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  1. Stackups
  2. Application & Data
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  4. Cloud Storage
  5. Amazon S3 vs Azure Cosmos DB vs Google BigQuery

Amazon S3 vs Azure Cosmos DB vs Google BigQuery

OverviewDecisionsComparisonAlternatives

Overview

Amazon S3
Amazon S3
Stacks55.1K
Followers40.2K
Votes2.0K
Google BigQuery
Google BigQuery
Stacks1.8K
Followers1.5K
Votes152
Azure Cosmos DB
Azure Cosmos DB
Stacks595
Followers1.1K
Votes130

Amazon S3 vs Azure Cosmos DB vs Google BigQuery: What are the differences?

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Key Differences Between Amazon S3, Azure Cosmos DB, and Google BigQuery

  1. Data Storage Model: Amazon S3 is an object storage service, Azure Cosmos DB is a NoSQL database, and Google BigQuery is a serverless, highly scalable, and cost-effective multi-cloud data warehouse. While S3 is suitable for storing unstructured data as objects, Cosmos DB offers schema-free, distributed database with support for multiple data models, and BigQuery excels in analyzing large datasets with SQL-like queries.

  2. Consistency and Availability: Amazon S3 provides high availability for storing and retrieving objects along with eventual consistency. On the other hand, Azure Cosmos DB offers five well-defined consistency models (strong, bounded-staleness, consistent-prefix, session, and eventual) with multi-region support for high availability. In comparison, Google BigQuery ensures high availability and consistency for querying and analyzing data stored in tables.

  3. Query Capabilities: Amazon S3 does not provide querying capabilities natively and requires integrating with other services like Amazon Athena or AWS Glue for querying data. In contrast, Azure Cosmos DB supports querying JSON data using SQL-like queries, MongoDB API, Gremlin API, Cassandra API, and Table API. Google BigQuery enables running super-fast SQL queries on large datasets using its serverless, highly scalable architecture.

  4. Scalability and Performance: Amazon S3 allows users to seamlessly scale storage capacity without upfront investment, but the performance depends on the object size and access patterns. Azure Cosmos DB offers elastic scalability with provisioned throughput and guarantees single-digit millisecond latencies. In the case of Google BigQuery, it automatically scales to handle any query load and optimizes query performance through columnar storage and parallel processing.

  5. Pricing Model: Amazon S3 pricing is based on usage (storage, requests, and data transfer), with various storage classes offering different levels of durability and availability at different costs. Azure Cosmos DB pricing is based on provisioned throughput and consumed storage, with additional charges for features like multi-region replication. Google BigQuery pricing is based on on-demand pricing or flat-rate pricing, with costs depending on the amount of data processed and streaming inserts.

  6. Use Cases: Amazon S3 is commonly used for data backup, archiving, content distribution, and as a data lake for analytics. Azure Cosmos DB is suitable for globally distributed applications, real-time analytics, IoT applications, and AI-driven solutions. Google BigQuery is often used for ad-hoc analysis, batch processing, machine learning, and business intelligence applications requiring complex queries on large datasets.

In Summary, Amazon S3, Azure Cosmos DB, and Google BigQuery offer distinct capabilities in terms of data storage, consistency, query capabilities, scalability, pricing models, and use cases, catering to different requirements in the cloud computing landscape.

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Advice on Amazon S3, Google BigQuery, Azure Cosmos DB

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
Julien
Julien

CTO at Hawk

Sep 19, 2020

Decided

Cloud Data-warehouse is the centerpiece of modern Data platform. The choice of the most suitable solution is therefore fundamental.

Our benchmark was conducted over BigQuery and Snowflake. These solutions seem to match our goals but they have very different approaches.

BigQuery is notably the only 100% serverless cloud data-warehouse, which requires absolutely NO maintenance: no re-clustering, no compression, no index optimization, no storage management, no performance management. Snowflake requires to set up (paid) reclustering processes, to manage the performance allocated to each profile, etc. We can also mention Redshift, which we have eliminated because this technology requires even more ops operation.

BigQuery can therefore be set up with almost zero cost of human resources. Its on-demand pricing is particularly adapted to small workloads. 0 cost when the solution is not used, only pay for the query you're running. But quickly the use of slots (with monthly or per-minute commitment) will drastically reduce the cost of use. We've reduced by 10 the cost of our nightly batches by using flex slots.

Finally, a major advantage of BigQuery is its almost perfect integration with Google Cloud Platform services: Cloud functions, Dataflow, Data Studio, etc.

