Commercial Distributions of Hadoop: An Overview

Introduction

Hadoop distributions are commercially packaged and supported versions of the open-source Apache Hadoop framework and its related projects. These distributions offer scalable, distributed computing capabilities for both on-premises and cloud-based file storage data. They provide a comprehensive suite of applications, query and reporting tools, machine learning, and data management infrastructure components. Initially introduced as collections of components for various use cases, these distributions have evolved into specialized solutions for data lakes, machine learning, and other applications, competing with both traditional database management systems (DBMSs) and newer technologies like Apache Spark.

One important aspect to highlight is that learning Hadoop provides a solid foundation for working with any of these commercial distributions. The core concepts, tools, and functionalities of Hadoop are preserved across different distributions, ensuring that skills in Hadoop can be directly applied to these commercial solutions.

Major Hadoop Distributions

HPE Ezmeral Data Fabric

HPE Ezmeral Data Fabric, formerly known as MapR, is designed for performance and scalability. It distinguishes itself by not using the Hadoop Distributed File System (HDFS), thereby eliminating many limitations associated with HDFS. This full read/write file system supports industry-standard file operations, offering robust performance for large-scale data processing.

Key Features:

• Non-HDFS file system
• High scalability and performance
• Suitable for diverse big data applications

Microsoft Azure Data Lake Store

Azure Data Lake Store is Microsoft's solution for cloud-based data storage, providing a fast, reliable service for storing hierarchical data. It is optimized for large-scale data processing and is particularly well-suited for enterprise-level applications.

Key Features:

• Hierarchical data storage
• Integration with other Microsoft Azure services
• High reliability and performance

Huawei FusionInsight Big Data Platform

FusionInsight by Huawei is a comprehensive big data platform that offers agile, intelligent, and reliable data storage and analysis capabilities. It supports multi-dimensional comparisons and high-performance querying, making it a robust solution for various data-intensive applications.

Key Features:

• Multi-dimensional data comparisons
• High-performance data analysis and querying
• Reliable data storage solutions

Google Cloud Platform (GCP)

Google Cloud Platform provides powerful distributed computing capabilities, making it easy to store and share files with robust privacy controls. GCP is particularly noted for its scalability and ease of cluster management, facilitating large-scale data processing tasks.

Key Features:

• Scalable distributed computing
• Easy file storage and sharing
• Strong privacy and access controls

Amazon EMR

Amazon EMR (Elastic MapReduce) is AWS's cloud-based Hadoop distribution, offering powerful tools for large-scale data processing. It integrates seamlessly with other AWS services and allows for rapid cluster creation and management, making it an efficient choice for processing big data.

Key Features:

• Integration with AWS ecosystem
• Rapid cluster creation and management
• Scalable data processing capabilities

IBM BigInsights for Apache Hadoop

IBM BigInsights provides a flexible and manageable platform for integrating Hadoop with other systems. It offers easy web component and logging management, making it suitable for analytical reporting and integration with various downstream systems.

Key Features:

• Flexible integration with other systems
• Easy management of web components and logging
• Suitable for analytical reporting

Oracle Big Data SQL

Oracle Big Data SQL facilitates data analysis across Hadoop, NoSQL, and relational databases. It enables comprehensive data analysis and extraction, supporting a wide variety of data sources and types, making it a versatile tool for big data applications.

Key Features:

• Cross-platform data analysis
• Supports Hadoop, NoSQL, and relational databases
• Comprehensive data extraction and analysis tools

Hadoop distributions provide powerful, scalable solutions for handling large-scale data processing needs. From on-premises systems to cloud-based platforms, these commercial distributions offer a variety of tools and features tailored to meet the demands of modern big data applications. Whether it's through enhanced performance, ease of use, or integration capabilities, these distributions play a crucial role in the evolving landscape of data management and analytics.

Crucially, learning Hadoop is enough to work with any of these commercial distributions. The core concepts and skills acquired through mastering Hadoop are directly applicable, enabling professionals to leverage the advanced features and capabilities of these distributions effectively.