AWS Snowball Edge vs Amazon Kinesis Firehose

Overview

Amazon Kinesis Firehose

Stacks239

Followers185

Votes0

AWS Snowball Edge

Stacks5

Followers51

Votes1

Amazon Kinesis Firehose vs AWS Snowball Edge: What are the differences?

Amazon Kinesis Firehose: Simple and Scalable Data Ingestion. 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; AWS Snowball Edge: Petabyte-scale data transport with on-board storage and compute. AWS Snowball Edge is a 100TB data transfer device with on-board storage and compute capabilities. You can use Snowball Edge to move large amounts of data into and out of AWS, as a temporary storage tier for large local datasets, or to support local workloads in remote or offline locations.

Amazon Kinesis Firehose belongs to "Real-time Data Processing" category of the tech stack, while AWS Snowball Edge can be primarily classified under "Data Transfer".

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Advice on Amazon Kinesis Firehose, AWS Snowball Edge

Ryan

Mar 11, 2021

Decided

Because we're getting continuous data from a variety of mediums and sources, we need a way to ingest data, process it, analyze it, and store it in a robust manner. AWS' tools provide just that. They make it easy to set up a data ingestion pipeline for handling gigabytes of data per second. GraphQL makes it easy for the front end to just query an API and get results in an efficient fashion, getting only the data we need. SwaggerHub makes it easy to make standardized OpenAPI's with consistent and predictable behavior.

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Comments

Roel

Lead Developer at Di-Vision Consultion

Dec 14, 2020

Decided

Use case for ingressing a lot of data and post-process the data and forward it to multiple endpoints.

Kinesis can ingress a lot of data easier without have to manage scaling in DynamoDB (ondemand would be too expensive) We looked at DynamoDB Streams to hook up with Lambda, but Kinesis provides the same, and a backup incoming data to S3 with Firehose instead of using the TTL in DynamoDB.

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Comments

Detailed Comparison

Amazon Kinesis Firehose	AWS Snowball Edge
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.	AWS Snowball Edge is a 100TB data transfer device with on-board storage and compute capabilities. You can use Snowball Edge to move large amounts of data into and out of AWS, as a temporary storage tier for large local datasets, or to support local workloads in remote or offline locations.
Easy-to-Use;Integrated with AWS Data Stores;Automatic Elasticity;Near Real-time	-
Statistics
Stacks 239	Stacks 5
Followers 185	Followers 51
Votes 0	Votes 1
Pros & Cons
No community feedback yet	Pros 1 SBManager™ is the only commercially available GUI for t
Integrations
Amazon S3 Amazon Redshift	Amazon SNS

What are some alternatives to Amazon Kinesis Firehose, AWS Snowball Edge?

Google Cloud Dataflow

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.

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.

AWS Data Pipeline

AWS Data Pipeline is a web service that provides a simple management system for data-driven workflows. Using AWS Data Pipeline, you define a pipeline composed of the “data sources” that contain your data, the “activities” or business logic such as EMR jobs or SQL queries, and the “schedule” on which your business logic executes. For example, you could define a job that, every hour, runs an Amazon Elastic MapReduce (Amazon EMR)–based analysis on that hour’s Amazon Simple Storage Service (Amazon S3) log data, loads the results into a relational database for future lookup, and then automatically sends you a daily summary email.

Earnings Feed API

REST API for real-time SEC filings data. Access 10-K, 10-Q, 8-K filings and Form 4 insider transactions as they hit EDGAR. Filter by ticker, form type, or date range. Build alerts, power dashboards, or integrate into trading systems. Free tier available.

Requests

It is an elegant and simple HTTP library for Python, built for human beings. It allows you to send HTTP/1.1 requests extremely easily. There’s no need to manually add query strings to your URLs, or to form-encode your POST data.

NPOI

It is a .NET library that can read/write Office formats without Microsoft Office installed. No COM+, no interop.

HTTP/2

It's focus is on performance; specifically, end-user perceived latency, network and server resource usage.

Embulk

It is an open-source bulk data loader that helps data transfer between various databases, storages, file formats, and cloud services.

Google BigQuery Data Transfer Service

BigQuery Data Transfer Service lets you focus your efforts on analyzing your data. You can setup a data transfer with a few clicks. Your analytics team can lay the foundation for a data warehouse without writing a single line of code.

PieSync

A cloud-based solution engineered to fill the gaps between cloud applications. The software utilizes Intelligent 2-way Contact Sync technology to sync contacts in real-time between your favorite CRM and marketing apps.

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