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HADOOP DISTRIBUTED COMPUTING MASTERY

In 2003 and 2004, Google published two whitepapers that changed the world: The Google File System (GFS) and MapReduce. They solved a problem no one else could: how to process petabytes of data using cheap, unreliable hardware. Doug Cutting and Mike Cafarella saw these papers and created Hadoop (named after a toy elephant).

Learn Hadoop Ecosystem: From Zero to Distributed Systems Master

Goal: Deeply understand the fundamentals of distributed storage and batch processing. By building and managing Hadoop clusters from scratch and writing raw MapReduce jobs, you will internalize how massive datasets are partitioned, replicated, and processed across commodity hardware, mastering the “Data Locality” principle that defines modern big data architecture.

Why Hadoop Matters

In 2003 and 2004, Google published two whitepapers that changed the world: The Google File System (GFS) and MapReduce. They solved a problem no one else could: how to process petabytes of data using cheap, “unreliable” hardware. Doug Cutting and Mike Cafarella saw these papers and created Hadoop (named after a toy elephant).

Hadoop is the “Grandfather” of Big Data. While Spark and Flink are faster today, they all stand on the shoulders of Hadoop’s two core breakthroughs:

  1. HDFS (Hadoop Distributed File System): Stop trying to build a reliable supercomputer; build a reliable file system out of thousands of unreliable computers.
  2. MapReduce: Move the code to the data, not the data to the code.

Understanding Hadoop is understanding the “why” behind every modern data lake, snowflake, and cloud-native database.

Core Concept Analysis

1. The HDFS Architecture (Distributed Storage)

HDFS follows a Master/Slave architecture. It breaks files into large “Blocks” (usually 128MB) and spreads them across a cluster.

      Client Write
          |
    [ NameNode ] (Metadata Master)
    /     |     \  "Where is file.txt?" -> "Blocks 1, 2, 3 on Nodes A, B, C"
   /      |      \
[DataNode] [DataNode] [DataNode]
 (Node A)   (Node B)   (Node C)
    |          |          |
 [Block 1]  [Block 2]  [Block 1] (Replica)
 [Block 3]  [Block 1]  [Block 2] (Replica)

Key Pillars:

  • Replication: Every block is stored on multiple nodes (default 3). If a node dies, the data survives.
  • Heartbeats: DataNodes constantly tell the NameNode “I am alive.”
  • Metadata: The NameNode keeps the “map” in RAM. If the NameNode dies without HA, the cluster is dead.

2. The MapReduce Paradigm (Distributed Compute)

MapReduce is a functional programming model scaled to thousands of machines.

Input Data -> [ SPLIT ] -> [ MAP ] -> [ SHUFFLE & SORT ] -> [ REDUCE ] -> Output
  • Map Phase: Filters and transforms data. Input is (Key, Value), Output is (Intermediate Key, Intermediate Value).
  • Shuffle & Sort: The “Magic” of Hadoop. It ensures all values for the SAME key end up on the SAME Reducer.
  • Reduce Phase: Aggregates the results.

3. YARN (Yet Another Resource Negotiator)

In Hadoop 1.0, MapReduce did everything. In Hadoop 2.0+, YARN was introduced to separate resource management from the compute framework.

       [ Resource Manager ] (Cluster Level)
      /         |          \
[Node Manager] [Node Manager] [Node Manager]
(Per Node)     (Per Node)     (Per Node)
     |              |              |
 [Container]    [Container]    [Container]
 (Running Task) (Running Task) (Running Task)

Concept Summary Table

Concept Cluster What You Need to Internalize
Data Locality Bandwidth is expensive; CPU is cheap. Move the JAR file to the node where the data block lives.
Fault Tolerance Hardware will fail. The software must detect failure and re-replicate/re-run tasks automatically.
HDFS Blocks Files aren’t continuous; they are chunks. Large blocks minimize disk seek time.
Shuffle & Sort This is the most expensive part of a job. It involves network I/O and disk I/O.
The “Write Once” Rule HDFS is optimized for high-throughput streaming reads, not random writes/updates.

Deep Dive Reading by Concept

Foundation: The Original Papers

Concept Resource
GFS (HDFS Origin) “The Google File System” by Ghemawat et al. (Google Research)
MapReduce Origin “MapReduce: Simplified Data Processing on Large Clusters” by Dean & Ghemawat

Technical Mastery

Concept Book & Chapter
HDFS Internals Hadoop: The Definitive Guide by Tom White — Ch. 3: “The Hadoop Distributed Filesystem”
MapReduce Workflow Hadoop: The Definitive Guide by Tom White — Ch. 2: “MapReduce”
YARN Architecture Hadoop: The Definitive Guide by Tom White — Ch. 4: “Hadoop I/O” & Ch. 4: “YARN”

Essential Reading Order

  1. The Architecture (Day 1):
    • Hadoop: The Definitive Guide Ch. 1 & 3.
  2. The Logic (Day 2):
    • MapReduce Whitepaper (Intro and Programming Model sections).

Project List

Projects are ordered from fundamental setup to complex distributed algorithms.

Project 1: The “Pseudo-Distributed” Foundation

  • File: HADOOP_DISTRIBUTED_COMPUTING_MASTERY.md
  • Main Programming Language: Bash / Configuration
  • Alternative Programming Languages: N/A
  • Coolness Level: Level 1: Pure Corporate Snoozefest
  • Business Potential: 1. The “Resume Gold”
  • Difficulty: Level 1: Beginner
  • Knowledge Area: System Administration / Linux
  • Software or Tool: Hadoop (Apache Distribution), OpenJDK, SSH
  • Main Book: “Hadoop: The Definitive Guide” by Tom White

What you’ll build: A single-machine Hadoop “cluster” where every Hadoop daemon (NameNode, DataNode, ResourceManager, NodeManager) runs as a separate Java process.

Why it teaches Hadoop: You cannot understand Hadoop without wrestling with its XML configuration files (core-site.xml, hdfs-site.xml). This project forces you to configure SSH keys for localhost, format the NameNode, and understand how the HDFS filesystem is layered on top of your local ext4/APFS filesystem.

Core challenges you’ll face:

  • SSH Key Passphrases → Hadoop needs passwordless SSH to start daemons; you’ll learn why.
  • Java Home Mismatch → Mapping Hadoop’s environment to your OS’s JDK location.
  • XML Schema → Navigating the verbose configuration style of the mid-2000s.

