Snowflake VS S3

Snowflake is a mighty cloud-based warehousing database management system. It covers a variety of technological areas like business intelligence, advanced analytics, data integration as well as security and governance. Its clients benefit from the ability to scale storage to deal with any amount of data. At the same time, Oracle is the full-featured database available in the marketplace. Oracle was started as a relational database, and relational is always a critically important part of what it does. But, Oracle also offers the option of modelling and processing data with different approaches in the same database. Oracle database products provide clients with cost-effective and efficient versions of the Oracle Database. Although both Oracle and Snowflake are powerful database management systems, there are a few features that differentiate both of them. So, Let's proceed to find out how oracle and Snowflake differ. Before we proceed with the comparison, let us briefly understand what Snowflake and Oracle are.

Snowflake vs S3 - Table of Content

What is Snowflake?

Snowflake data cloud is a powerful data warehouse management platform. It is not an addition to already existing data warehouse management platforms. Snowflake data cloud is built on top of various cloud services such as Amazon web services, Microsoft Azure, and GCP (Google cloud platform) infrastructure. One more interesting thing about Snowflake data cloud is that this platform doesn’t require any installation prerequisites like hardware or software components to select, configure, or manage. With the help of the Snowflake data cloud, it’s easy to move the data into the ETL (extract, transfer, and load) process.

What is S3 (a simple storage service)?

S3 stands for simple storage service and this is the first-ever service produced by Amazon web service. S3 file is considered to be the safest place to store the data files. S3 is object-based storage; here you can store the images, pdf files, and Word files, etc. The S3 file storage value can range from 0 bytes to 5Terra bytes. Usually, files are stored in Bucket. A bucket is like a folder that is used to store the files. S3 is a universal namespace that contains a DNS address, unique bucket name, and unique DNS address.

If you want to create the bucket, you have to use the following bucket:

https://s3-eu-west-1.amazonaws.com/acloudguru

Where s3-EU-west-1 is the region name and acloudguru is the bucket name. 

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Snowflake VS S3:

Here we are going to explain the major differences between the Snowflake and S3 that are based on a few factors. Let’s get started;

We are going to consider the following factors for comparison.

  1. Continuous data integration
  2. Consumption and exposure of data.
  3. SQL interface.
  4. Sharing of data across accounts.
  5. Compression of data.
  6. Native stack or better integration.
  7. Supported data formats.
  8. How each data lake solution updates data.

Let me explain them one by one;

  1. Continuous data integration:

  • Snowflake has an inbuilt option such as STREAMS.
  • In S3, it can be achieved using various technologies or tools available such as AWS Glue, Athena, and Spark.

     2.Consuming and exposing data:

  •  Snowflake consists of JDBC, ODBC, .NET, and GO, drivers. Additionally, it has Node.JS, Python, Spark, and Kafka data connectors. Snowflake also offers JAVA and Python APIs to simply work in REST API.
  • Whereas S3 consists of the REST API, SOAP, API (depreciated), JDBC, and ODBC connectors for JAVA Script, Python, PHP, .NET, Ruby, JAVA, C++, and for Node.JS.

    3.SQL interface:

  • Snowflake consists of various inbuilt (worksheets).
  • Whereas in S3, it needs Athena or Presto (additional cost).

    4.Sharing of data across accounts:

  • Actual data in Snowflake is not copied or shared with another account. Read-only access is provided to a consumer account. It is achieved to a consumer account. It is achieved using a simple “ share” command. That also incurs computational cost and not storage cost.
  • S3 offers an access file across accounts that can be achieved using Amazon quick sight, which incurs additional costs.

   5.Compression or data storage:

  • The snowflake automatically compresses the file as it stores the data in a columnar format in the ratio of 4:1.
  • In S3, it can be achieved manually using the EC2 machines.

  6.Native stack or better integration:

  • The Snowflake partner tools provide a better integration than other tools.
  • Whereas Amazon -S3 provides storage, and Amazon Redshift, data warehouse, Amazon Athena-quering, Amazon RDS-Database, AWS data pipeline or orchestration, etc.

   7.Supported formats:

  • In Snowflake, there are different structured, and semi-structured data (JSON, AVRO, ORC, PARQUET, and XML).
  • Whereas S3 supports Structured, unstructured, and semi-structured data formats.

