Hadoop Big Data Stack - HDFS MapReduce YARN

This is a repackaged open source software product wherein additional charges apply for cloudimg support services.

## Hadoop Big Data Stack by cloudimg

Stop spending days manually installing and configuring Hadoop. This pre-configured AMI gives data engineering teams a production-ready Apache Hadoop cluster on AWS - with HDFS, MapReduce, and YARN running and optimized from first boot. Available on Alma Linux 8, Ubuntu 20.04, and Ubuntu 22.04, with 24/7 cloudimg support and a guaranteed 24-hour response SLA.

## Who Is This For?

Data engineering teams and platform architects who need full control over their Hadoop infrastructure without the operational overhead of Amazon EMR's managed service model. Ideal for organizations building data lakes, running ETL pipelines, or processing large-scale analytics workloads where cluster-level customization and persistent infrastructure are required.

## Why Choose This Hadoop AMI Over Alternatives?

• Full cluster control - Unlike managed services, you retain SSH access, custom configuration, and complete flexibility over Hadoop versions and ecosystem components
• Multi-OS support - Choose from Alma Linux 8, Ubuntu 20.04, or Ubuntu 22.04 to match your organization's standards
• Pre-tuned JVM and storage - Hadoop configuration optimized for EC2 instance storage patterns, reducing time spent on performance tuning
• Cluster expansion with support - Launch additional nodes and cloudimg assists with multi-node configuration and HDFS rebalancing
• 24/7 UK-based support - Guaranteed 24-hour response SLA with average one-hour response for critical issues

## Key Components

HDFS Distributed Storage - Reliable file storage across cluster nodes with block replication for redundancy. Petabyte-scale capacity with high-throughput reads, write-once-read-many optimization, and rack awareness for data locality.NameNode manages metadata; DataNodes store blocks.

MapReduce Processing - Parallel data processing framework distributing work across nodes. Map phase splits tasks, Reduce phase aggregates results. Includes fault recovery for failed tasks, data locality optimization, and job history tracking.

YARN Resource Management - Cluster resource scheduler with dynamic allocation, container-based execution, queue management, and ApplicationMaster coordination. Supports multiple processing frameworks beyond MapReduce.

## Real-World Use Case: E-Commerce Clickstream Processing

An e-commerce platform ingesting500GB per day of clickstream events can use this AMI to build a processing pipeline: raw event logs land in HDFS via Flume, MapReduce jobs run hourly to sessionize user journeys and compute conversion funnels, and processed data loads into a data warehouse via Sqoop for business intelligence dashboards. The entire pipeline runs on a cluster of storage-optimized EC2 instances with YARN managing job scheduling and resource allocation.

## Pre-Configured Integration

• HDFS NameNode and DataNode services configured for startup via systemd
• YARN ResourceManager and NodeManager ready
• SSH access on port 22
• Java runtime optimized for Hadoop workloads
• Configuration files in standard locations
• Log aggregation enabled
• Cluster configuration templates included

## Monitoring and Management

• YARN ResourceManager web UI on port 8088
• HDFS NameNode web UI on port 9870
• JMX metrics available for integration with monitoring tools
• systemd service management for all Hadoop daemons

## Ecosystem Compatibility

Works with Apache Hive for SQL queries, Pig for data flow scripting, HBase for NoSQL workloads, Spark for in-memory processing, Sqoop for database import/export, Flume for log collection, and Oozie for workflow scheduling.

## Fault Tolerance and Reliability

Automatic failure detection and recovery. Block replication prevents data loss. Task retries on node failures. Speculative execution for slow tasks. NameNode high availability configurable for multi-node deployments. Checkpoint and journal nodes protect metadata.

## Performance Optimization

Data locality reduces network transfer. Compression support includes Snappy, LZO, and Gzip. Combiner functions reduce shuffle data volume. Rack awareness enables optimal data placement across EC2 availability zones.

## Getting Started

1. Launch the AMI on your chosen EC2 instance type

2. SSH into the instance on port 22

3. Verify Hadoop services are running via systemd

4. Access HDFS web UI on port 9870 and YARN on port 8088

5. Run sample MapReduce jobs from /usr/local/hadoop/share/hadoop

6. For multi-node clusters, launch additional instances and contact cloudimg support for cluster formation assistance

## Book a Free Cluster Planning Session

## Supported Versions

Multiple Apache Hadoop versions available across Alma Linux 8, Ubuntu 20.04, and Ubuntu 22.04.