Key Concepts:

  • HDFS Daemons: “Hadoop: The Definitive Guide” Ch. 3 - Tom White
  • XML Configuration: Official Apache Hadoop Documentation (Single Node Setup)

Difficulty: Beginner Time estimate: 3-4 hours Prerequisites: Basic Linux CLI, SSH knowledge.

Real World Outcome

You will have a running Hadoop instance. When you run jps (Java Process Status), you will see the full suite of Hadoop machinery running on your laptop.

Example Output:

$ jps
12345 NameNode
12389 DataNode
12450 SecondaryNameNode
12678 ResourceManager
12789 NodeManager
13001 Jps

$ hdfs dfs -mkdir /user
$ hdfs dfs -ls /
Found 1 items
drwxr-xr-x   - douglas supergroup          0 2025-12-28 10:00 /user

The Core Question You’re Answering

“How can a software system treat my local hard drive as if it were a network of distributed nodes?”

Before you write any code, sit with this question. Hadoop isn’t a “database” you install; it’s a set of daemons that manage files. Understanding that it uses your local disk to simulate a distributed environment is the first step to thinking in “blocks.”

Concepts You Must Understand First

Stop and research these before coding:

  1. Passwordless SSH
    • Why does the start-dfs.sh script need to SSH into its own machine?
    • How do id_rsa and authorized_keys work?
    • Reference: “The Linux Command Line” Ch. 16 - William Shotts
  2. The NameNode Format Command
    • What actually happens when you run hdfs namenode -format? (Hint: It’s not like format C:)
    • Reference: “Hadoop: The Definitive Guide” Ch. 3

Questions to Guide Your Design

Before implementing, think through these:

  1. Directory Structure
    • Where will the actual data blocks live on your host machine? (Look at hadoop.tmp.dir).
    • What happens if you run the format command twice?
  2. Memory Limits
    • How much RAM will 5 Java processes consume on your machine? How can you limit them in hadoop-env.sh?

Thinking Exercise

The Startup Trace

Before running start-all.sh, try to predict the order of operations:

  1. Does the DataNode start before the NameNode?
  2. Does the ResourceManager need HDFS to be up first?

Questions while tracing:

  • If the NameNode fails to start, can you still write files to the DataNode?
  • Why is there a “SecondaryNameNode”? Does it provide High Availability? (Spoiler: No).

The Interview Questions They’ll Ask

Prepare to answer these:

  1. “What is the difference between a NameNode and a DataNode?”
  2. “How does Hadoop achieve fault tolerance in a single-node setup?”
  3. “Explain the role of the core-site.xml file.”
  4. “What is the default block size in HDFS 3.x and why is it so large?”
  5. “If I lose my NameNode metadata, but my DataNode disks are healthy, is my data safe?”

Hints in Layers

Hint 1: Environmental Variables Start by setting JAVA_HOME and HADOOP_HOME in your .bashrc or .zshrc. Without these, nothing works.

Hint 2: The Core XML In core-site.xml, you must define the fs.defaultFS. This tells the client “When I say ‘/’, I mean the NameNode at this URI.”

Hint 3: Format is Once Only format the NameNode once. If you do it again, the NameNode gets a new ClusterID, and the old DataNodes will refuse to connect because their IDs don’t match.

Hint 4: Log Surfing When a daemon doesn’t show up in jps, check the $HADOOP_HOME/logs directory. Hadoop is very talkative in its logs.

Books That Will Help

Topic Book Chapter
Installation “Hadoop: The Definitive Guide” by Tom White Appendix A
Configuration “Hadoop: The Definitive Guide” by Tom White Ch. 10

Project 2: Raw MapReduce WordCount (The Hello World)

  • File: HADOOP_DISTRIBUTED_COMPUTING_MASTERY.md
  • Main Programming Language: Java
  • Alternative Programming Languages: Python (via Streaming)
  • Coolness Level: Level 2: Practical but Forgettable
  • Business Potential: 1. The “Resume Gold”
  • Difficulty: Level 2: Intermediate
  • Knowledge Area: Distributed Algorithms
  • Software or Tool: Hadoop MapReduce API
  • Main Book: “Hadoop: The Definitive Guide” by Tom White

What you’ll build: A Java application that counts the occurrences of every word in a large text file using the Mapper, Reducer, and Driver classes.

Why it teaches Hadoop: You’ll learn the “Shuffle and Sort” magic. You’ll see that you don’t control the flow; Hadoop calls your code when it’s ready. You’ll learn about Text, IntWritable, and why regular Java String and Integer objects aren’t used for serialization.

Core challenges you’ll face:

  • Serialization → Understanding Writable interfaces.
  • The Driver Class → Configuring a Job, setting Input/Output paths, and submitting it to YARN.
  • Classpath Hell → Compiling a JAR that includes Hadoop dependencies without bloating the file.

Key Concepts:

  • Mapper/Reducer API: “Hadoop: The Definitive Guide” Ch. 2 - Tom White
  • Serialization (Writable): “Hadoop: The Definitive Guide” Ch. 4 - Tom White

Difficulty: Intermediate Time estimate: 1 weekend Prerequisites: Project 1, Basic Java (Generics).

Real World Outcome

You’ll submit a JAR file to your cluster. You’ll watch the YARN web UI (port 8088) show the progress bars for “Map” and “Reduce”. Finally, you’ll see a part-r-00000 file in HDFS with the results.

Example Output:

$ hadoop jar wordcount.jar WordCount /input/wiki.txt /output/wiki_counts
...
2025-12-28 12:00:00 INFO mapreduce.Job:  map 0% reduce 0%
2025-12-28 12:00:45 INFO mapreduce.Job:  map 100% reduce 0%
2025-12-28 12:01:10 INFO mapreduce.Job:  map 100% reduce 100%

$ hdfs dfs -cat /output/wiki_counts/part-r-00000 | head -n 5
the     145602
and     98450
of      87321
a       76543
to      65432

The Core Question You’re Answering

“How can I count items in a file that is too large to fit in my machine’s RAM?”

MapReduce answers this by splitting the problem. Each Mapper handles a chunk. The “Shuffle” phase then groups all instances of the word “the” and sends them to the same Reducer. The Reducer just does: sum += value.

Concepts You Must Understand First

Stop and research these before coding:

  1. The Context Object
    • How does the Mapper pass data to the Reducer? (Look at context.write()).
    • Why can’t we just use a global HashMap?
    • Book Reference: “Hadoop: The Definitive Guide” Ch. 2 - Tom White
  2. The “Stateless” Nature
    • Why must the map function be independent of other map functions?
    • What happens if the machine running Mapper #4 fails?

