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  1. Stackups
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  5. AWS Storage Gateway vs Google Cloud Dataflow

AWS Storage Gateway vs Google Cloud Dataflow

OverviewComparisonAlternatives

Overview

AWS Storage Gateway
AWS Storage Gateway
Stacks17
Followers59
Votes0
Google Cloud Dataflow
Google Cloud Dataflow
Stacks219
Followers497
Votes19

AWS Storage Gateway vs Google Cloud Dataflow: What are the differences?

Introduction

This markdown code provides a comparison between AWS Storage Gateway and Google Cloud Dataflow in terms of their key differences.

  1. Deployment: AWS Storage Gateway is a hybrid storage service that connects an on-premises environment with AWS storage infrastructure. It can be deployed as an on-premises appliance or a virtual machine. Google Cloud Dataflow, on the other hand, is a fully managed service provided by Google Cloud Platform that allows developers to create complex data processing pipelines. It is deployed on the Google Cloud Platform.

  2. Data Processing Model: AWS Storage Gateway primarily focuses on providing seamless integration between on-premises storage and AWS cloud storage. It provides storage and retrieval of data to/from AWS services such as Amazon S3 and Amazon Glacier. Google Cloud Dataflow, on the other hand, is a powerful data processing service that allows developers to write and execute large-scale data processing and analytics pipelines. It supports complex data transformations and processing operations.

  3. Coding Paradigm: AWS Storage Gateway is configured and managed using the AWS Management Console, which provides a graphical user interface (GUI) for easy configuration and management. Google Cloud Dataflow, on the other hand, requires developers to write code using languages such as Java or Python to define their data processing pipelines. It follows the coding paradigm, where developers define the data transformations and operations in code.

  4. Scalability: AWS Storage Gateway can scale storage capacity and throughput based on the requirements of the application. It can automatically scale up or down as needed. Google Cloud Dataflow, on the other hand, provides a fully managed service that automatically scales up or down based on processing requirements. It can handle large volumes of data and supports parallel execution of data processing tasks.

  5. Integration with other Services: AWS Storage Gateway integrates seamlessly with other AWS services such as Amazon S3, Amazon Glacier, and AWS Snowball. It allows data to be stored and accessed in these services using standard storage protocols. Google Cloud Dataflow, on the other hand, integrates with other Google Cloud Platform services such as BigQuery, Pub/Sub, and Cloud Storage. It provides easy integration with these services for data ingestion, processing, and storage.

  6. Performance Optimization: AWS Storage Gateway provides features such as local caching and volume gateway modes to optimize performance and reduce latency for on-premises applications. It allows data to be cached locally in the gateway appliance, reducing the need to fetch data from the cloud. Google Cloud Dataflow, on the other hand, optimizes performance by executing data processing pipelines in a distributed and parallel manner. It automatically optimizes resource allocation and execution to achieve high performance.

In summary, AWS Storage Gateway is primarily focused on providing hybrid storage capabilities and seamless integration with AWS cloud storage services. Google Cloud Dataflow, on the other hand, is a fully managed data processing service that allows developers to write and execute complex data processing pipelines.

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Detailed Comparison

AWS Storage Gateway
AWS Storage Gateway
Google Cloud Dataflow
Google Cloud Dataflow

The AWS Storage Gateway is a service connecting an on-premises software appliance with cloud-based storage. Once the AWS Storage Gateway’s software appliance is installed on a local host, you can mount Storage Gateway volumes to your on-premises application servers as iSCSI devices, enabling a wide variety of systems and applications to make use of them. Data written to these volumes is maintained on your on-premises storage hardware while being asynchronously backed up to AWS, where it is stored in Amazon Glacier or in Amazon S3 in the form of Amazon EBS snapshots. Snapshots are encrypted to make sure that customers do not have to worry about encrypting sensitive data themselves. When customers need to retrieve data, they can restore snapshots locally, or create Amazon EBS volumes from snapshots for use with applications running in Amazon EC2. It provides low-latency performance by maintaining frequently accessed data on-premises while securely storing all of your data encrypted.

Google Cloud Dataflow is a unified programming model and a managed service for developing and executing a wide range of data processing patterns including ETL, batch computation, and continuous computation. Cloud Dataflow frees you from operational tasks like resource management and performance optimization.

