December 9, 2025 11 min to read

Data ETL Pipeline Components and Architecture Guide

A comprehensive guide to building scalable ETL pipelines with Hadoop ecosystem

Image Reference

Overview

ETL (Extract, Transform, Load) pipelines are essential data engineering processes that extract, transform, and load large volumes of data for data analysis, machine learning, and business insights.

This post will explain the components of Hadoop-based ETL pipelines and the key technology stack for each stage.

What is an ETL Pipeline?

An ETL pipeline is a data processing workflow that collects, transforms, and transfers data to data storage systems.

Most implementations leverage the Hadoop Ecosystem for big data processing, while real-time streaming data often combines technologies like Kafka and Flink.

ETL vs ELT Differences

ETL Pipeline Core Components

ETL pipelines consist of three fundamental stages that work together to process data efficiently.

Data Flow Architecture

1. Extract (Data Extraction)

The extraction stage involves collecting data from various sources and systems.

Major Data Sources

• Databases: MySQL, PostgreSQL, Oracle, SQL Server
• File Systems: CSV, JSON, Parquet formats
• APIs and Web Data: REST API, GraphQL
• Streaming Data: Kafka, Flume
• Log Files and Sensor Data: Fluentd, Logstash, IoT devices

Recommended Technology Stack

2. Transform (Data Transformation and Processing)

The transformation stage processes extracted data to make it suitable for analysis. This includes deduplication, filtering, joining, aggregation, and format conversion.

Recommended Technology Stack

3. Load (Data Storage and Utilization)

The loading stage stores transformed data in appropriate storage systems, making it available for analysis and utilization through data warehouses, data lakes, NoSQL databases, and search engines.

Recommended Technology Stack

ETL Pipeline Design Considerations

When designing ETL pipelines, several critical factors must be considered to ensure scalability, performance, and reliability.

Key Design Factors

1. Data Volume (Big Data)

• Distributed Processing: As data volume increases, distributed systems like Hadoop and Spark become essential
• Scalable Storage: Requires expandable storage solutions like HDFS and S3

2. Real-time vs Batch Processing

• Batch Processing: Spark, MapReduce, Hive for scheduled data processing
• Streaming Processing: Kafka, Flink, Spark Streaming for real-time data processing

3. Scalability Requirements

• Distributed Environments: Use Spark, Kubernetes, and Airflow for scaling
• Cloud Solutions: Leverage AWS Glue, Google Dataflow, Azure Data Factory

4. Error Handling and Monitoring

• Workflow Management: Use Airflow or Prefect for orchestration
• Real-time Monitoring: Implement Prometheus + Grafana
• Log Analysis: Deploy Elasticsearch + Kibana

ETL Pipeline Example (Hadoop & Spark Based)

This section demonstrates a practical ETL pipeline implementation using the Hadoop ecosystem.

ETL Process Example

Scenario: E-commerce transaction data processing

1. Extract: Import transactions table data from MySQL and store in HDFS
2. Transform: Use Spark to clean data and filter users with high purchase amounts
3. Load: Store transformed data in HBase for fast query support

ETL Workflow Implementation

Basic ETL Commands

Detailed ETL Process Implementation

This section provides comprehensive examples and additional use cases for each ETL stage.

Extract Stage (MySQL → HDFS)

Objective

• Transfer MySQL transactions table data to HDFS
• Use Sqoop for relational database to Hadoop data migration

Basic Implementation

Explanation:

• jdbc:mysql://dbserver:3306/ecommerce → MySQL database connection
• transactions → Source table name
• /data/transactions → HDFS destination directory
• -m 1 → Single mapper usage (can enable parallel processing)

Advanced Use Cases

Filtered Data Extraction (Last 30 Days)

High-Volume Data Processing (4 Parallel Mappers)

Transform Stage (Spark Data Processing)

Objective

• Use Spark to filter users with high purchase amounts
• Store filtered data back to HDFS for further processing

Implementation Command

spark-submit --class MainApp transform.py

Python Spark Script Example

from pyspark.sql import SparkSession

# Create Spark session
spark = SparkSession.builder.appName("HighValueCustomers").getOrCreate()

# Read data from HDFS
df = spark.read.option("header", "true").csv("hdfs:///data/transactions")

# Filter data (purchase_amount > 1000)
high_value_customers = df.filter(df.purchase_amount > 1000)

# Save filtered data
high_value_customers.write.csv("hdfs:///data/high_value_customers", header=True)

spark.stop()