Questions to Guide Your Design

Before implementing, think through these:

  1. Input Splits
    • How does Hadoop decide how many Mappers to start?
    • Is one Mapper per file, or one Mapper per block?
  2. Type Safety
    • Why do we have to specify types twice (Input Key/Value and Output Key/Value) in the Driver?

Thinking Exercise

The Big Sort

Imagine you have 10 machines. Each has 10% of a file.

  1. Machine A finds “apple” 5 times.
  2. Machine B finds “apple” 3 times.
  3. Machine C finds “orange” 10 times.

Questions while tracing:

  • Draw a diagram showing how these “apple” counts get to the same machine for the final addition.
  • Who does the sorting? The Mapper or the Reducer?
  • Does the Reducer start before all Mappers are finished?

The Interview Questions They’ll Ask

Prepare to answer these:

  1. “What happens between the Map and Reduce phases?”
  2. “Why does Hadoop use IntWritable instead of java.lang.Integer?”
  3. “Can a MapReduce job have zero reducers? If so, what is the output?”
  4. “What is an InputSplit?”
  5. “If a Reducer fails, does the entire job restart?”

Hints in Layers

Hint 1: The Hadoop Command Use the hadoop com.sun.tools.javac.Main WordCount.java command to compile if you don’t want to deal with Maven/Gradle yet. It automatically adds Hadoop jars to the classpath.

Hint 2: Writable Wrappers Remember that everything in Hadoop MapReduce must be Writable. If you try to use int, it will fail at compile time or runtime when Hadoop tries to serialize it.

Hint 3: Input/Output Paths The output directory must NOT exist before you run the job. Hadoop will throw an exception. This is to prevent accidental data loss.

Hint 4: Monitor the UI Keep localhost:8088 open. Seeing the ApplicationID and tracking the logs there is much better than squinting at terminal output.

Books That Will Help

Topic Book Chapter
MapReduce API “Hadoop: The Definitive Guide” by Tom White Ch. 2
Data Types “Hadoop: The Definitive Guide” by Tom White Ch. 4
Workflow “Hadoop: The Definitive Guide” by Tom White Ch. 6

Project 3: Custom Writable: Average Temperature by Station

  • File: HADOOP_DISTRIBUTED_COMPUTING_MASTERY.md
  • Main Programming Language: Java
  • Alternative Programming Languages: N/A
  • Coolness Level: Level 3: Genuinely Clever
  • Business Potential: 1. The “Resume Gold”
  • Difficulty: Level 3: Advanced
  • Knowledge Area: Data Serialization & Aggregation
  • Software or Tool: Hadoop MapReduce API, NCDC Weather Dataset
  • Main Book: “Hadoop: The Definitive Guide” by Tom White

What you’ll build: A system that processes weather data (NCDC format). You must calculate the average temperature per weather station. Since a Reducer only gets a list of values, you can’t just sum them; you need the count and the sum to compute an average. You’ll build a WeatherTuple class that implements Writable.

Why it teaches Hadoop: It teaches you how to pass complex data structures between phases. You’ll move beyond simple (Text, IntWritable) to custom objects, teaching you the overhead of serialization and the readFields / write methods required for binary transport.

Core challenges you’ll face:

  • Designing the Writable Interface → Implementing binary serialization for your custom class.
  • Handling Nulls/Malformed Data → NCDC data is notoriously messy; you’ll need robust parsing logic.
  • Aggregation Logic → Realizing that “Average” is not associative. You can’t just combine averages; you must combine sums and counts.

Key Concepts:

  • Custom Writables: “Hadoop: The Definitive Guide” Ch. 4 - Tom White
  • Data Integrity: “Hadoop: The Definitive Guide” Ch. 4 (Checksums/Compression)

Difficulty: Advanced Time estimate: 1 week Prerequisites: Project 2.

Real World Outcome

You’ll process millions of weather records. The output will show each station ID followed by its calculated average temperature for the year.

Example Output:

$ hdfs dfs -cat /output/avg_temp/part-r-00000 | head -n 3
010010-99999    12.4
010014-99999    8.9
010015-99999    15.2

The Core Question You’re Answering

“How do I pass more than one value from a Mapper to a Reducer without using a String-concatenation hack?”

Many beginners try to send “sum,count” as a String and parse it in the Reducer. This project forces you to do it the “Hadoop way” using binary objects, which is faster and safer.

Concepts You Must Understand First

Stop and research these before coding:

  1. The Writable Interface
    • What is the difference between Writable and WritableComparable?
    • How does DataInput and DataOutput work in Java?
    • Book Reference: “Hadoop: The Definitive Guide” Ch. 4 - Tom White
  2. Associative vs. Commutative Ops
    • Why is Sum easy in MapReduce but Average hard?
    • What is a “Combiner” and why can’t it calculate an average directly?

Questions to Guide Your Design

Before implementing, think through these:

  1. Serialization Order
    • Does the order of write() calls in your class matter? (Spoiler: It must match the order in readFields()).
  2. Memory Efficiency
    • Can you reuse your Writable objects inside the Mapper to avoid Garbage Collection overhead? (Look at the “Object Reuse” pattern in Hadoop).

Thinking Exercise

The Average Dilemma

If Mapper A calculates an average of 10 and Mapper B calculates an average of 20:

  • If Mapper A had 1 record and Mapper B had 9 records, is the global average 15?

Questions while tracing:

  • What pieces of information must the Mapper send to the Reducer to ensure the math is correct?
  • Can you use a Combiner for this task? If so, what does the Combiner output?

The Interview Questions They’ll Ask

Prepare to answer these:

  1. “Why would you implement a custom Writable?”
  2. “Explain the difference between Writable and Serializable in Java.”
  3. “How does the Reducer handle the values list for a specific key if the list is too big for RAM?”
  4. “What is the purpose of the RawComparator?”
  5. “When is it better to use a custom Writable instead of a standard Text object?”

Hints in Layers

Hint 1: The Class Skeleton Your WeatherTuple needs two private fields: sum (Double) and count (Long). It must have a no-arg constructor for Hadoop’s reflection.

Hint 2: readFields and write Inside write(DataOutput out), you call out.writeDouble(sum). Inside readFields(DataInput in), you MUST call sum = in.readDouble(). The sequence is the protocol.

