Amazon S3 vs Apache Spark vs RabbitMQ

Overview

Amazon S3

Stacks55.1K

Followers40.2K

Votes2.0K

RabbitMQ

Stacks21.8K

Followers18.9K

Votes558

GitHub Stars13.2K

Forks4.0K

Apache Spark

Stacks3.1K

Followers3.5K

Votes140

GitHub Stars42.2K

Forks28.9K

Amazon S3 vs Apache Spark vs RabbitMQ: What are the differences?

<Write Introduction here>

Scalability:
- Amazon S3 is designed to store and retrieve large amounts of data in a scalable manner, making it ideal for big data applications. On the other hand, Apache Spark is a distributed computing framework that provides an in-memory computing capability, allowing it to process large datasets efficiently. RabbitMQ, on the other hand, is a messaging broker that allows different components of a system to communicate with each other asynchronously, facilitating scalable and decoupled architectures.
Data Processing:
- Amazon S3 primarily focuses on storage and retrieval of data, providing an object storage service. Apache Spark, on the other hand, is a powerful data processing engine that can perform complex analytics and data transformations in memory, making it suitable for processing large datasets efficiently. RabbitMQ, being a messaging broker, is not designed specifically for data processing but rather for facilitating communication between different components in a system.
Real-Time Processing:
- While Amazon S3 and Apache Spark are more focused on batch processing of data, RabbitMQ excels in real-time processing by enabling seamless communication between components in a system in real-time. Apache Spark does have streaming capabilities through its Spark Streaming module, but RabbitMQ is specifically designed for real-time communication through message queues.
Programming Models:
- Amazon S3 does not offer any programming models as it is primarily a storage service. Apache Spark, on the other hand, provides various APIs and libraries for different programming languages such as Scala, Java, and Python, making it versatile for different use cases. RabbitMQ offers support for multiple programming languages as well, enabling developers to integrate messaging functionalities into their applications easily.
Fault Tolerance:
- Amazon S3 is highly fault-tolerant and durable, ensuring that data is stored redundantly across multiple servers to prevent data loss. Apache Spark also provides fault tolerance mechanisms through lineage information and RDDs (Resilient Distributed Datasets), which enable the recomputation of lost data in case of failures. RabbitMQ offers features like message acknowledgments, durable queues, and message persistence to ensure message delivery even in case of failures.
Use Cases:
- Amazon S3 is commonly used for storing static files, serving websites, and data lakes for analytics. Apache Spark is popular for data processing, machine learning, and real-time analytics. RabbitMQ is often used for decoupling systems, implementing asynchronous communication, and building scalable distributed systems.

In Summary, Amazon S3 is focused on storage, Apache Spark on data processing, and RabbitMQ on messaging, each serving specific functions in the big data ecosystem.

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Advice on Amazon S3, RabbitMQ, Apache Spark

viradiya

Apr 12, 2020

Needs adviceon

AngularJS

ASP.NET Core

MSSQL

We are going to develop a microservices-based application. It consists of AngularJS, ASP.NET Core, and MSSQL.

We have 3 types of microservices. Emailservice, Filemanagementservice, Filevalidationservice

I am a beginner in microservices. But I have read about RabbitMQ, but come to know that there are Redis and Kafka also in the market. So, I want to know which is best.

933k views933k

Comments

Nilesh

Technical Architect at Self Employed

Jul 8, 2020

Needs adviceon

Elasticsearch

Kafka

We have a Kafka topic having events of type A and type B. We need to perform an inner join on both type of events using some common field (primary-key). The joined events to be inserted in Elasticsearch.

In usual cases, type A and type B events (with same key) observed to be close upto 15 minutes. But in some cases they may be far from each other, lets say 6 hours. Sometimes event of either of the types never come.

In all cases, we should be able to find joined events instantly after they are joined and not-joined events within 15 minutes.

576k views576k

Comments

André

Technology Manager at GS1 Portugal - Codipor

Jul 30, 2020

Needs adviceon

.NET Core

Hello dear developers, our company is starting a new project for a new Web App, and we are currently designing the Architecture (we will be using .NET Core). We want to embark on something new, so we are thinking about migrating from a monolithic perspective to a microservices perspective. We wish to containerize those microservices and make them independent from each other. Is it the best way for microservices to communicate with each other via ESB, or is there a new way of doing this? Maybe complementing with an API Gateway? Can you recommend something else different than the two tools I provided?

