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

Amazon S3 vs Google BigQuery

OverviewComparisonAlternatives

Overview

Amazon S3
Amazon S3
Stacks55.1K
Followers40.2K
Votes2.0K
Google BigQuery
Google BigQuery
Stacks1.8K
Followers1.5K
Votes152

Amazon S3 vs Google BigQuery: What are the differences?

Amazon S3 (Simple Storage Service) and Google BigQuery are two popular cloud storage and data analytics services that offer various features and capabilities. While both services are designed to handle large volumes of data, there are several key differences between them. Below are the key differences between Amazon S3 and Google BigQuery.
  1. Data Storage and Retrieval: Amazon S3 is primarily designed as a scalable object storage service, allowing users to store and retrieve any amount of data. It provides simple APIs to upload and download files, making it suitable for storing unstructured data or files such as images, videos, and documents. On the other hand, Google BigQuery is a fully managed, serverless, and highly scalable data warehouse that focuses on providing fast and interactive analysis of structured and semi-structured data. It supports SQL queries and provides advanced capabilities like automatic data partitioning and clustering for efficient data retrieval.

  2. Pricing Model: Amazon S3 follows a pay-as-you-go pricing model, where users are billed based on the amount of data stored and the number of requests made for data retrieval. It also offers different storage classes with varying costs and durability options. In contrast, Google BigQuery has a pricing model based on the amount of data processed during query execution. Users are billed for the quantity of data scanned by their queries, with separate pricing for storage and data processing.

  3. Querying and Analytics: While both Amazon S3 and Google BigQuery allow users to analyze data, they have different approaches to querying and analytics. In Amazon S3, users need to use additional tools or frameworks like Apache Spark or Amazon Athena to process and analyze the data stored in S3. On the other hand, Google BigQuery provides a built-in, fully managed SQL engine that allows users to run fast and complex queries directly on the data stored in BigQuery, without the need for any additional tools.

  4. Data Partitioning and Clustering: Google BigQuery provides built-in capabilities for automatically partitioning and clustering data, which helps improve query performance and reduce costs. Users can define partitioning columns based on date or other criteria, allowing BigQuery to efficiently scan only the relevant data partitions during query execution. Amazon S3 does not have built-in partitioning and clustering capabilities and requires users to manually organize the data to achieve similar benefits.

  5. Data Processing Capabilities: While Amazon S3 mainly focuses on data storage and retrieval, Google BigQuery offers more advanced data processing capabilities. BigQuery supports data transformation operations like JOINs, aggregations, and window functions, making it suitable for complex analytics and reporting tasks. It also provides integration with Google Cloud's ecosystem of services, enabling users to leverage other services like Google Data Studio for visualizing data.

  6. Integration with Ecosystem: Both Amazon S3 and Google BigQuery can be integrated with various other services and tools, but their ecosystem integration differs. Amazon S3 is tightly integrated with other Amazon Web Services (AWS) services, such as Amazon EC2, Amazon Redshift, and Amazon EMR, making it suitable for building complex data pipelines and workflows within the AWS ecosystem. On the other hand, Google BigQuery is part of the Google Cloud Platform (GCP) and integrates well with other services like Google Cloud Storage, Google Cloud Dataproc, and Google Cloud Dataflow, providing a comprehensive data analytics and processing solution within the GCP ecosystem.

In Summary, Amazon S3 is a scalable object storage service with various storage classes, while Google BigQuery is a fully managed data warehouse with advanced querying and analytics capabilities. S3 focuses on data storage and retrieval, while BigQuery provides built-in querying and data processing capabilities.

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Detailed Comparison

Amazon S3
Amazon S3
Google BigQuery
Google BigQuery

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.

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.
Statistics
Stacks
55.1K
Stacks
1.8K
Followers
40.2K
Followers
1.5K
Votes
2.0K
Votes
152
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
Integrations
No integrations available
Xplenty
Xplenty
Fluentd
Fluentd
Looker
Looker
Chartio
Chartio
Treasure Data
Treasure Data

What are some alternatives to Amazon S3, Google BigQuery?

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.

Altiscale

Altiscale

we run Apache Hadoop for you. We not only deploy Hadoop, we monitor, manage, fix, and update it for you. Then we take it a step further: We monitor your jobs, notify you when something’s wrong with them, and can help with tuning.

Snowflake

Snowflake

Snowflake eliminates the administration and management demands of traditional data warehouses and big data platforms. Snowflake is a true data warehouse as a service running on Amazon Web Services (AWS)—no infrastructure to manage and no knobs to turn.

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