Hint 3: The Mapper Logic The Mapper should parse the line, extract the station ID as the key, and create a WeatherTuple(temp, 1) as the value.

Hint 4: The Reducer Logic The Reducer iterates through the tuples, summing all sum fields and all count fields. The final result is totalSum / totalCount.

Books That Will Help

Topic Book Chapter
Serialization “Hadoop: The Definitive Guide” by Tom White Ch. 4
Practical MR “Hadoop: The Definitive Guide” by Tom White Ch. 8

Project 4: The Multi-Node Bare Metal Migration

  • File: HADOOP_DISTRIBUTED_COMPUTING_MASTERY.md
  • Main Programming Language: Bash / YAML (Ansible)
  • Alternative Programming Languages: N/A
  • Coolness Level: Level 4: Hardcore Tech Flex
  • Business Potential: 3. The “Service & Support” Model
  • Difficulty: Level 4: Expert
  • Knowledge Area: Infrastructure / Networking
  • Software or Tool: VirtualBox/Proxmox, Ubuntu Server, Hadoop
  • Main Book: “Hadoop: The Definitive Guide” by Tom White

What you’ll build: A 3-node Hadoop cluster using Virtual Machines or physical hardware. You’ll move away from localhost to real hostnames. You’ll have one master node (NameNode/ResourceManager) and two worker nodes (DataNode/NodeManager).

Why it teaches Hadoop: This is the “Aha!” moment. You’ll deal with networking, firewalls, and workers files. You’ll see HDFS replication actually happen across the network. You’ll understand the “Rack Awareness” configuration and why Hadoop is “Network Topology Aware.”

Core challenges you’ll face:

  • Internal Networking → Ensuring nodes can talk via hostnames, not just IPs.
  • Firewall Woes → Hadoop uses many ports (9870, 8088, 9000, 50010, etc.); you’ll need to open them.
  • Worker Management → Coordinating the start/stop of daemons across multiple machines.

Key Concepts:

  • Cluster Setup: “Hadoop: The Definitive Guide” Ch. 10 - Tom White
  • Rack Awareness: Apache Hadoop Documentation (HDFS Architecture)

Difficulty: Expert Time estimate: 1-2 weeks Prerequisites: Project 1.

Real World Outcome

You’ll run a job on the master, and see the tasks distributed to the workers. If you pull the network cable on one worker, the Job should continue and finish on the other (Fault Tolerance).

Example Output:

$ hdfs dfsadmin -report
Live datanodes (2):

Name: 192.168.1.101:9866 (worker-1)
Hostname: worker-1
Configured Capacity: 50 GB
DFS Used: 10 GB

Name: 192.168.1.102:9866 (worker-2)
Hostname: worker-2
Configured Capacity: 50 GB
DFS Used: 10 GB

The Core Question You’re Answering

“How does Hadoop manage a ‘fleet’ of machines without me logging into each one individually?”

The Master node uses the workers file and SSH to remotely execute commands on all slaves. You are moving from managing a process to managing a cluster.

Concepts You Must Understand First

Stop and research these before coding:

  1. DNS / /etc/hosts
    • Why is it dangerous to use IP addresses in Hadoop configs?
    • How do you set up a static IP for your VMs?
  2. HDFS Replication Factor
    • If you have 2 DataNodes and a replication factor of 3, what happens? (Look up “Under-replicated blocks”).
    • Book Reference: “Hadoop: The Definitive Guide” Ch. 3 - Tom White

Questions to Guide Your Design

Before implementing, think through these:

  1. Hardware Heterogeneity
    • What if worker-1 has 8GB RAM and worker-2 has 2GB? How do you tell YARN to respect these limits?
  2. Security
    • How can you prevent a random machine on your network from connecting to your NameNode and deleting data?

Thinking Exercise

The Split-Brain Scenario

Imagine your Master and Worker are separated by a network failure.

  • The Master thinks the Worker is dead (Heartbeat timeout).
  • The Worker thinks it’s still healthy but can’t talk to the Master.

Questions while tracing:

  • Who decides what data is “missing”?
  • What happens when the network comes back? Does the Master delete the “extra” replicas?

The Interview Questions They’ll Ask

Prepare to answer these:

  1. “What is Rack Awareness and why is it important?”
  2. “How do you add a new DataNode to a running cluster?”
  3. “What happens if the NameNode runs out of disk space for its EditLog?”
  4. “Describe the heartbeat mechanism between DataNodes and NameNode.”
  5. “Explain how Hadoop handles a slow machine (speculative execution).”

Project 5: Distributed Grep & Log Analyzer

  • File: HADOOP_DISTRIBUTED_COMPUTING_MASTERY.md
  • Main Programming Language: Java
  • Alternative Programming Languages: N/A
  • Coolness Level: Level 2: Practical but Forgettable
  • Business Potential: 3. The “Service & Support” Model
  • Difficulty: Level 2: Intermediate
  • Knowledge Area: Log Processing / Text Analysis
  • Software or Tool: Hadoop MapReduce, Apache/Nginx Logs
  • Main Book: “Hadoop: The Definitive Guide” by Tom White

What you’ll build: A tool that searches for regex patterns across terabytes of server logs. It must output the line, the filename, and the line number where the match occurred.

Why it teaches Hadoop: This project focuses on the “Map-Only” job. You don’t need a Reducer for a search. You’ll learn how to optimize jobs by disabling the Shuffle/Sort phase entirely (job.setNumReduceTasks(0)), which is a massive performance boost for simple filtering.

Core challenges you’ll face:

  • Accessing Metadata → Getting the filename of the split being processed in the Mapper.
  • Regex Performance → Efficiently matching patterns in a high-throughput stream.
  • Large Input Handling → Processing millions of small files (and learning why that’s bad for Hadoop).

Key Concepts:

  • Map-Only Jobs: “Hadoop: The Definitive Guide” Ch. 8 - Tom White
  • The Small Files Problem: “Hadoop: The Definitive Guide” Ch. 13

Difficulty: Intermediate Time estimate: 3 days Prerequisites: Project 2.

Real World Outcome

You’ll search for “404” errors across 100GB of logs. The output will be a single directory in HDFS containing exactly the lines that matched, with zero shuffle time.

Example Output:

$ hdfs dfs -cat /output/grep_results/part-m-00000 | head -n 2
[access.log:452] 192.168.1.1 - - [28/Dec/2025:10:00:01] "GET /broken_link HTTP/1.1" 404
[access.log:891] 10.0.0.5 - - [28/Dec/2025:10:05:22] "GET /admin/login HTTP/1.1" 404

The Core Question You’re Answering

“Why would I ever want to skip the Reduce phase?”

