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  1. Stackups
  2. Application & Data
  3. Infrastructure as a Service
  4. Cloud Storage
  5. Amazon S3 vs Hadoop vs Serverless

Amazon S3 vs Hadoop vs Serverless

OverviewDecisionsComparisonAlternatives

Overview

Amazon S3
Amazon S3
Stacks55.1K
Followers40.2K
Votes2.0K
Hadoop
Hadoop
Stacks2.7K
Followers2.3K
Votes56
GitHub Stars15.3K
Forks9.1K
Serverless
Serverless
Stacks2.2K
Followers1.2K
Votes28
GitHub Stars46.9K
Forks5.7K

Amazon S3 vs Hadoop vs Serverless: What are the differences?

# Introduction

1. **Data Storage**:
   - Amazon S3 is a storage service offered by Amazon Web Services, primarily used for object storage, while Hadoop is a distributed file system for storing and processing large datasets, and serverless architecture can utilize cloud storage services like Amazon S3 for storage purposes. 

2. **Data Processing**:
   - Hadoop provides a distributed computing framework for processing large datasets, while Amazon S3 does not have built-in processing capabilities, and serverless architecture relies on event-driven functions for data processing.

3. **Scalability**:
   - Amazon S3 is highly scalable for storing large amounts of data and can handle high throughput, Hadoop is also scalable due to its distributed nature, while serverless architecture automatically scales based on the demand by executing functions in response to events.

4. **Ease of Management**:
   - Amazon S3 requires minimal management as a fully-managed service, Hadoop clusters require more administrative efforts for monitoring and maintenance, and serverless architecture does not require any server management, allowing developers to focus on writing code.

5. **Cost Efficiency**:
   - Amazon S3 offers pay-as-you-go pricing, making it cost-effective for storage, Hadoop can be expensive to set up and maintain due to the infrastructure requirements, while serverless architecture can be cost-efficient as it charges users only for the resources consumed during the function execution.

6. **Real-time Processing**:
   - Amazon S3 is more suitable for storing static data and is not optimized for real-time processing, Hadoop can handle batch processing of data but may not be efficient for real-time processing, while serverless architecture can be used for real-time processing by triggering functions in response to events.

In Summary, Amazon S3, Hadoop, and Serverless architectures differ in terms of their primary use cases, scalability, management, cost efficiency, and real-time processing capabilities.

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Advice on Amazon S3, Hadoop, Serverless

Tim
Tim

CTO at Checkly Inc.

Sep 18, 2019

Needs adviceonHerokuHerokuAWS LambdaAWS Lambda

When adding a new feature to Checkly rearchitecting some older piece, I tend to pick Heroku for rolling it out. But not always, because sometimes I pick AWS Lambda . The short story:

  • Developer Experience trumps everything.
  • AWS Lambda is cheap. Up to a limit though. This impact not only your wallet.
  • If you need geographic spread, AWS is lonely at the top.

The setup

Recently, I was doing a brainstorm at a startup here in Berlin on the future of their infrastructure. They were ready to move on from their initial, almost 100% Ec2 + Chef based setup. Everything was on the table. But we crossed out a lot quite quickly:

  • Pure, uncut, self hosted Kubernetes — way too much complexity
  • Managed Kubernetes in various flavors — still too much complexity
  • Zeit — Maybe, but no Docker support
  • Elastic Beanstalk — Maybe, bit old but does the job
  • Heroku
  • Lambda

It became clear a mix of PaaS and FaaS was the way to go. What a surprise! That is exactly what I use for Checkly! But when do you pick which model?

I chopped that question up into the following categories:

  • Developer Experience / DX 🤓
  • Ops Experience / OX 🐂 (?)
  • Cost 💵
  • Lock in 🔐

Read the full post linked below for all details

357k views357k
Comments
Mohammad
Mohammad

Aug 30, 2020

Needs adviceonBackblaze B2 Cloud StorageBackblaze B2 Cloud StoragePHPPHPLaravelLaravel

Hello! I have a mobile app with nearly 100k MAU, and I want to add a cloud file storage service to my app.

My app will allow users to store their image, video, and audio files and retrieve them to their device when necessary.

I have already decided to use PHP & Laravel as my backend, and I use Contabo VPS. Now, I need an object storage service for my app, and my options are:

  • Amazon S3 : It sounds to me like the best option but the most expensive. Closest to my users (MENA Region) for other services, I will have to go to Europe. Not sure how important this is?

  • DigitalOcean Spaces : Seems like my best option for price/service, but I am still not sure

  • Wasabi: the best price (6 USD/MONTH/TB) and free bandwidth, but I am not sure if it fits my needs as I want to allow my users to preview audio and video files. They don't recommend their service for streaming videos.

  • Backblaze B2 Cloud Storage: Good price but not sure about them.

  • There is also the self-hosted s3 compatible option, but I am not sure about that.

Any thoughts will be helpful. Also, if you think I should post in a different sub, please tell me.

180k views180k
Comments
Dalton
Dalton

Oct 23, 2020

Decided

Minio is a free and open source object storage system. It can be self-hosted and is S3 compatible. During the early stage it would save cost and allow us to move to a different object storage when we scale up. It is also fast and easy to set up. This is very useful during development since it can be run on localhost.

143k views143k
Comments

Detailed Comparison

Amazon S3
Amazon S3
Hadoop
Hadoop
Serverless
Serverless

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

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.

Build applications comprised of microservices that run in response to events, auto-scale for you, and only charge you when they run. This lowers the total cost of maintaining your apps, enabling you to build more logic, faster. The Framework uses new event-driven compute services, like AWS Lambda, Google CloudFunctions, and more.

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.
--
Statistics
GitHub Stars
-
GitHub Stars
15.3K
GitHub Stars
46.9K
GitHub Forks
-
GitHub Forks
9.1K
GitHub Forks
5.7K
Stacks
55.1K
Stacks
2.7K
Stacks
2.2K
Followers
40.2K
Followers
2.3K
Followers
1.2K
Votes
2.0K
Votes
56
Votes
28
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
    Takes time/work to organize buckets & folders properly
  • 6
    Requires a credit card
  • 3
    Complex to set up
Pros
  • 39
    Great ecosystem
  • 11
    One stack to rule them all
  • 4
    Great load balancer
  • 1
    Java syntax
  • 1
    Amazon aws
Pros
  • 14
    API integration
  • 7
    Supports cloud functions for Google, Azure, and IBM
  • 3
    Lower cost
  • 1
    Openwhisk
  • 1
    Auto scale
Integrations
No integrations availableNo integrations available
Azure Functions
Azure Functions
AWS Lambda
AWS Lambda
Amazon API Gateway
Amazon API Gateway

What are some alternatives to Amazon S3, Hadoop, Serverless?

MongoDB

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.

MySQL

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

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.

Microsoft SQL Server

Microsoft SQL Server

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.

SQLite

SQLite

SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file.

Cassandra

Cassandra

Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.

Memcached

Memcached

Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering.

MariaDB

MariaDB

Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. MariaDB is designed as a drop-in replacement of MySQL(R) with more features, new storage engines, fewer bugs, and better performance.

AWS Lambda

AWS Lambda

AWS Lambda is a compute service that runs your code in response to events and automatically manages the underlying compute resources for you. You can use AWS Lambda to extend other AWS services with custom logic, or create your own back-end services that operate at AWS scale, performance, and security.

RethinkDB

RethinkDB

RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn.

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