We want something good for Cost/Benefit; performance should be high too (but not the primary constraint).

Thank you very much in advance :)

461k views461k

Comments

Detailed Comparison

Amazon S3	RabbitMQ	Apache Spark
Amazon Simple Storage Service provides a fully redundant data storage infrastructure for storing and retrieving any amount of data, at any time, from anywhere on the web	RabbitMQ gives your applications a common platform to send and receive messages, and your messages a safe place to live until received.	Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
Write, read, and delete objects containing from 1 byte to 5 terabytes of data each. The number of objects you can store is unlimited.;Each object is stored in a bucket and retrieved via a unique, developer-assigned key.;A bucket can be stored in one of several Regions. You can choose a Region to optimize for latency, minimize costs, or address regulatory requirements. Amazon S3 is currently available in the US Standard, US West (Oregon), US West (Northern California), EU (Ireland), Asia Pacific (Singapore), Asia Pacific (Tokyo), Asia Pacific (Sydney), South America (Sao Paulo), and GovCloud (US) Regions. The US Standard Region automatically routes requests to facilities in Northern Virginia or the Pacific Northwest using network maps.;Objects stored in a Region never leave the Region unless you transfer them out. For example, objects stored in the EU (Ireland) Region never leave the EU.;Authentication mechanisms are provided to ensure that data is kept secure from unauthorized access. Objects can be made private or public, and rights can be granted to specific users.;Options for secure data upload/download and encryption of data at rest are provided for additional data protection.;Uses standards-based REST and SOAP interfaces designed to work with any Internet-development toolkit.;Built to be flexible so that protocol or functional layers can easily be added. The default download protocol is HTTP. A BitTorrent protocol interface is provided to lower costs for high-scale distribution.;Provides functionality to simplify manageability of data through its lifetime. Includes options for segregating data by buckets, monitoring and controlling spend, and automatically archiving data to even lower cost storage options. These options can be easily administered from the Amazon S3 Management Console.;Reliability backed with the Amazon S3 Service Level Agreement.	Robust messaging for applications;Easy to use;Runs on all major operating systems;Supports a huge number of developer platforms;Open source and commercially supported	Run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk;Write applications quickly in Java, Scala or Python;Combine SQL, streaming, and complex analytics;Spark runs on Hadoop, Mesos, standalone, or in the cloud. It can access diverse data sources including HDFS, Cassandra, HBase, S3
Statistics
GitHub Stars -	GitHub Stars 13.2K	GitHub Stars 42.2K
GitHub Forks -	GitHub Forks 4.0K	GitHub Forks 28.9K
Stacks 55.1K	Stacks 21.8K	Stacks 3.1K
Followers 40.2K	Followers 18.9K	Followers 3.5K
Votes 2.0K	Votes 558	Votes 140
Pros & Cons
Pros 590 Reliable 492 Scalable 456 Cheap 329 Simple & easy 83 Many sdks Cons 7 Permissions take some time to get right 6 Requires a credit card 6 Takes time/work to organize buckets & folders properly 3 Complex to set up	Pros 235 It's fast and it works with good metrics/monitoring 80 Ease of configuration 60 I like the admin interface 52 Easy to set-up and start with 22 Durable Cons 9 Too complicated cluster/HA config and management 6 Needs Erlang runtime. Need ops good with Erlang runtime 5 Configuration must be done first, not by your code 4 Slow	Pros 61 Open-source 48 Fast and Flexible 8 Great for distributed SQL like applications 8 One platform for every big data problem 6 Easy to install and to use Cons 4 Speed

What are some alternatives to Amazon S3, RabbitMQ, Apache Spark?

Kafka

Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.

Celery

Celery is an asynchronous task queue/job queue based on distributed message passing. It is focused on real-time operation, but supports scheduling as well.

Amazon SQS

Transmit any volume of data, at any level of throughput, without losing messages or requiring other services to be always available. With SQS, you can offload the administrative burden of operating and scaling a highly available messaging cluster, while paying a low price for only what you use.