Many beginners think every MapReduce job needs both. This project teaches you that sometimes the most efficient way to use a cluster is as a giant parallel filter.

Concepts You Must Understand First

Stop and research these before coding:

  1. InputSplit and FileSplit
    • How can you cast the context.getInputSplit() to a FileSplit?
    • What metadata does FileSplit provide?
  2. The “Small Files” Problem
    • Why is HDFS slow if you have 1 million 1KB files instead of one 1GB file?

Questions to Guide Your Design

Before implementing, think through these:

  1. Parameters
    • How do you pass the Regex string from your terminal to the Mappers running on different machines? (Hint: Configuration.set()).
  2. Output Format
    • If you have 500 Mappers, you’ll have 500 output files. How do you merge them?

Thinking Exercise

The Log Hunt

You have 10,000 log files.

  1. If you run a standard grep on one machine, it takes 2 hours.
  2. If you run it on Hadoop with 10 nodes, it takes 15 minutes.

Questions while tracing:

  • Where does the output go? Is it stored on the local disk of each node or back into HDFS?
  • If the output is small, why not just print it to stdout?

The Interview Questions They’ll Ask

Prepare to answer these:

  1. “How do you handle the small files problem in Hadoop?”
  2. “How do you pass a variable to all mappers in a job?”
  3. “What happens when numReduceTasks is set to 0?”
  4. “What is a SequenceFile and why is it useful for logs?”
  5. “Can you explain how CombineFileInputFormat works?”

Hints in Layers

Hint 1: The Context Metadata In your map method, use ((FileSplit) context.getInputSplit()).getPath().getName() to get the filename.

Hint 2: Configuration Sharing In the Driver: conf.set("grep.pattern", regex). In the Mapper’s setup method: this.pattern = conf.get("grep.pattern").

Hint 3: Zero Reducers Ensure you call job.setNumReduceTasks(0) in your Driver. If you don’t, Hadoop will still run a shuffle phase for no reason.

Hint 4: Output Naming Notice the output files are named part-m-XXXXX instead of part-r-XXXXX. The ‘m’ stands for Map-only.

Books That Will Help

Topic Book Chapter
Map-Only Jobs “Hadoop: The Definitive Guide” by Tom White Ch. 8
Config Management “Hadoop: The Definitive Guide” by Tom White Ch. 6

Project 6: Joins in MapReduce (Reduce-side Join)

  • File: HADOOP_DISTRIBUTED_COMPUTING_MASTERY.md
  • Main Programming Language: Java
  • Alternative Programming Languages: N/A
  • Coolness Level: Level 3: Genuinely Clever
  • Business Potential: 4. The “Open Core” Infrastructure
  • Difficulty: Level 3: Advanced
  • Knowledge Area: Relational Data / Distributed Joins
  • Software or Tool: Hadoop MapReduce
  • Main Book: “Hadoop: The Definitive Guide” by Tom White

What you’ll build: A system that joins two disparate datasets: a “Users” CSV (ID, Name, Email) and an “Orders” CSV (OrderID, UserID, Amount). The output should be a joined record: (Name, Amount).

Why it teaches Hadoop: Joins are the hardest part of distributed computing. You’ll learn how to “tag” data in the Mapper so the Reducer knows which dataset a record came from. You’ll learn the trade-offs of the “Reduce-side Join” (which works for any size data but is slow because of the shuffle).

Core challenges you’ll face:

  • Data Tagging → Designing a value wrapper that indicates “Source A” or “Source B”.
  • Shuffle Management → Handling the case where one User has 1 million Orders (Data Skew).
  • Secondary Keys → Ensuring the “User” record arrives at the Reducer before the “Order” records.

Key Concepts:

  • Reduce-side Joins: “Hadoop: The Definitive Guide” Ch. 9 - Tom White
  • Data Skew: “Hadoop: The Definitive Guide” Ch. 9

Difficulty: Advanced Time estimate: 1 week Prerequisites: Project 3.

Real World Outcome

You’ll take two separate files and produce a report that merges them based on a common key, simulating a SQL JOIN without using a database.

Example Output:

$ hdfs dfs -cat /output/joined_data/part-r-00000 | head -n 3
Alice   50.00
Alice   120.50
Bob     15.00

The Core Question You’re Answering

“If my data is split across 100 machines, how can I find the ‘User’ information for an ‘Order’ that lives on a different node?”

The answer is the Shuffle. By emitting the UserID as the key for both datasets, Hadoop’s shuffle ensures that all data for UserID: 123 arrives at the same Reducer.

Concepts You Must Understand First

Stop and research these before coding:

  1. Inner vs. Left Outer Join
    • How do you implement a Left Join if the “User” record is missing in the Reducer?
  2. The “Bloom Filter”
    • How could a Bloom Filter speed up a join by filtering out keys that definitely don’t exist in the other dataset?
    • Reference: “Hadoop: The Definitive Guide” Ch. 9 - Tom White

Questions to Guide Your Design

Before implementing, think through these:

  1. Memory Management
    • Should you store all orders in a List in the Reducer’s memory? What if a user has 10 million orders?
  2. The Tagging Strategy
    • How will you distinguish a User record from an Order record in the reduce(key, values) iterable?

Thinking Exercise

The Join Shuffle

You have a 1TB Users file and a 10TB Orders file.

  1. Mapper A reads Users.
  2. Mapper B reads Orders.

Questions while tracing:

  • If you use the UserID as the key, how much data will travel over the network?
  • Is there a way to do this without moving the 10TB of data? (Hint: Look up Map-side joins).

The Interview Questions They’ll Ask

Prepare to answer these:

  1. “Explain the difference between a Map-side join and a Reduce-side join.”
  2. “How do you handle data skew in a distributed join?”
  3. “What is a ‘Distributed Cache’ and how is it used in joins?”
  4. “Why is the Shuffle phase the bottleneck in a Reduce-side join?”
  5. “Can you join three or more datasets in a single MapReduce job?”

Project 7: Secondary Sort (The “Value-to-Key” Pattern)

  • File: HADOOP_DISTRIBUTED_COMPUTING_MASTERY.md
  • Main Programming Language: Java
  • Alternative Programming Languages: N/A
  • Coolness Level: Level 4: Hardcore Tech Flex
  • Business Potential: 1. The “Resume Gold”
  • Difficulty: Level 4: Expert
  • Knowledge Area: Sort Algorithms / Partitioner Logic
  • Software or Tool: Hadoop MapReduce
  • Main Book: “Hadoop: The Definitive Guide” by Tom White

What you’ll build: A job that processes financial transactions. The output must be sorted by CustomerID (the key) AND by Timestamp (the value).