Gateway-Cached Volumes – Gateway-Cached volumes allow you to utilize Amazon S3 for your primary data, while retaining some portion of it locally in a cache for frequently accessed data.;Gateway-Stored Volumes – Gateway-Stored volumes store your primary data locally, while asynchronously backing up that data to AWS.;Data Snapshots – Gateway-Cached volumes and Gateway-Stored volumes provide the ability to create and store point-in-time snapshots of your storage volumes in Amazon S3.;Gateway-VTL – Gateway-VTL provides you with a cost-effective, scalable, and durable virtual tape infrastructure that allows you to eliminate the challenges associated with owning and operating an on-premises physical tape infrastructure.;Secure – The AWS Storage Gateway securely transfers your data to AWS over SSL and stores data encrypted at rest in Amazon S3 and Amazon Glacier using Advanced Encryption Standard (AES) 256, a secure symmetric-key encryption standard using 256-bit encryption keys.;Durably backed by Amazon S3 and Amazon Glacier –The AWS Storage Gateway durably stores your on-premises application data by uploading it to Amazon S3 and Amazon Glacier. Amazon S3 and Amazon Glacier redundantly store data in multiple facilities and on multiple devices within each facility. Amazon S3 and Amazon Glacier also perform regular, systematic data integrity checks and are built to be automatically self-healing.;Compatible – There is no need to re-architect your on-premises applications. Gateway-Cached volumes and Gateway-Stored volumes expose a standard iSCSI block disk device interface and Gateway-VTL presents a standard iSCSI virtual tape library interface.
Fully managed; Combines batch and streaming with a single API; High performance with automatic workload rebalancing Open source SDK;
Statistics
Stacks
17
Stacks
219
Followers
59
Followers
497
Votes
0
Votes
19
Pros & Cons
No community feedback yet
Pros
  • 7
    Unified batch and stream processing
  • 5
    Autoscaling
  • 4
    Fully managed
  • 3
    Throughput Transparency

What are some alternatives to AWS Storage Gateway, Google Cloud Dataflow?

Amazon Glacier

Amazon Glacier

In order to keep costs low, Amazon Glacier is optimized for data that is infrequently accessed and for which retrieval times of several hours are suitable. With Amazon Glacier, customers can reliably store large or small amounts of data for as little as $0.01 per gigabyte per month, a significant savings compared to on-premises solutions.

Amazon Kinesis

Amazon Kinesis

Amazon Kinesis can collect and process hundreds of gigabytes of data per second from hundreds of thousands of sources, allowing you to easily write applications that process information in real-time, from sources such as web site click-streams, marketing and financial information, manufacturing instrumentation and social media, and operational logs and metering data.

Amazon Kinesis Firehose

Amazon Kinesis Firehose

Amazon Kinesis Firehose is the easiest way to load streaming data into AWS. It can capture and automatically load streaming data into Amazon S3 and Amazon Redshift, enabling near real-time analytics with existing business intelligence tools and dashboards you’re already using today.

restic

restic

It is a backup program that is fast, efficient and secure. It uses cryptography to guarantee the confidentiality and integrity of your data.

Veeam Backup & Replication

Veeam Backup & Replication

It is industry-leading Backup & Replication software. It delivers availability for all your cloud, virtual and physical workloads. Through a simple-by-design management console, you can easily achieve fast, flexible and reliable backup, recovery and replication for all your applications and data.

Borg

Borg

It is a deduplicating backup program. It provides an efficient and secure way to backup data. The data deduplication technique used makes it suitable for daily backups since only changes are stored. The authenticated encryption technique makes it suitable for backups to not fully trusted targets.

Afi

Afi

Afi.ai is the latest generation of data protection for M365, Google Workspace & Kubernetes. Clean & responsive UI, easy to use SLA-based protection settings, and 2-3x better backup/restore performance compared to legacy vendors.

RainyDay Backup

RainyDay Backup

RainyDay Backup offers an easily configurable system that enables you to back up your Azure DevOps source code, Work Items and NuGet artifacts and more.

runrestic

runrestic

It is a simple Python wrapper script for the Restic backup software that initiates a backup, prunes any old backups according to a retention policy, and validates backups for consistency. The script supports specifying your settings in a declarative configuration file rather than having to put them all on the command-line, and handles common errors.

Cohesity

Cohesity

It allows IT professionals to backup, manage and gain insights from their data, across multiple systems or cloud providers.

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