Hadoop vs Spring Batch

Overview

Hadoop

Stacks2.7K

Followers2.3K

Votes56

GitHub Stars15.3K

Forks9.1K

Spring Batch

Stacks184

Followers250

Votes0

GitHub Stars2.9K

Forks2.5K

Hadoop vs Spring Batch: What are the differences?

Scalability: Hadoop is designed for massive scalability and handling large amounts of data. It supports distributed computing and can easily scale the processing power by adding more machines to the cluster. On the other hand, Spring Batch focuses on batch processing and is not inherently built for scalability like Hadoop. It may require additional configuration and infrastructure setup to achieve scalability in Spring Batch.
Data Processing: Hadoop is primarily used for processing big data in distributed environments. It provides a framework for distributed file storage (HDFS) and a processing engine (MapReduce) that allows parallel processing of data across multiple nodes in a Hadoop cluster. Spring Batch, on the other hand, is a framework designed specifically for batch processing. It provides a set of reusable components and patterns for processing large volumes of data in a batch fashion.
Ecosystem: Hadoop has a thriving ecosystem of tools and technologies built around it, such as Hive, Pig, and Spark, which provide additional functionalities for data processing, querying, and analysis. Spring Batch, although does not have as extensive an ecosystem as Hadoop, integrates well with other Spring frameworks and libraries, such as Spring Data and Spring Integration, allowing for a wider range of capabilities beyond batch processing.
Data Storage: Hadoop's HDFS (Hadoop Distributed File System) is a distributed file system designed for storing large volumes of data across multiple machines. It provides fault tolerance and high throughput for data storage. Spring Batch does not provide a built-in distributed file system and relies on other storage solutions for persisting batch job data, such as relational databases or message queues.
Fault Tolerance: Hadoop is known for its built-in fault tolerance mechanisms. It automatically handles failures by replicating data blocks across multiple nodes in the cluster and rerouting tasks to other nodes in case of failures. Spring Batch does not have built-in fault tolerance features like Hadoop, and the handling of failures needs to be implemented manually, typically through error logging and retry mechanisms.
Toolset and Complexity: Hadoop offers a comprehensive set of tools and frameworks for various aspects of big data processing, including data ingestion, storage, processing, and analysis. This wide range of tools and frameworks can make Hadoop more complex to set up and maintain compared to Spring Batch, which is focused solely on batch processing and provides a more streamlined and simplified approach.

In summary, Hadoop and Spring Batch differ in terms of scalability, data processing capabilities, ecosystem, data storage, fault tolerance mechanisms, and toolset complexity.

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Detailed Comparison

Hadoop	Spring Batch
The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.	It is designed to enable the development of robust batch applications vital for the daily operations of enterprise systems. It also provides reusable functions that are essential in processing large volumes of records, including logging/tracing, transaction management, job processing statistics, job restart, skip, and resource management.
-	Transaction management; Chunk based processing; Declarative I/O
Statistics
GitHub Stars 15.3K	GitHub Stars 2.9K
GitHub Forks 9.1K	GitHub Forks 2.5K
Stacks 2.7K	Stacks 184
Followers 2.3K	Followers 250
Votes 56	Votes 0
Pros & Cons
Pros 39 Great ecosystem 11 One stack to rule them all 4 Great load balancer 1 Java syntax 1 Amazon aws	No community feedback yet
Integrations
No integrations available	Spring Boot MongoDB

What are some alternatives to Hadoop, Spring Batch?

Node.js

Node.js uses an event-driven, non-blocking I/O model that makes it lightweight and efficient, perfect for data-intensive real-time applications that run across distributed devices.

Rails

Rails is a web-application framework that includes everything needed to create database-backed web applications according to the Model-View-Controller (MVC) pattern.

Django

Django is a high-level Python Web framework that encourages rapid development and clean, pragmatic design.

MongoDB

MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.

Laravel

It is a web application framework with expressive, elegant syntax. It attempts to take the pain out of development by easing common tasks used in the majority of web projects, such as authentication, routing, sessions, and caching.

MySQL

The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.

PostgreSQL

PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.

.NET

.NET is a general purpose development platform. With .NET, you can use multiple languages, editors, and libraries to build native applications for web, mobile, desktop, gaming, and IoT for Windows, macOS, Linux, Android, and more.

ASP.NET Core

A free and open-source web framework, and higher performance than ASP.NET, developed by Microsoft and the community. It is a modular framework that runs on both the full .NET Framework, on Windows, and the cross-platform .NET Core.

Symfony

It is written with speed and flexibility in mind. It allows developers to build better and easy to maintain websites with PHP..

Related Comparisons

Hadoop vs Spring Batch: What are the differences?

Scalability: Hadoop is designed for massive scalability and handling large amounts of data. It supports distributed computing and can easily scale the processing power by adding more machines to the cluster. On the other hand, Spring Batch focuses on batch processing and is not inherently built for scalability like Hadoop. It may require additional configuration and infrastructure setup to achieve scalability in Spring Batch.
Data Processing: Hadoop is primarily used for processing big data in distributed environments. It provides a framework for distributed file storage (HDFS) and a processing engine (MapReduce) that allows parallel processing of data across multiple nodes in a Hadoop cluster. Spring Batch, on the other hand, is a framework designed specifically for batch processing. It provides a set of reusable components and patterns for processing large volumes of data in a batch fashion.
Ecosystem: Hadoop has a thriving ecosystem of tools and technologies built around it, such as Hive, Pig, and Spark, which provide additional functionalities for data processing, querying, and analysis. Spring Batch, although does not have as extensive an ecosystem as Hadoop, integrates well with other Spring frameworks and libraries, such as Spring Data and Spring Integration, allowing for a wider range of capabilities beyond batch processing.
Data Storage: Hadoop's HDFS (Hadoop Distributed File System) is a distributed file system designed for storing large volumes of data across multiple machines. It provides fault tolerance and high throughput for data storage. Spring Batch does not provide a built-in distributed file system and relies on other storage solutions for persisting batch job data, such as relational databases or message queues.
Fault Tolerance: Hadoop is known for its built-in fault tolerance mechanisms. It automatically handles failures by replicating data blocks across multiple nodes in the cluster and rerouting tasks to other nodes in case of failures. Spring Batch does not have built-in fault tolerance features like Hadoop, and the handling of failures needs to be implemented manually, typically through error logging and retry mechanisms.
Toolset and Complexity: Hadoop offers a comprehensive set of tools and frameworks for various aspects of big data processing, including data ingestion, storage, processing, and analysis. This wide range of tools and frameworks can make Hadoop more complex to set up and maintain compared to Spring Batch, which is focused solely on batch processing and provides a more streamlined and simplified approach.

In summary, Hadoop and Spring Batch differ in terms of scalability, data processing capabilities, ecosystem, data storage, fault tolerance mechanisms, and toolset complexity.

Hadoop vs Spring Batch

Overview