Why it teaches Hadoop: By default, Hadoop only sorts by Key. To sort by Value, you must use the “Value-to-Key” pattern: you move the timestamp into the Key (creating a composite key), then write a custom Partitioner and GroupingComparator to ensure Hadoop still treats records with the same CustomerID as part of the same group.

Core challenges you’ll face:

  • Composite Key Design → Creating a WritableComparable that holds two fields.
  • Custom Partitioning → Ensuring the composite key (ID, Time) still hashes only on ID.
  • Grouping Logic → Telling the Reducer that (ID, Time1) and (ID, Time2) belong to the same call.

Key Concepts:

  • Secondary Sort: “Hadoop: The Definitive Guide” Ch. 6 - Tom White
  • Partitioner Interface: “Hadoop: The Definitive Guide” Ch. 6

Difficulty: Expert Time estimate: 1 week Prerequisites: Project 6.

Real World Outcome

You’ll see a perfectly ordered stream of transactions for every user, allowing you to calculate running balances or session intervals without sorting in memory.

Example Output:

$ hdfs dfs -cat /output/sorted_txns/part-r-00000 | head -n 3
Cust-001, 2025-12-01 10:00:00, $5.00
Cust-001, 2025-12-01 10:05:00, $12.00
Cust-002, 2025-11-30 09:00:00, $2.50
  • File: HADOOP_DISTRIBUTED_COMPUTING_MASTERY.md
  • Main Programming Language: Java
  • Alternative Programming Languages: N/A
  • Coolness Level: Level 5: Pure Magic
  • Business Potential: 5. The “Industry Disruptor”
  • Difficulty: Level 3: Advanced
  • Knowledge Area: Information Retrieval
  • Software or Tool: Hadoop MapReduce
  • Main Book: “Hadoop: The Definitive Guide” by Tom White

What you’ll build: A search index for a collection of text documents. For every word, you will produce a list of document IDs and the positions where that word appears.

Why it teaches Hadoop: This is the foundation of search engines. It teaches you how to handle “Many-to-Many” relationships. You’ll learn how to use the MultipleOutputs class to save results into different directories based on the first letter of the word.

Core challenges you’ll face:

  • Large Value Strings → Building long lists of Document IDs in the Reducer.
  • Efficient Storage → Using compression or bit-packing for IDs.
  • Tokenization → Handling punctuation, case-insensitivity, and “stop words.”

Difficulty: Advanced Time estimate: 1 week Prerequisites: Project 2.

Project 9: Distributed Graph Processing (PageRank)

  • File: HADOOP_DISTRIBUTED_COMPUTING_MASTERY.md
  • Main Programming Language: Java
  • Alternative Programming Languages: N/A
  • Coolness Level: Level 5: Pure Magic
  • Business Potential: 5. The “Industry Disruptor”
  • Difficulty: Level 5: Master
  • Knowledge Area: Graph Theory / Iterative Algorithms
  • Software or Tool: Hadoop MapReduce
  • Main Book: “Hadoop: The Definitive Guide” by Tom White

What you’ll build: An implementation of the original PageRank algorithm. You’ll process a graph of web pages (URLs and their outgoing links) and iteratively calculate the importance of each page.

Why it teaches Hadoop: PageRank is iterative. MapReduce is inherently a one-shot batch process. You’ll learn how to “chain” jobs where the output of Job N becomes the input of Job N+1. You’ll also learn about the “dangling node” problem and how to handle global state in a distributed system.

Core challenges you’ll face:

  • Job Chaining → Writing a Driver that runs the MR job in a while loop until the PageRank values converge.
  • Graph Representation → Representing nodes and adjacency lists in a Writable object.
  • Convergence Check → How do you know when to stop? (Look at Hadoop Counters).

Key Concepts:

  • Iterative MapReduce: “Hadoop: The Definitive Guide” Ch. 16 - Tom White
  • Hadoop Counters: “Hadoop: The Definitive Guide” Ch. 9 - Tom White

Difficulty: Master Time estimate: 2 weeks Prerequisites: Project 8.

Real World Outcome

You’ll take a small “web crawl” dataset and produce a list of URLs sorted by importance. You’ll see “google.com” rise to the top as you increase the number of iterations.

Example Output:

Iteration 5:
google.com      0.452
wikipedia.org   0.312
example.com     0.012

The Core Question You’re Answering

“How do I perform algorithms that require multiple passes over the same data in a system designed for single passes?”

The answer is iterative orchestration. This project shows you the limits of MapReduce (and why Spark was eventually created to handle iterative data in-memory).

Concepts You Must Understand First

Stop and research these before coding:

  1. The PageRank Formula
    • What is the “dampening factor”?
    • Why do we divide a page’s rank by its number of outbound links?
  2. Dangling Nodes
    • What happens if a page has no outbound links? Where does its rank go?

Questions to Guide Your Design

Before implementing, think through these:

  1. Data Format
    • Should you output the adjacency list at every iteration? (Hint: Yes, otherwise the next iteration won’t know where the links go).
  2. Counters
    • Can you use a Global Counter to track the total “Delta” change in PageRank to decide if you’ve converged?

Thinking Exercise

The Iteration Tax

Every time you start a new MapReduce job:

  1. JVMs must start.
  2. Data must be read from HDFS.
  3. Data must be written to HDFS.

Questions while tracing:

  • If PageRank takes 50 iterations, how much time is spent on I/O vs. actual math?
  • Why is this “stop-start” approach inefficient?

The Interview Questions They’ll Ask

Prepare to answer these:

  1. “How do you implement iterative algorithms in MapReduce?”
  2. “Explain the role of Hadoop Counters.”
  3. “What are the limitations of MapReduce for graph processing?”
  4. “How do you handle ‘Sink’ nodes in PageRank?”
  5. “If a node has 1 million outbound links, how does that affect the Reducer’s memory?”

Hints in Layers

Hint 1: The Node Class Create a Node class that holds double pageRank and List<String> adjacentNodes. This will be your value in the MapReduce job.

Hint 2: The Map Phase The Mapper emits the current PageRank divided by the link count to each adjacent node. It ALSO emits the adjacency list back to the same node ID so it’s not lost.

