3-Day Hadoop Interview Prep
WHY HADOOP MATTERS FOR YOUR INTERVIEW
- Amadeus JD explicitly mentions: Hadoop/Hive as required technology
- With 10 years experience: interviewers expect deep internals (NameNode HA, YARN schedulers, Hive optimization)
- Companies still run massive Hadoop clusters — knowing it + knowing how to MIGRATE to Spark/cloud is gold
- Critical bridge question: "How would you migrate this Hadoop pipeline to Databricks/Spark?" — shows you know BOTH
3-DAY SCHEDULE
Memory Map
DAY 15-6 hoursHDFS + YARN + MAPREDUCE INTERNALS
HDFS Architecture (NameNode, DataNode, blocks, replication)
HDFS NameNode High Availability (Active/Standby, JournalNodes)
HDFS Federation (multiple NameNodes for horizontal scaling)
HDFS Read & Write paths (step-by-step internals)
YARN Architecture (ResourceManager, NodeManager, ApplicationMaster)
YARN Schedulers (FIFO, Capacity, Fair — when to use which)
MapReduce Internals (Map → Shuffle → Sort → Reduce)
MapReduce Optimization (combiner, partitioner, compression)
Small Files Problem & Solutions
Hadoop 1 vs Hadoop 2 vs Hadoop 3
DAY 25-6 hoursHIVE + ECOSYSTEM TOOLS
Hive Architecture (Metastore, Driver, Compiler, Execution Engine)
Hive Internal vs External Tables
Hive Partitioning (static, dynamic) — design decisions
Hive Bucketing — vs partitioning, when to use
Hive File Formats (ORC, Parquet, Avro, Text — when to use which)
Hive Query Optimization (vectorization, Tez, LLAP, joins)
HBase Architecture (row key design, regions, compactions)
Sqoop (import/export, incremental loads, split-by)
Flume (sources, channels, sinks — for log ingestion)
Oozie (workflow vs coordinator jobs)
ZooKeeper (leader election, distributed coordination)
Scenario: Design a complete Hadoop pipeline for Amadeus
DAY 35-6 hoursPERFORMANCE, SECURITY + CLOUD MIGRATION
Hadoop Security (Kerberos, Apache Ranger, Knox, TLS)
Hadoop Performance Tuning (JVM, GC, memory settings)
Data Skew handling in MapReduce and Hive
HDFS Balancer & Block Management
Hadoop Cluster Sizing & Capacity Planning
Cloudera CDP vs Hortonworks HDP vs Apache Hadoop
Hadoop to Cloud Migration Patterns
Lift-and-Shift (HDFS → ADLS/S3)
Replatform (MapReduce → Spark)
Refactor (Hive → Delta Lake / Snowflake)
Hadoop vs Spark — key differences (as a 10-year engineer)
Pig Latin — basics + when you'd use it vs Hive vs Spark
Mock Interview — 10 most-likely questions for 10-year engineers
FILES STRUCTURE
| Day | Main File (Deep Questions) | Quick Recall File |
|---|---|---|
| Plan | HD_00_INTERVIEW_PLAN.md | — |
| 1 | HD_01_HDFS_YARN_MapReduce.md | HD_01_Quick_Recall.md |
| 2 | HD_02_Hive_Ecosystem.md | HD_02_Quick_Recall.md |
| 3 | HD_03_Performance_Security_Migration.md | HD_03_Quick_Recall.md |
Total: 7 files (1 plan + 3 main + 3 quick recall)
PRIORITY MATRIX
MUST KNOW (Will definitely be asked — 55%)
- HDFS architecture — NameNode/DataNode, blocks, replication factor
- NameNode HA — Active/Standby, JournalNodes, ZooKeeper
- YARN — ResourceManager, NodeManager, ApplicationMaster
- MapReduce flow — Map → Shuffle → Sort → Reduce (step by step)
- Hive partitioning vs bucketing — when to use each
- ORC vs Parquet vs Text — file formats and tradeoffs
- Hive optimization — vectorization, Tez, execution engine
- Small files problem — causes and solutions
SHOULD KNOW (High probability — 30%)
- Hive internal vs external tables
- Sqoop incremental imports
- HBase row key design
- YARN schedulers (Capacity, Fair)
- MapReduce combiner and partitioner
- Kerberos authentication in Hadoop
- Apache Ranger for authorization
- HDFS Federation
- Data skew in Hive/MapReduce
NICE TO KNOW — Differentiators (15%)
- HDFS erasure coding (Hadoop 3 — replaces 3x replication)
- Hadoop 3 features (3x→EC, YARN Timeline v2, OpportunisticContainers)
- Cloudera CDP vs HDP
- Oozie coordinator jobs (time + data triggers)
- Flume channel types (memory vs file)
- LLAP (Live Long And Process) — Hive sub-second queries
- Hadoop to Databricks migration strategy (Lakebridge tool 2025)
- Apache Pig — when still relevant
APPROACH (Same as Databricks + Snowflake Prep)
🧠 INTERVIEW TIP → How to answer confidently with 10-year framing
WHAT IS IT?→Simple 2-3 line English explanation
WHY NEED IT?→Problem it solves (with travel/Amadeus example)
HOW IT WORKS?→Internals + diagrams + commands with comments
WHEN TO USE?→Decision guide
INTERVIEW TIPHow to answer confidently with 10-year framing
MEMORY MAPMnemonic to never forget
ALL 3 LEVELS:→Direct Q (one-liner) + Mid-level (how/why) + Scenario (design)
Key framing for 10-year experience:
Senior engineers are expected to explain WHY, not just WHAT. Don't just say "NameNode stores metadata" — say "NameNode is the single point of failure in Hadoop 1, which is why Hadoop 2 introduced Active/Standby HA with JournalNodes and ZooKeeper, and Hadoop 3 added HDFS Federation for horizontal scaling of the namespace."
