Hadoop Tutorials

Apache Hadoop Tutorials Roadmap

Section 1: Introduction to Big Data and Hadoop Fundamentals

  • Understanding Big Data:
    • Definition of Big Data (Volume, Velocity, Variety, Veracity).
    • Challenges of processing and storing Big Data with traditional systems.
    • Use cases for Big Data (analytics, machine learning, IoT).
  • Introduction to Apache Hadoop:
    • What is Hadoop? (An open-source framework for distributed storage and processing of large datasets).
    • Hadoop's core idea (processing data where it's stored).
    • Hadoop's history and evolution.
    • Key advantages of Hadoop (Scalability, Fault Tolerance, Flexibility, Cost-Effectiveness).
  • Hadoop Ecosystem Overview:
    • Introduction to the major components of the Hadoop ecosystem (HDFS, MapReduce, YARN, Hive, Pig, Spark, etc.).
    • Understanding how these components fit together.
  • Hadoop Distributions:
    • Introduction to commercial Hadoop distributions (Cloudera, Hortonworks - now Cloudera, MapR - now HPE).
    • Understanding the differences and benefits of using a distribution.
    • Introduction to Apache Hadoop (vanilla) installation.

Section 2: Hadoop Distributed File System (HDFS)

  • HDFS Architecture:
    • Understanding the Master-Slave architecture (NameNode and DataNodes).
    • Role of the NameNode (metadata management).
    • Role of the DataNodes (storing actual data blocks).
    • Understanding Data Blocks and Block Size.
    • Understanding Data Replication and Fault Tolerance.
    • Secondary NameNode (Checkpointing).
  • HDFS Commands:
    • Using the hdfs dfs command-line utility.
    • Common commands (ls, put, get, copyFromLocal, copyToLocal, mkdir, rm, cat, du, count).
    • Understanding HDFS file permissions.
  • HDFS Read/Write Operations:
    • Understanding the process of reading a file from HDFS (Client interacts with NameNode and DataNodes).
    • Understanding the process of writing a file to HDFS (Client interacts with NameNode and DataNodes, data pipelining).
  • HDFS High Availability (HA):
    • Understanding the need for NameNode HA.
    • Introduction to Quorum Journal Manager (QJM) or NFS for shared edits.
    • Failover Controller.
  • HDFS Federation (Optional):
    • Understanding how to scale the NameNode horizontally.
    • Multiple NameNodes managing different parts of the HDFS namespace.

Section 3: Yet Another Resource Negotiator (YARN)

  • YARN Architecture:
    • Understanding the components (ResourceManager, NodeManager, ApplicationMaster, Containers).
    • Role of the ResourceManager (Global resource scheduler).
    • Role of the NodeManager (Per-node resource manager).
    • Role of the ApplicationMaster (Per-application coordinator).
    • Understanding Containers (Resource allocation units).
  • Resource Scheduling in YARN:
    • Introduction to different schedulers (FIFO, Capacity, Fair).
    • Understanding how YARN allocates resources to applications.
  • YARN Application Execution Flow:
    • Understanding the steps involved when an application is submitted to YARN.
  • YARN High Availability (HA) (Optional):
    • Understanding ResourceManager HA.

Section 4: MapReduce (Batch Processing Framework)

  • MapReduce Concepts:
    • Understanding the Map and Reduce phases.
    • Understanding Key-Value pairs as the data model.
    • Understanding InputFormat, Mapper, Shuffler, Sort, Reducer, OutputFormat.
  • MapReduce Architecture (within YARN):
    • How MapReduce jobs run on YARN.
    • JobHistoryServer.
  • Writing a Simple MapReduce Program (Java):
    • Setting up a development environment.
    • Writing the Mapper class.
    • Writing the Reducer class.
    • Writing the Driver class.
    • Compiling and packaging the job.
  • Running a MapReduce Job:
    • Using the hadoop jar command.
    • Monitoring jobs via the YARN ResourceManager UI.
  • Advanced MapReduce Concepts (Optional):
    • Combiners.
    • Partitioners.
    • Counters.
    • Caching (Distributed Cache).
    • Input/Output Formats.