Hint 3: The Reduce Phase The Reducer sums the incoming rank values and applies the formula (1-d) + d * sum. It then re-attaches the adjacency list for the next iteration.

Hint 4: The Loop In your main method, use a loop. After each job.waitForCompletion(true), read the global counter. If the change is below 0.001, stop.

Books That Will Help

Topic Book Chapter
Iterative Algorithms “Hadoop: The Definitive Guide” by Tom White Ch. 16
Global State “Hadoop: The Definitive Guide” by Tom White Ch. 9

Project 10: Implementing a mini-HDFS client in Python/C

  • File: HADOOP_DISTRIBUTED_COMPUTING_MASTERY.md
  • Main Programming Language: Python
  • Alternative Programming Languages: C
  • Coolness Level: Level 4: Hardcore Tech Flex
  • Business Potential: 2. The “Micro-SaaS / Pro Tool”
  • Difficulty: Level 3: Advanced
  • Knowledge Area: Network Protocols / REST APIs
  • Software or Tool: WebHDFS REST API
  • Main Book: “Hadoop: The Definitive Guide” by Tom White

What you’ll build: A CLI tool that can upload, download, and list files in HDFS without having Hadoop installed locally. You’ll use the WebHDFS REST API to talk directly to the NameNode and DataNodes.

Why it teaches Hadoop: It forces you to understand the “Two-Step Redirect” of HDFS. When you ask the NameNode for a file, it doesn’t give you the file; it gives you a 307 Temporary Redirect to the DataNode that actually has the blocks.

Core challenges you’ll face:

  • Handling Redirects → Following the 307 response correctly in your HTTP client.
  • Authentication → Dealing with Hadoop’s user-impersonation (the user.name query parameter).
  • Streaming → Efficiently piping large files through HTTP without loading the whole file into memory.

Key Concepts:

  • WebHDFS: Apache Hadoop Documentation (WebHDFS REST API)
  • HDFS Client Protocol: “Hadoop: The Definitive Guide” Ch. 3 - Tom White

Difficulty: Advanced Time estimate: 1 week Prerequisites: Project 1.

Real World Outcome

You’ll have a lightweight script that can interact with your cluster from any machine on the network.

Example Output:

$ ./my_hdfs_client.py ls /user/douglas
[FILE] data.txt (128 MB)
[DIR]  logs/

$ ./my_hdfs_client.py cat /user/douglas/data.txt
Hello from HDFS!

The Core Question You’re Answering

“Why doesn’t the NameNode just serve the file content directly?”

Before you write any code, sit with this question. If the NameNode (the master) served all the data, it would become a bottleneck. By redirecting the client to a DataNode, the NameNode stays free to handle thousands of requests per second.

Concepts You Must Understand First

Stop and research these before coding:

  1. REST and HTTP Redirects
    • What is a 307 Temporary Redirect?
    • How does a client know where to go next?
  2. WebHDFS Ports
    • Which port does the NameNode listen on for REST? (Hint: 9870). Which port does the DataNode use? (Hint: 9864).

Questions to Guide Your Design

Before implementing, think through these:

  1. The ‘PUT’ Request
    • Uploading a file requires two requests. Request 1 asks the NameNode “Where can I put this?” Request 2 sends the data to the DataNode. How will you handle the handoff?
  2. Security
    • If you don’t provide a user.name, what user does Hadoop think you are? (Look up “Hadoop Simple Authentication”).

Thinking Exercise

The Data Bypass

You are downloading a 10GB file.

  1. You talk to the NameNode (Master).
  2. It gives you a URL for DataNode A.
  3. You download from DataNode A.

Questions while tracing:

  • Does the 10GB of data ever pass through the NameNode?
  • Why is this architecture “horizontally scalable”?

The Interview Questions They’ll Ask

Prepare to answer these:

  1. “How does the HDFS write pipeline work?”
  2. “Explain the two-step redirection in WebHDFS.”
  3. “What are the advantages of WebHDFS over the native RPC protocol?”
  4. “How do you handle authentication in WebHDFS?”
  5. “Can you perform block-level operations via WebHDFS?”

Hints in Layers

Hint 1: The Request Library In Python, use the requests library. It handles redirects automatically by default, but for WebHDFS, you might want to inspect the redirect URL first.

Hint 2: The List Operation Start with the LISTSTATUS operation. It’s a simple GET request and doesn’t involve redirects. It will return a JSON object.

Hint 3: The Redirect URL When doing a OPEN (Read) or CREATE (Write), the NameNode returns a URL that includes the IP of a DataNode. Your client must then send a second request to THAT URL.

Hint 4: Streaming Use response.iter_content() in Python to stream the file. Never do f.read() on a 10GB file.

Project 11: HDFS Snapshot & Quota Management

  • File: HADOOP_DISTRIBUTED_COMPUTING_MASTERY.md
  • Main Programming Language: Bash
  • Alternative Programming Languages: N/A
  • Coolness Level: Level 1: Pure Corporate Snoozefest
  • Business Potential: 3. The “Service & Support” Model
  • Difficulty: Level 2: Intermediate
  • Knowledge Area: System Administration / Data Governance
  • Software or Tool: HDFS CLI, hdfs dfsadmin
  • Main Book: “Hadoop: The Definitive Guide” by Tom White

What you’ll build: A set of automation scripts that manage a shared Hadoop cluster. You must implement directory-level quotas (limiting users to 10GB each) and a nightly snapshot system for “Time Travel” recovery.

Why it teaches Hadoop: You’ll learn the administrative side of HDFS. You’ll see how Hadoop manages metadata snapshots without copying the actual data blocks (Copy-on-Write). You’ll understand the difference between “Space Quota” and “Name Quota.”

Core challenges you’ll face:

  • Quota Calculations → Understanding that “Space Quota” includes replicas (a 1GB file with replication 3 takes 3GB of quota).
  • Snapshot Recovery → Restoring a specific file from a .snapshot directory.
  • Reporting → Creating a summary of cluster usage for “billing” purposes.

Key Concepts:

  • HDFS Snapshots: Apache Hadoop Documentation (HDFS Snapshots)
  • HDFS Quotas: Apache Hadoop Documentation (HDFS Quotas)

Difficulty: Intermediate Time estimate: 3 days Prerequisites: Project 1.