NSQ

NSQ is a realtime distributed messaging platform designed to operate at scale, handling billions of messages per day. It promotes distributed and decentralized topologies without single points of failure, enabling fault tolerance and high availability coupled with a reliable message delivery guarantee. See features & guarantees.

Amazon EBS

Amazon EBS volumes are network-attached, and persist independently from the life of an instance. Amazon EBS provides highly available, highly reliable, predictable storage volumes that can be attached to a running Amazon EC2 instance and exposed as a device within the instance. Amazon EBS is particularly suited for applications that require a database, file system, or access to raw block level storage.

ActiveMQ

Apache ActiveMQ is fast, supports many Cross Language Clients and Protocols, comes with easy to use Enterprise Integration Patterns and many advanced features while fully supporting JMS 1.1 and J2EE 1.4. Apache ActiveMQ is released under the Apache 2.0 License.

Google Cloud Storage

Google Cloud Storage allows world-wide storing and retrieval of any amount of data and at any time. It provides a simple programming interface which enables developers to take advantage of Google's own reliable and fast networking infrastructure to perform data operations in a secure and cost effective manner. If expansion needs arise, developers can benefit from the scalability provided by Google's infrastructure.

ZeroMQ

The 0MQ lightweight messaging kernel is a library which extends the standard socket interfaces with features traditionally provided by specialised messaging middleware products. 0MQ sockets provide an abstraction of asynchronous message queues, multiple messaging patterns, message filtering (subscriptions), seamless access to multiple transport protocols and more.

Presto

Distributed SQL Query Engine for Big Data

Apache NiFi

An easy to use, powerful, and reliable system to process and distribute data. It supports powerful and scalable directed graphs of data routing, transformation, and system mediation logic.

Related Comparisons

Amazon S3 vs Apache Spark vs RabbitMQ: What are the differences?

<Write Introduction here>

Scalability:
- Amazon S3 is designed to store and retrieve large amounts of data in a scalable manner, making it ideal for big data applications. On the other hand, Apache Spark is a distributed computing framework that provides an in-memory computing capability, allowing it to process large datasets efficiently. RabbitMQ, on the other hand, is a messaging broker that allows different components of a system to communicate with each other asynchronously, facilitating scalable and decoupled architectures.
Data Processing:
- Amazon S3 primarily focuses on storage and retrieval of data, providing an object storage service. Apache Spark, on the other hand, is a powerful data processing engine that can perform complex analytics and data transformations in memory, making it suitable for processing large datasets efficiently. RabbitMQ, being a messaging broker, is not designed specifically for data processing but rather for facilitating communication between different components in a system.
Real-Time Processing:
- While Amazon S3 and Apache Spark are more focused on batch processing of data, RabbitMQ excels in real-time processing by enabling seamless communication between components in a system in real-time. Apache Spark does have streaming capabilities through its Spark Streaming module, but RabbitMQ is specifically designed for real-time communication through message queues.
Programming Models:
- Amazon S3 does not offer any programming models as it is primarily a storage service. Apache Spark, on the other hand, provides various APIs and libraries for different programming languages such as Scala, Java, and Python, making it versatile for different use cases. RabbitMQ offers support for multiple programming languages as well, enabling developers to integrate messaging functionalities into their applications easily.
Fault Tolerance:
- Amazon S3 is highly fault-tolerant and durable, ensuring that data is stored redundantly across multiple servers to prevent data loss. Apache Spark also provides fault tolerance mechanisms through lineage information and RDDs (Resilient Distributed Datasets), which enable the recomputation of lost data in case of failures. RabbitMQ offers features like message acknowledgments, durable queues, and message persistence to ensure message delivery even in case of failures.
Use Cases:
- Amazon S3 is commonly used for storing static files, serving websites, and data lakes for analytics. Apache Spark is popular for data processing, machine learning, and real-time analytics. RabbitMQ is often used for decoupling systems, implementing asynchronous communication, and building scalable distributed systems.

In Summary, Amazon S3 is focused on storage, Apache Spark on data processing, and RabbitMQ on messaging, each serving specific functions in the big data ecosystem.

Amazon S3 vs Apache Spark vs RabbitMQ

Overview