Section 5: Introduction to Key Hadoop Ecosystem Projects

  • Apache Hive (Data Warehousing on Hadoop):
    • What is Hive? (Provides a SQL-like interface to query data stored in HDFS).
    • Hive Architecture (Metastore, Driver, Compiler, Optimizer, Executor).
    • HiveQL (Hive Query Language).
    • Creating databases and tables (Managed vs. External tables).
    • Loading data into Hive.
    • Executing Hive queries.
    • Understanding how Hive translates queries into MapReduce or Spark jobs.
  • Apache Pig (Data Flow Language):
    • What is Pig? (A platform for analyzing large datasets using a high-level language called Pig Latin).
    • Pig Architecture.
    • Pig Latin commands (LOAD, FOREACH, GROUP, FILTER, JOIN, STORE).
    • Executing Pig scripts.
    • Understanding how Pig translates scripts into MapReduce or Spark jobs.
  • Apache Spark (Fast and General-Purpose Cluster Computing):
    • What is Spark? (An in-memory processing engine, faster than traditional MapReduce).
    • Spark Architecture (Driver, Executors, Cluster Manager - YARN, Mesos, Standalone).
    • Resilient Distributed Datasets (RDDs) and DataFrames/Datasets.
    • Spark Core, Spark SQL, Spark Streaming, MLlib, GraphX.
    • Writing a simple Spark application (Scala, Python, Java).
    • Running Spark on YARN.
  • Apache HBase (NoSQL Database on Hadoop):
    • What is HBase? (A distributed, versioned, non-relational column-oriented database built on HDFS).
    • HBase Architecture (Master, RegionServers).
    • HBase Data Model (Tables, Row Keys, Column Families, Columns, Versions, Timestamps).
    • Basic HBase operations (put, get, scan, delete).
    • Use cases for HBase (random real-time read/write access to large datasets).
  • Apache ZooKeeper (Distributed Coordination Service):
    • What is ZooKeeper? (A centralized service for maintaining configuration information, naming, providing distributed synchronization, and providing group services).
    • How Hadoop components (HDFS HA, YARN HA, HBase) use ZooKeeper.
    • Basic ZooKeeper concepts (ZNodes, Watches).
  • Apache Sqoop (Data Transfer between RDBMS and Hadoop):
    • What is Sqoop? (A tool for transferring data between Hadoop and relational databases).
    • Importing data from RDBMS to HDFS/Hive/HBase.
    • Exporting data from HDFS/Hive/HBase to RDBMS.
  • Apache Flume (Data Ingestion Service):
    • What is Flume? (A distributed, reliable, and available system for efficiently collecting, aggregating, and moving large amounts of log data from many different sources to a centralized data store).
    • Flume Architecture (Sources, Channels, Sinks).
    • Configuring Flume agents.
  • Apache Kafka (Distributed Event Streaming Platform):
    • What is Kafka? (A distributed publish-subscribe messaging system).
    • Core concepts (Topics, Producers, Consumers, Brokers, Zookeeper).
    • Integrating Kafka with Hadoop ecosystem components (Spark Streaming, Flume, Storm).
  • Apache Oozie (Workflow Scheduler):
    • What is Oozie? (A workflow scheduler system to manage Apache Hadoop jobs).
    • Defining workflows (XML).
    • Scheduling jobs.

Section 6: Hadoop Administration and Operations (Optional)

  • Hadoop Installation and Configuration:
    • Setting up a single-node pseudo-distributed cluster.
    • Setting up a multi-node distributed cluster.
    • Understanding Hadoop configuration files (core-site.xml, hdfs-site.xml, yarn-site.xml, mapred-site.xml).
  • Cluster Management:
    • Starting and stopping Hadoop services.
    • Monitoring cluster health (NameNode UI, ResourceManager UI).
    • Adding/removing DataNodes.
    • Balancing HDFS data.
  • Security in Hadoop:
    • Introduction to Kerberos for authentication.
    • HDFS file permissions.
    • Service-level authorization.
  • Troubleshooting Common Hadoop Issues.

Section 7: Advanced Topics and Future Trends (Optional)

  • Hadoop 3.x Features:
    • Erasure Coding.
    • Multiple NameNodes.
    • YARN Federation.
    • Containerization (Docker/Kubernetes with Hadoop).
  • Integration with Cloud Platforms:
    • Running Hadoop/Spark on GCP (Cloud Dataproc).
    • Running Hadoop/Spark on AWS (EMR).
    • Running Hadoop/Spark on Azure (HDInsight).
  • Emerging Big Data Technologies:
    • Apache Flink (Stream Processing).
    • Apache Kudu (Fast Analytics on Fast Data).
    • Presto/Trino (Distributed SQL Query Engine).

Section 8: Practice and Projects

  • Work on Real-World Datasets:
    • Find publicly available datasets (e.g., from Kaggle, data.gov).
  • Implement Sample MapReduce/Spark/Hive/Pig Jobs:
    • Word Count (classic).
    • Log analysis.
    • Analyzing social media data.
    • Processing sensor data.
  • Build a Simple Data Pipeline:
    • Ingest data using Flume/Sqoop.
    • Store data in HDFS/HBase.
    • Process data using MapReduce/Spark/Hive/Pig.
    • Analyze data using Hive/Spark SQL.
  • Contribute to Open Source Projects (Optional).