Big Data Development Interview Guide and Skill Tree Overview

Big Data Development Interview Guide

The article presents a structured skill tree for big data development positions, serving as a learning and revision outline.

Java Fundamentals

Language basics, locks, multithreading, concurrent containers (J.U.C)

Object-oriented concepts, data types, string internals, key keywords, collection implementations, dynamic proxies

Advanced Java

JVM memory structure, heap vs stack, Java Memory Model, garbage collection algorithms, JVM tuning parameters, class loading mechanisms

Netty architecture, threading model, serialization, pipeline, handlers

Linux Basics

Common commands, remote login, file operations, permission system, compression, user/group management, shell scripting

Distributed Theory

Cluster concepts, load balancing, consistency, 2PC/3PC, CAP theorem, Paxos, Raft, ZAB, distributed locks, transactions, ID generators

Offline Computing Foundations

Hadoop ecosystem: MapReduce principles, WordCount, combiner, partitioner, cluster setup, shuffle, data skew

HDFS architecture, configuration, NameNode HA, commands, safe mode

YARN roles, resource scheduling, task allocation

Hive basics, SQL translation to MapReduce, data formats, NULL storage, partitioning, query optimization

HBase columnar database: architecture, read/write flow, concurrency, MVCC, region design, hot-spot handling, performance tuning, filters, compaction, failure recovery

Real‑Time Computing

Kafka: architecture, concepts (broker, producer, consumer, topic, partition, ISR), election, message reliability, exactly‑once semantics, offset management

Spark: core (cluster modes, RDD, DAG, transformations, actions, shuffle, checkpoint), Streaming (DStream, Kafka integration, offset handling), SQL (Catalyst, DataFrame, optimization), Structured Streaming (model, windows, watermarks, fault tolerance), MLlib overview

Flink: cluster deployment, architecture, programming model, HA, DataSet/DataStream APIs, state management, windows, parallelism, integration with Kafka, Table/SQL, Blink SQL extensions

Big Data Algorithms

Common interview algorithm problems: large‑file word intersection, top‑N, deduplication, Bloom filter, bitmap, heap, trie, inverted index

Career & Resume Advice

Typical requirements from leading tech companies (language basics, backend fundamentals, offline and real‑time computing knowledge)

Resume best practices: clean formatting, avoid buzzword stuffing, highlight 1‑2 major projects, understand every listed technology, showcase internships or work experience

Emphasize both depth and breadth of technical skills and future‑oriented thinking