HADOOP ECOSYSTEM OVERVIEW
📐 Architecture Diagram
┌─────────────────────────────────────────────────────────────────┐ │ HADOOP ECOSYSTEM │ ├─────────────────────────────────────────────────────────────────┤ │ │ │ INGESTION: │ │ Sqoop → Import from RDBMS (Oracle, MySQL) into HDFS/Hive │ │ Flume → Stream logs from web servers into HDFS │ │ Kafka → Real-time event streaming (feeds into HDFS/HBase) │ │ │ │ STORAGE: │ │ HDFS → Distributed file system (the core storage) │ │ HBase → NoSQL column store on top of HDFS (row-level access) │ │ │ │ PROCESSING: │ │ MapReduce → Batch processing (Java, old way) │ │ Hive → SQL on HDFS (translated to MapReduce or Tez) │ │ Pig → Scripting language for data flows (Pig Latin) │ │ Spark → In-memory fast processing (replaces MapReduce) │ │ Impala → Low-latency SQL (Cloudera, no MapReduce) │ │ │ │ RESOURCE MANAGEMENT: │ │ YARN → Cluster resource manager (since Hadoop 2) │ │ │ │ COORDINATION: │ │ ZooKeeper → Distributed coordination (NameNode HA, HBase) │ │ │ │ WORKFLOW: │ │ Oozie → Job scheduler/workflow (chains MapReduce/Hive/Pig) │ │ │ │ SECURITY: │ │ Kerberos → Authentication (who are you?) │ │ Ranger → Authorization (what can you do?) │ │ Knox → Gateway (API proxy, SSL termination) │ │ │ │ METADATA: │ │ Hive Metastore → Table schema + HDFS location mapping │ │ Atlas → Data lineage + governance (Cloudera/HDP) │ │ │ └─────────────────────────────────────────────────────────────────┘
HADOOP vs SPARK vs CLOUD — The Big Picture
HADOOP (2006-2018): The original big data platform
✓ Batch processing at scale (terabytes to petabytes)
✓ Fault-tolerant distributed storage (HDFS)
✗ Slow (disk-based MapReduce)
✗ Complex (Java MapReduce code)
✗ No real-time processing
SPARK (2014-present): The upgrade
✓ In-memory processing (10-100x faster than MapReduce)
✓ Python/SQL API (much simpler)
✓ Streaming + batch in one framework
✓ Still uses HDFS for storage (or cloud storage)
✗ Still requires cluster management
CLOUD LAKEHOUSE (2020-present): The future
✓ No cluster management (fully managed)
✓ Infinite scale (pay per use)
✓ Delta Lake / Iceberg (ACID on data lake)
✓ Unified batch + streaming + ML
Most companies: Hadoop→Spark → Cloud Lakehouse
YOUR POSITION AS 10-YEAR ENGINEER:
"I've worked with Hadoop for years — I understand why it was groundbreaking.
I also understand its limitations and have modernized pipelines from
Hive/MapReduce to Spark and now to Databricks/cloud lakehouses."
SOURCES USED
- DataCamp — Top 24 Hadoop Interview Questions 2026
- Simplilearn — Top 80 Hadoop Interview Questions 2026
- ProjectPro — Top 100 Hadoop Questions 2025
- Edureka — Top 50 Hadoop Questions
- SparkCodeHub — Hive Performance Tuning
- Datafold — Hadoop to Databricks Migration
- Edureka — Hive Interview Questions 2025