Hadoop Ecosystem
1. Definition
Hadoop Ecosystem is a collection of open-source software projects and tools that extend the capabilities of core Hadoop framework, providing specialized functionality for data ingestion, processing, querying, storage, and management.
2. Core Components
2.1 HDFS (Hadoop Distributed File System)
Purpose: Distributed storage system for Big Data
Key Features:
- Stores large files by splitting into blocks (128 MB)
- Replicates each block across multiple nodes (default: 3 copies)
- Provides high throughput data access
- Fault-tolerant through automatic replication
Use Case: Storing petabytes of log files, images, videos, and raw data
2.2 MapReduce
Purpose: Programming model for parallel data processing
Workflow:
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Characteristics:
- Batch processing engine
- Automatic parallelization
- Fault tolerance through task re-execution
- Data locality optimization
2.3 YARN (Yet Another Resource Negotiator)
Purpose: Resource management and job scheduling
Components:
- ResourceManager: Manages cluster resources globally
- NodeManager: Manages resources on individual nodes
- ApplicationMaster: Manages lifecycle of individual applications
- Container: Allocated resource slice (CPU, memory)
Benefits:
- Enables multiple processing frameworks on same cluster
- Better resource utilization
- Improved scalability
- Framework independence
3. Data Storage Components
3.1 HBase
Definition: Distributed, column-oriented NoSQL database built on top of HDFS
Characteristics:
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Use Cases:
- Real-time read/write access to Big Data
- Time-series data storage
- Messaging and inbox systems
- Recommendation systems requiring quick lookups
Example: Facebook uses HBase for Messages storing billions of messages
3.2 Cassandra
Definition: Distributed NoSQL database designed for high availability
Key Features:
- Peer-to-peer architecture (no master-slave)
- Tunable consistency levels
- Linear scalability
- Multi-datacenter replication
Comparison with HBase:
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4. Data Processing Components
4.1 Apache Spark
Definition: Fast, general purpose cluster computing framework
Key Advantages:
- 100x faster than MapReduce for in-memory processing
- Supports batch, streaming, machine learning, and graph processing
- Easy to program (Python, Scala, Java, R)
- Interactive queries possible
Components:
- Spark Core: Basic Spark functionality
- Spark SQL: SQL queries on Big Data
- Spark Streaming: Real-time stream processing
- MLlib: Machine learning library
- GraphX: Graph processing
Why Faster Than MapReduce:
- In-memory computation vs disk-based MapReduce
- Optimized execution plans
- Reduced intermediate writes to disk
4.2 Apache Flink
Definition: Stream processing framework for stateful computations
Key Features:
- True stream processing (event-by-event)
- Low latency (milliseconds)
- Exactly-once processing semantics
- Event time processing
Use Cases:
- Real-time fraud detection
- Live dashboards
- Real-time recommendations
- Continuous ETL
5. Data Query Components
5.1 Apache Hive
Definition: Data warehouse infrastructure providing SQL interface for Hadoop
How It Works:
SQL Query (HiveQL) → Hive Translator → MapReduce/Spark Jobs →
Execute on Hadoop → Results
Features:
- SQL-like query language (HiveQL)
- Familiar interface for SQL analysts
- Handles structured and semi-structured data
- Integrates with business intelligence tools
Example Query:
CREATE TABLE sales (product STRING, amount DECIMAL);
LOAD DATA INPATH '/data/sales.csv' INTO TABLE sales;
SELECT product, SUM(amount) FROM sales GROUP BY product;
Use Case: Business analysts querying Big Data without learning Java/Python
5.2 Apache Pig
Definition: High-level platform for creating MapReduce programs
Pig Latin Language:
data = LOAD 'sales.txt' AS (product:chararray, amount:int);
filtered = FILTER data BY amount > 1000;
grouped = GROUP filtered BY product;
result = FOREACH grouped GENERATE group, SUM(filtered.amount);
STORE result INTO 'output';
Advantages:
- Simpler than writing MapReduce code
- Automatically optimizes execution
- Handles complex data transformations
- Good for ETL operations
Hive vs Pig:
| Aspect | Hive | Pig |
|---|---|---|
| Language | SQL-like (HiveQL) | Procedural (Pig Latin) |
| Users | Analysts familiar with SQL | Programmers/data engineers |
| Optimization | Query optimizer | Execution optimizer |
| Use Case | Reporting, analytics | ETL, data transformation |
6. Data Ingestion Components
6.1 Apache Flume
Definition: Service for efficiently collecting, aggregating, and moving streaming data
Architecture:
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Use Cases:
- Collecting server logs from multiple servers
- Streaming data from applications to HDFS
- Real-time log aggregation
Example: Collecting Apache web server logs from 100 servers into HDFS
6.2 Apache Sqoop
Definition: Tool for efficiently transferring data between Hadoop and relational databases
Functionality:
Import (RDBMS → Hadoop):