   8.Data with Updates:

  • Snowflake updates the specific rows in the table with new values where the conditions match.
  • In S3, we cannot add or remove or modify the data as just a part of an existing S3 object. We should read the object, make changes to the object, and then write the entire object back to S3. We cannot update the data in S3, only we can read or rewrite the entire objects to the S3.

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Key features of the Snowflake:

Below are the salient features of the snowflake:

  • Standard and extended SQL Support.
  • Web-based graphical user interface (GUI).
  • Command-line interface support (CLI).
  • Rich set of client connectors
  • Offers extensive third-party plugins.
  • Supports bulk loading and unloading of data.
  • Offers adequate data protection and security.

Key features of S3( a simple storage service):

The following are the salient features of S3:

  • This simple storage service allows unlimited storage of objects or files containing 1 byte to 5 gigabytes each.
  •  Objects consist of storing the raw object data and metadata.
  •  Objects are stored and retrieved using a developer–assigned key.
  •  Data are kept secured from unauthorized access through an authentication mechanism.
  •  Objects can be made available to the public by the HTTP or BitTorrent protocol.     

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Advantages of the Snowflake:

The following are the key benefits of the Snowflake:

  • Management and metadata: Snowflake data cloud is an independent data warehouse management tool that manages the metadata, optimizes the data delivery, and security.
  • A computing platform: it provides multiple, independent computing clusters to process the queries.
  • Provides a world-class data storage capacity: there is a cloud data storage available that consists of the shared disk which helps to store teh persistent data. Snowflake data cloud also manages ingestion, compression, and storage.
    No limit on the number of consumer accounts with which a data set may be shared.
  • Get access to the data without any need to move or transform it.
  • Query and combine shared data with existing data or join together data from multiple publishers.

Disadvantages of the Snowflake

The below are a few drawbacks of the snowflake:

  • The snowflake does not support the data movement at the moment because its features have been designed to facilitate only structured and semi-structured data.
  • One major drawback of the snowflake data cloud is, that it supports only bulk data loading. When you want to migrate data from data files to the snowflake, at that moment, there is no such guidance or support available on bulk data loading.
  • No data constraints are available.

Advantages of the S3:

Below are a few benefits of the S3:

  •  Helps in the creation of buckets: In S3 firstly we are able to create a bucket and provide a name to the bucket. Buckets are nothing but containers in a Simple storage service that helps to store the data. The bucket must hold a unique name to create a unique DNS address.
  •  Supports storing the data in buckets: The bucket can be used to store an unlimited amount of data. So that you can upload the files (Here you can upload an infinite amount of files) into an Amazon S3 bucket. Each object can be stored and retrieved with the help of a unique developer-assigned key.
  •  Helps to download data: With help of Amazon S3, user can also download their own data from an Amazon bucket and also give permission to others while downloading the same data. Here you can download any amount of data at any time.
  •  Permissions: You can also get permission (grant or deny access permission) from any users who want to upload or download the data from the Amazon S3 bucket. The authentication mechanism keeps the data that is secured from unauthorized access.
  •  Standard interfaces: S3 is used with standard interfaces like REST and SOAP. These interfaces are designed in such a way that they can work with development tool kits.
  • Offers better security: Amazon S3 offers better security features that are used to protect unauthorized users from accessing the data.

Disadvantages of S3:

The following are the few drawbacks of S3:

  • No support for file syncing.
  • Offers only limited sharing capabilities.
  • No collaboration features are available.
  • Customers can only restore the entire drive, not separate files via the desktop application because the desktop application is limited. 
  • Unlimited storage claim is flimsy, at best.

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Final words:

In the Snowflake VS S3 article, we have tried to explain the major comparison between the tools based on various factors such as data integration, data updates, data support formats, native stack, SQL interfaces, exposing or consuming data, and sharing of the data across the accounts. Stay tuned to our website for more updates.

Related Article:

  1. Snowflake  vs Redshift
  2. Snowflake vs Oracle
  3. Snowflake vs Azure

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Gayathri
Gayathri
Research Analyst
As a senior Technical Content Writer for HKR Trainings, Gayathri has a good comprehension of the present technical innovations, which incorporates perspectives like Business Intelligence and Analytics. She conveys advanced technical ideas precisely and vividly, as conceivable to the target group, guaranteeing that the content is available to clients. She writes qualitative content in the field of Data Warehousing & ETL, Big Data Analytics, and ERP Tools. Connect me on LinkedIn.