Instagram-style news feed, notification system, and ride-hailing or payment platform — fintech and mobility companies in India often add constraints like idempotency and transaction consistency. Expect follow-ups on scaling to millions of users, handling failures, and keeping data consistent across regions.

It is a widely used preparation course that teaches a step-by-step framework through classic design problems like URL shorteners, news feeds, and chat systems. It is a solid starting point for learning standard patterns and vocabulary, but pair it with live, timed practice because a course alone will not prepare you to design under an interviewer's pressure.

System Design Interview by Alex Xu (Volumes 1 and 2) is the most popular system design interview book because it breaks down realistic problems with clear diagrams and a repeatable approach. Use it to build your base, then supplement it with engineering blogs about how real companies scaled their systems.

A distributed system is a group of independent computers connected over a network that cooperate so closely that users see them as one single system — UPI payments, WhatsApp, and IRCTC all run on such systems. The distributed systems architecture — how nodes communicate, replicate data, and survive failures — determines whether the system stays fast, reliable, and consistent at scale.

Strengthen your OS, networking, and database fundamentals first, then take a structured distributed systems course or university lectures covering consistency, replication, partitioning, consensus, and fault tolerance. Reinforce the theory by reading a standard textbook and building small projects, because these concepts only truly click when you see them break in practice.

Start with a distributed key-value store, a simple message queue, a rate limiter service, a URL shortener with caching and sharding, or a replicated log with basic leader election. Deploy them across multiple VMs or containers, deliberately kill a node, and observe recovery — these are exactly the failure scenarios that distributed systems interview questions probe.

Designing Data-Intensive Applications by Martin Kleppmann is the best modern distributed systems book for engineers, while classics like Distributed Systems Principles and Paradigms by Tanenbaum cover the theoretical foundations. Read Kleppmann for practical, interview-relevant depth, and use a classic textbook when you need formal grounding in concepts like consensus and consistency.

A distributed system is any setup where multiple independent machines cooperate over a network, while microservices is an architectural style that splits one application into small, independently deployable services. In other words, microservices are one popular way of building distributed systems — every microservices-based application is distributed, but not every distributed system uses microservices.

Expect questions on strong vs eventual consistency, CAP and PACELC, replication and failover, consensus algorithms like Raft and Paxos, idempotency, exactly-once vs at-least-once delivery, split-brain, clock skew, and distributed transactions. Scenario prompts such as "design a distributed rate limiter" or "how would you prevent double payment in a wallet system" are common at product-based companies.

Pick one language and move pattern by pattern: arrays and hashing, two pointers and sliding window, stacks and queues, linked lists, binary search, trees, heaps, backtracking, graphs, and finally dynamic programming. Solve one to two quality problems daily, revise your mistakes weekly, and prioritise the patterns that repeat in placements and product-company interviews over random problem solving.

Both are accepted everywhere, so choose based on comfort and target roles. Practising data structures and algorithms in Java makes sense if you are aiming for backend roles at product companies where Java dominates, while data structures and algorithms in Python lets you write shorter, faster code in timed online tests and interviews. The key is to stick to one language throughout your preparation.

Not mandatory — a disciplined self-study plan with consistent daily practice works well if you have enough time before placement season. A structured data structures and algorithms placement preparation course helps when you are starting late, need deadlines and mentor feedback, or keep losing momentum; whichever route you take, prioritise pattern-based practice and mock interviews over passively watching videos.