Building a Complete Big Data Platform: From Hadoop Basics to Real‑Time Analytics

Introduction

The article is a step‑by‑step tutorial for newcomers who want to build a full‑stack big‑data platform, covering everything from basic Hadoop concepts to advanced real‑time analytics.

Four V Characteristics of Big Data

Volume : terabytes to petabytes of data.

Variety : structured, semi‑structured, unstructured (logs, video, images, geo‑data).

Value : high commercial value that must be extracted via analysis and machine learning.

Velocity : need for low‑latency processing beyond offline batch jobs.

Common Open‑Source Big Data Components

File storage: Hadoop HDFS, Tachyon, KFS

Batch processing: Hadoop MapReduce, Spark

Streaming: Storm, Spark Streaming, S4, Heron

NoSQL: HBase, Redis, MongoDB

Resource management: YARN, Mesos

Log collection: Flume, Scribe, Logstash, Kibana

Message systems: Kafka, StormMQ, ZeroMQ, RabbitMQ

SQL engines: Hive, Impala, Pig, Presto, Phoenix, SparkSQL, Drill, Flink, Kylin, Druid

Coordination: Zookeeper

Cluster monitoring: Ambari, Ganglia, Nagios, Cloudera Manager

Machine learning: Mahout, Spark MLLib

Data transfer: Sqoop, Oozie (workflow), DataX

Chapter 1 – Getting Started with Hadoop

1.1 Search for Solutions

Always start by searching online (Google first, Baidu if needed) to solve problems yourself.

1.2 Official Documentation

The official Hadoop documentation is the primary learning resource.

1.3 Run Hadoop

Install Hadoop via command‑line packages, not GUI tools. Focus on Hadoop 2.x (YARN) rather than the legacy 1.x.

1.4 Core Concepts to Know

Hadoop 1.0 / 2.0

MapReduce and HDFS

NameNode and DataNode

JobTracker and TaskTracker (legacy)

YARN, ResourceManager, NodeManager

1.5 Basic Operations

Practice HDFS commands (put, get, ls), submit a simple MapReduce job, and inspect the Hadoop Web UI for job status and logs.

1.6 Understand the Principles

Learn how MapReduce divides work, how HDFS stores replicas, and the roles of YARN, NameNode, and ResourceManager.

1.7 Write a Simple MapReduce Program

Implement a WordCount example (Java, Python, or Shell via Hadoop Streaming), package it, and run it on the cluster.

Chapter 2 – SQL on Hadoop with Hive

2.1 Learn SQL

Basic SELECT, WHERE, GROUP BY statements are essential.

2.2 Hive Overview

Hive is a data‑warehouse tool that stores massive, relatively static datasets and provides a SQL‑like interface that translates queries into MapReduce jobs.

2.3 Install and Configure Hive

Follow the same steps as Hadoop installation to get Hive running and access its CLI.

2.4 Basic Hive Commands

Create / drop tables

Load data into tables

Download table data

2.5 Example: WordCount with Hive

Create a wordcount table and run: SELECT word, COUNT(1) FROM wordcount GROUP BY word; Verify that the result matches the MapReduce WordCount output.

Chapter 3 – Ingesting Data into Hadoop

3.1 HDFS PUT

Use the hdfs dfs -put command directly or via scripts.

3.2 HDFS API

Programmatic write access via Java/Python APIs; often wrapped by higher‑level tools.

3.3 Sqoop

Transfer data between relational databases (MySQL, Oracle, SQL Server) and Hadoop/Hive by generating MapReduce jobs.

3.4 Flume

Collect and transport massive log streams to HDFS in near‑real time.

3.5 DataX

Alibaba’s open‑source data‑exchange tool; comparable to Sqoop, useful for heterogeneous sources.

Chapter 4 – Exporting Data from Hadoop

4.1 HDFS GET

Retrieve files from HDFS to local storage.

4.2 Sqoop / DataX

Use the same tools as ingestion to move processed data back to relational databases.

Chapter 5 – Faster SQL with SparkSQL

Hive’s MapReduce engine can be slow; SparkSQL, Impala, and Presto provide in‑memory or semi‑memory execution for quicker queries. The guide prefers SparkSQL due to lower resource requirements.

5.1 Spark and SparkSQL Basics

What Spark is and its core concepts.

Relationship between SparkSQL and Hive.

Why SparkSQL outperforms Hive.

5.2 Deploying SparkSQL on YARN

Run SparkSQL jobs on a YARN cluster and query Hive tables directly.

Chapter 6 – One‑to‑Many Data Consumption with Kafka

6.1 Kafka Fundamentals

Explain Kafka’s architecture and key terminology.

6.2 Deploy and Use Kafka

Set up a single‑node Kafka cluster, run the built‑in producer/consumer demos, write custom Java producers/consumers, and integrate Flume to forward logs to Kafka.

Chapter 7 – Workflow Scheduling with Oozie

7.1 Oozie Overview

What Oozie is and its capabilities.

Supported task types (MapReduce, Hive, Spark, Shell, etc.).

Trigger mechanisms (time‑based, data‑driven).

Installation and configuration steps.

7.2 Alternative Schedulers

Briefly mention Azkaban, Zeus, and custom solutions.

Chapter 8 – Real‑Time Processing

8.1 Storm

Introduce Storm, its components, typical use cases, and a simple demo.

8.2 Spark Streaming

Explain Spark Streaming, compare it with Storm, and show a Kafka + Spark Streaming demo.

Chapter 9 – Exposing Data to Consumers

Discuss offline delivery (Sqoop, DataX), real‑time APIs (HBase, Redis, MongoDB, Elasticsearch), OLAP engines (Impala, Presto, Kylin), and ad‑hoc query tools.

Chapter 10 – Introductory Machine Learning

Outline three typical problems (classification, clustering, recommendation) and suggest a learning path: mathematics basics → Python → Spark MLlib.

Conclusion

After completing all chapters, readers should be able to design, deploy, and operate a robust big‑data platform covering data ingestion, storage, batch and streaming computation, data exchange, workflow orchestration, and basic machine‑learning pipelines.