BigQuery is still evolving very quickly. The next milestone, BigQuery Omni, will allow to run queries over data stored in an external Cloud platform (Amazon S3 for example). It will be a major breakthrough in the history of cloud data-warehouses. Omni will compensate a weakness of BigQuery: transferring data in near real time from S3 to BQ is not easy today. It was even simpler to implement via Snowflake's Snowpipe solution.

We also plan to use the Machine Learning features built into BigQuery to accelerate our deployment of Data-Science-based projects. An opportunity only offered by the BigQuery solution

193k views193k
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
Google BigQuery
Google BigQuery
Azure Cosmos DB
Azure Cosmos DB

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

Run super-fast, SQL-like queries against terabytes of data in seconds, using the processing power of Google's infrastructure. Load data with ease. Bulk load your data using Google Cloud Storage or stream it in. Easy access. Access BigQuery by using a browser tool, a command-line tool, or by making calls to the BigQuery REST API with client libraries such as Java, PHP or Python.

Azure DocumentDB is a fully managed NoSQL database service built for fast and predictable performance, high availability, elastic scaling, global distribution, and ease of development.

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.
All behind the scenes- Your queries can execute asynchronously in the background, and can be polled for status.;Import data with ease- Bulk load your data using Google Cloud Storage or stream it in bursts of up to 1,000 rows per second.;Affordable big data- The first Terabyte of data processed each month is free.;The right interface- Separate interfaces for administration and developers will make sure that you have access to the tools you need.
Fully managed with 99.99% Availability SLA;Elastically and highly scalable (both throughput and storage);Predictable low latency: <10ms @ P99 reads and <15ms @ P99 fully-indexed writes;Globally distributed with multi-region replication;Rich SQL queries over schema-agnostic automatic indexing;JavaScript language integrated multi-record ACID transactions with snapshot isolation;Well-defined tunable consistency models: Strong, Bounded Staleness, Session, and Eventual
Statistics
Stacks
55.1K
Stacks
1.8K
Stacks
595
Followers
40.2K
Followers
1.5K
Followers
1.1K
Votes
2.0K
Votes
152
Votes
130
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
  • 28
    High Performance
  • 25
    Easy to use
  • 22
    Fully managed service
  • 19
    Cheap Pricing
  • 16
    Process hundreds of GB in seconds
Cons
  • 1
    You can't unit test changes in BQ data
  • 0
    Sdas
Pros
  • 28
    Best-of-breed NoSQL features
  • 22
    High scalability
  • 15
    Globally distributed
  • 14
    Automatic indexing over flexible json data model
  • 10
    Always on with 99.99% availability sla
Cons
  • 18
    Pricing
  • 4
    Poor No SQL query support
Integrations
No integrations available
Xplenty
Xplenty
Fluentd
Fluentd
Looker
Looker
Chartio
Chartio
Treasure Data
Treasure Data
Azure Machine Learning
Azure Machine Learning
MongoDB
MongoDB
Hadoop
Hadoop
Java
Java
Azure Functions
Azure Functions
Azure Container Service
Azure Container Service
Azure Storage
Azure Storage
Azure Websites
Azure Websites
Apache Spark
Apache Spark
Python
Python

What are some alternatives to Amazon S3, Google BigQuery, Azure Cosmos DB?

Amazon DynamoDB

Amazon DynamoDB

With it , you can offload the administrative burden of operating and scaling a highly available distributed database cluster, while paying a low price for only what you use.

Cloud Firestore

Cloud Firestore

Cloud Firestore is a NoSQL document database that lets you easily store, sync, and query data for your mobile and web apps - at global scale.

Amazon Redshift

Amazon Redshift

It is optimized for data sets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions.

Amazon EBS

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.

Google Cloud Storage

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.

Qubole

Qubole

Qubole is a cloud based service that makes big data easy for analysts and data engineers.

Amazon EMR

Amazon EMR

It is used in a variety of applications, including log analysis, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics.

Azure Storage

Azure Storage

Azure Storage provides the flexibility to store and retrieve large amounts of unstructured data, such as documents and media files with Azure Blobs; structured nosql based data with Azure Tables; reliable messages with Azure Queues, and use SMB based Azure Files for migrating on-premises applications to the cloud.

Minio

Minio

Minio is an object storage server compatible with Amazon S3 and licensed under Apache 2.0 License

OpenEBS

OpenEBS

OpenEBS allows you to treat your persistent workload containers, such as DBs on containers, just like other containers. OpenEBS itself is deployed as just another container on your host.

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