Process Explanation:

• Create SparkSession → Read CSV data from HDFS
• Apply filtering → Keep only records where purchase_amount > 1000
• Save results back to HDFS

Advanced Use Cases

Parquet Format Conversion (Performance Optimization)

high_value_customers.write.parquet("hdfs:///data/high_value_customers_parquet")

Advanced Filtering with Spark SQL

df.createOrReplaceTempView("transactions")
high_value_customers = spark.sql("""
    SELECT * FROM transactions 
    WHERE purchase_amount > 1000 AND region = 'US'
""")

Load Stage (HDFS → HBase Storage)

Objective

• Store filtered data in HBase for real-time analysis capabilities
• HBase is optimal for data requiring fast query performance

Basic HBase Operations

hbase shell <<EOF
  create 'high_value_customers', 'info'
  put 'high_value_customers', 'user123', 'info:purchase', '1000'
EOF

Explanation:

• create 'high_value_customers', 'info' → Create HBase table
• put 'high_value_customers', 'user123', 'info:purchase', '1000' → Store user data

Advanced Use Case: Direct Spark to HBase Loading

from pyspark.sql import SparkSession
from pyspark.sql.functions import col
import happybase

# Create Spark session
spark = SparkSession.builder.appName("LoadToHBase").getOrCreate()

# HBase connection
connection = happybase.Connection('hbase-master')
table = connection.table('high_value_customers')

# Read data from HDFS
df = spark.read.parquet("hdfs:///data/high_value_customers_parquet")

# Store in HBase
for row in df.collect():
    table.put(
        row['user_id'].encode(), 
        {b'info:purchase': str(row['purchase_amount']).encode()}
    )

connection.close()

Final ETL Workflow Summary

Modern ETL Trends and Technologies

The data engineering landscape continues to evolve with cloud-native solutions and modern architectures.

Current Industry Trends

ELT Approach with Cloud Data Warehouses

• Snowflake, BigQuery: Cloud-native data warehouses supporting ELT patterns
• Advantages: Leverage cloud compute power for transformations
• Use Cases: When transformation logic is complex and benefits from warehouse optimization

Spark + Airflow Automation

• Data Pipeline Automation: Orchestrate complex workflows
• Benefits: Scheduling, monitoring, and error handling
• Integration: Seamless integration with Kubernetes environments

Real-time Processing Evolution

• Stream Processing: Kafka + Flink + Elasticsearch combinations
• Lambda Architecture: Batch and stream processing in parallel
• Kappa Architecture: Stream-first approach with unified processing

Technology Selection Guide

Implementation Best Practices

When building production ETL pipelines, following established best practices ensures reliability and maintainability.

Design Principles

Data Quality and Validation

• Schema Validation: Enforce data types and constraints
• Data Profiling: Monitor data quality metrics
• Error Handling: Implement comprehensive error recovery

Performance Optimization

• Partitioning Strategy: Optimize data layout for query performance
• Compression: Use appropriate compression algorithms
• Caching: Implement intelligent caching for frequently accessed data

Monitoring and Observability

• Pipeline Monitoring: Track execution times and success rates
• Data Lineage: Maintain visibility into data flow and transformations
• Alerting: Set up proactive alerts for pipeline failures

Key Points

Conclusion

ETL pipelines are fundamental data engineering processes that collect (Extract) → transform (Transform) → store (Load) data for analytics and insights.

When building Hadoop-based solutions, technologies like HDFS, Spark, Hive, and Kafka provide the scalability and performance needed for modern data workloads.

Current trends favor cloud-native approaches with Spark, Flink, and Airflow for automated data pipeline orchestration.

Latest Technology Trends

• ELT approach using cloud data warehouses like Snowflake and BigQuery
• Data pipeline automation with Spark + Airflow integration
• Real-time processing capabilities with modern streaming technologies

Recommendations for ETL Pipeline Development

1. Batch Processing: Hadoop + Spark + Airflow
2. Real-time Streaming: Kafka + Flink + Elasticsearch
3. Cloud Data Warehouse: Snowflake, Redshift, BigQuery

The choice depends on your specific requirements for data volume, processing speed, and infrastructure preferences.

References

• ETL Pipeline Architecture Guide
• What is ETL? - Luzmo Blog
• Data Engineering Best Practices
• Apache Spark Documentation
• Hadoop Ecosystem Overview
• Apache Airflow Documentation