sqoop import --connect jdbc:mysql://database/db --table customers --target-dir /user/hdfs/customers
Export (Hadoop → RDBMS):
sqoop export --connect jdbc:mysql://database/db --table results --export-dir /user/hdfs/results
Use Cases:
- Migrating legacy database data to Hadoop
- Exporting Hadoop analysis results to databases
- Incremental data imports for daily/hourly updates
6.3 Apache Kafka
Definition: Distributed streaming platform for building real-time data pipelines
Key Capabilities:
- Publish-Subscribe: Applications publish/consume data streams
- Storage: Durable, fault-tolerant storage of streams
- Processing: Real-time stream processing
Architecture:
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Use Cases:
- Uber: Real-time trip data streaming
- LinkedIn: Activity tracking
- Netflix: Viewing data streams
7. Coordination and Management
7.1 Apache ZooKeeper
Definition: Centralized service for maintaining configuration, naming, and synchronization
Functions:
- Cluster coordination
- Configuration management
- Leader election
- Distributed locking
- Service discovery
Role in Hadoop: Ensures high availability of NameNode through automatic failover
7.2 Apache Oozie
Definition: Workflow scheduler for managing Hadoop jobs
Features:
- Schedule complex workflows
- Handle job dependencies
- Support for MapReduce, Pig, Hive, Sqoop jobs
- Time-based and data-based triggers
Example Workflow:
Daily ETL Process:
1. Sqoop: Import new data from MySQL (6:00 AM)
2. Pig: Clean and transform data (6:30 AM)
3. Hive: Run analytical queries (7:00 AM)
4. Sqoop: Export results back to MySQL (8:00 AM)
8. Complete Ecosystem Map
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Exam Pattern Questions and Answers
Question 1: "Explain the Hadoop Ecosystem with its major components." (10 Marks)
Answer:
Introduction (1 mark):
Hadoop Ecosystem is a collection of open-source tools extending core Hadoop capabilities, providing specialized functionality for data ingestion, storage, processing, querying, and management to build complete Big Data solutions.
Core Components (3 marks):
HDFS (Hadoop Distributed File System) provides distributed storage by splitting files into 128 MB blocks and replicating across nodes for fault tolerance. MapReduce offers parallel data processing through Map and Reduce phases with automatic fault handling. YARN (Yet Another Resource Negotiator) manages cluster resources and schedules jobs, enabling multiple processing frameworks on same cluster.
Storage Components (2 marks):
HBase provides real-time random read/write access to Big Data with column-family data model, used by Facebook for Messages. Cassandra offers peer-to-peer distributed database with high availability and tunable consistency for always-on applications.
Processing Components (2 marks):
Apache Spark delivers 100x faster in-memory processing than MapReduce, supporting batch, streaming, ML, and graph processing. Apache Flink enables true stream processing with millisecond latency for real-time applications like fraud detection and live dashboards.
Query and Ingestion (2 marks):
Hive provides SQL interface (HiveQL) for analysts to query Big Data without programming. Pig offers Pig Latin scripting for ETL operations. Flume collects streaming log data, while Sqoop transfers data between Hadoop and relational databases. Kafka handles real-time data streaming for applications like Uber's trip data processing.
Question 2: "Compare Hive and Pig. When to use each?" (6 Marks)
Answer:
Apache Hive (3 marks):
Hive provides SQL-like interface (HiveQL) for querying Big Data, making it accessible to analysts familiar with SQL but not programming. It includes query optimizer for efficient execution and integrates easily with business intelligence tools. Suitable for reporting, analytics, and ad-hoc queries where users know SQL. Example: Business analysts generating sales reports by writing SELECT queries on terabytes of transaction data.
Apache Pig (3 marks):
Pig uses procedural language (Pig Latin) for data transformation workflows, preferred by programmers and data engineers. It provides execution optimizer for complex transformations and excels at ETL operations involving multiple data manipulation steps. Suitable for data pipeline development and complex transformations requiring programmatic control. Example: Data engineers building daily ETL pipeline to clean, transform, and aggregate log data before analysis.
Summary
Key Points for Revision:
- Core: HDFS (storage), MapReduce (batch processing), YARN (resource management)
- Storage: HBase (real-time), Cassandra (high availability)
- Processing: Spark (fast in-memory), Flink (stream processing)
- Query: Hive (SQL), Pig (scripting)
- Ingestion: Flume (logs), Sqoop (RDBMS), Kafka (streaming)
- Coordination: ZooKeeper (cluster coordination), Oozie (workflow scheduling)
For ecosystem questions, group components by function (storage, processing, query, ingestion). Always mention real-world examples (Facebook uses HBase, Uber uses Kafka). Draw the data flow diagram showing how components work together.
Quiz Time! 🎯
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