Project 12: YARN Scheduler Simulation

  • File: HADOOP_DISTRIBUTED_COMPUTING_MASTERY.md
  • Main Programming Language: Java / Python
  • Alternative Programming Languages: N/A
  • Coolness Level: Level 3: Genuinely Clever
  • Business Potential: 1. The “Resume Gold”
  • Difficulty: Level 3: Advanced
  • Knowledge Area: Operating Systems / Scheduling
  • Software or Tool: YARN Configuration
  • Main Book: “Hadoop: The Definitive Guide” by Tom White

What you’ll build: A tool that simulates different cluster workloads (Batch vs. Interactive) and analyzes how the Fair Scheduler and Capacity Scheduler handle them. You’ll then configure your actual cluster to enforce these policies.

Why it teaches Hadoop: YARN is the OS of the data center. You’ll learn how to partition resources into “Queues,” set priorities, and handle “Preemption” (where YARN kills a low-priority job to make room for a high-priority one).

Key Concepts:

  • YARN Schedulers: “Hadoop: The Definitive Guide” Ch. 4 - Tom White
  • Queue Configuration: Apache Hadoop Documentation (Fair Scheduler)

Project 13: Data Serialization (Avro/Parquet Deep Dive)

  • File: HADOOP_DISTRIBUTED_COMPUTING_MASTERY.md
  • Main Programming Language: Java
  • Alternative Programming Languages: Python
  • Coolness Level: Level 4: Hardcore Tech Flex
  • Business Potential: 5. The “Industry Disruptor”
  • Difficulty: Level 3: Advanced
  • Knowledge Area: Data Engineering / Performance
  • Software or Tool: Apache Avro, Apache Parquet
  • Main Book: “Hadoop: The Definitive Guide” by Tom White

What you’ll build: A performance benchmark comparing three file formats: Plain Text (CSV), Avro (Row-based binary), and Parquet (Column-based binary). You’ll write MapReduce jobs to read from each and measure time and storage size.

Why it teaches Hadoop: Modern Hadoop rarely uses CSV. This project teaches you why Parquet is the king of Big Data (it allows “Predicate Pushdown” and “Column Projection”). You’ll understand how schema-evolution works in Avro.

Project 14: High Availability with Zookeeper

  • File: HADOOP_DISTRIBUTED_COMPUTING_MASTERY.md
  • Main Programming Language: Bash / Configuration
  • Alternative Programming Languages: N/A
  • Coolness Level: Level 5: Pure Magic
  • Business Potential: 3. The “Service & Support” Model
  • Difficulty: Level 5: Master
  • Knowledge Area: Distributed Consensus
  • Software or Tool: Apache Zookeeper, Hadoop HA
  • Main Book: “Hadoop: The Definitive Guide” by Tom White

What you’ll build: An HA (High Availability) cluster with two NameNodes: one Active and one Standby. You’ll use Apache Zookeeper to perform automatic failover.

Why it teaches Hadoop: You’ll eliminate the “Single Point of Failure” (SPOF). You’ll learn about “Fencing” (preventing two NameNodes from thinking they are both active) and the “Quorum Journal Manager” (how metadata is kept in sync).

Project Comparison Table

Project Difficulty Time Depth of Understanding Fun Factor
1. Pseudo-Cluster Level 1 4h Fundamental Config ⭐⭐
2. WordCount Level 2 1d Shuffle & Sort Logic ⭐⭐⭐
3. Custom Writable Level 3 1w Binary Serialization ⭐⭐⭐
4. Bare Metal Level 4 1w Networking & Cluster Ops ⭐⭐⭐⭐
6. Joins Level 3 1w Distributed Data Joins ⭐⭐⭐⭐
7. Secondary Sort Level 4 1w Advanced Shuffle Control ⭐⭐⭐
9. PageRank Level 5 2w Iterative Computing ⭐⭐⭐⭐⭐
10. Mini-Client Level 3 1w Protocol Internals ⭐⭐⭐⭐
14. High Availability Level 5 2w Distributed Consensus ⭐⭐⭐⭐⭐

Recommendation

If you are a beginner: Start with Project 1 and Project 2. This gets you comfortable with the environment and the programming model. If you are an infrastructure person: Focus on Project 4 and Project 14. Building the cluster is your primary goal. If you are a data engineer: Prioritize Project 3, Project 6, and Project 13. These focus on data movement and optimization.

Final Overall Project: The “Big Search” Architecture

Goal: Apply everything to build a complete distributed search engine pipeline.

The Workflow:

  1. Ingest: Use your Mini-Client (Project 10) to upload raw text dumps to HDFS.
  2. Clean: Run a Log Analyzer (Project 5) type job to remove HTML tags and “stop words.”
  3. Index: Run the Inverted Index (Project 8) job to generate the searchable index.
  4. Rank: Use the PageRank (Project 9) job to determine which documents are the most important.
  5. Optimize: Convert the final index into Parquet (Project 13) for fast querying.
  6. Serve: Write a small web UI that queries the Parquet index and returns results ranked by the PageRank importance.

Summary

This learning path covers the Hadoop Ecosystem through 14 hands-on projects. Here’s the complete list:

# Project Name Main Language Difficulty Time Estimate
1 Pseudo-Distributed Foundation Bash/XML Beginner 4 hours
2 Raw MapReduce WordCount Java Intermediate 1 weekend
3 Custom Writable (Weather) Java Advanced 1 week
4 Multi-Node Bare Metal Bash/Ansible Expert 1-2 weeks
5 Distributed Grep Java Intermediate 3 days
6 Reduce-side Joins Java Advanced 1 week
7 Secondary Sort Java Expert 1 week
8 Inverted Index for Search Java Advanced 1 week
9 Distributed PageRank Java Master 2 weeks
10 Mini-HDFS Client Python Advanced 1 week
11 Snapshot & Quota Admin Bash Intermediate 3 days
12 YARN Scheduler Sim Java Advanced 1 week
13 Avro vs Parquet Benchmark Java Advanced 1 week
14 High Availability (ZK) Bash/Config Master 2 weeks

Expected Outcomes

After completing these projects, you will:

  • Internalize the Data Locality principle (moving compute to storage).
  • Master the Map-Shuffle-Sort-Reduce pipeline at a low level.
  • Be able to configure, deploy, and manage a multi-node distributed cluster.
  • Understand binary serialization and the trade-offs of different Big Data file formats.
  • Be prepared for distributed systems interview questions concerning fault tolerance, data skew, and distributed consensus.

You’ll have built 14 working projects that demonstrate deep understanding of Hadoop from first principles.