Amazon Glacier vs Elasticsearch

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Amazon Glacier
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Amazon Glacier vs Elasticsearch: What are the differences?

Amazon Glacier: Low-cost storage service that provides secure and durable storage for data archiving and backup. 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; Elasticsearch: Open Source, Distributed, RESTful Search Engine. Elasticsearch is a distributed, RESTful search and analytics engine capable of storing data and searching it in near real time. Elasticsearch, Kibana, Beats and Logstash are the Elastic Stack (sometimes called the ELK Stack).

Amazon Glacier and Elasticsearch are primarily classified as "Data Backup" and "Search as a Service" tools respectively.

Some of the features offered by Amazon Glacier are:

  • Low cost – Amazon Glacier is an extremely low-cost, pay-as-you-go storage service that can cost as little as $0.01 per gigabyte per month.
  • You store data in Amazon Glacier as archives. An archive can represent a single file or you may choose to combine several files to be uploaded as a single archive. Retrieving archives from Amazon Glacier requires the initiation of a job. Jobs typically complete in 3 to 5 hours. You organize your archives in vaults.
  • Secure – Amazon Glacier supports secure transfer of your data over Secure Sockets Layer (SSL) and automatically stores data encrypted at rest using Advanced Encryption Standard (AES) 256, a secure symmetric-key encryption standard using 256-bit encryption keys.

On the other hand, Elasticsearch provides the following key features:

  • Distributed and Highly Available Search Engine.
  • Multi Tenant with Multi Types.
  • Various set of APIs including RESTful

"Cold Storage" is the primary reason why developers consider Amazon Glacier over the competitors, whereas "Powerful api" was stated as the key factor in picking Elasticsearch.

Elasticsearch is an open source tool with 42.4K GitHub stars and 14.2K GitHub forks. Here's a link to Elasticsearch's open source repository on GitHub.

According to the StackShare community, Elasticsearch has a broader approval, being mentioned in 2002 company stacks & 977 developers stacks; compared to Amazon Glacier, which is listed in 24 company stacks and 9 developer stacks.

- No public GitHub repository available -

What is 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.

What is Elasticsearch?

Elasticsearch is a distributed, RESTful search and analytics engine capable of storing data and searching it in near real time. Elasticsearch, Kibana, Beats and Logstash are the Elastic Stack (sometimes called the ELK Stack).
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      What are some alternatives to Amazon Glacier and Elasticsearch?
      Google Drive
      The Drive SDK gives you a group of APIs along with client libraries, language-specific examples, and documentation to help you develop apps that integrate with Drive. The core functionality of Drive apps is to download and upload files in Google Drive. However, the Drive SDK provides a lot more than just storage.
      Amazon S3
      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
      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.
      CrashPlan
      It runs continually in the background of a device, providing constant backup of new files. Any time a new file is created or an existing file is changed, the product adds the file to a "to do" list.
      AWS Storage Gateway
      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.
      See all alternatives
      Decisions about Amazon Glacier and Elasticsearch
      Tim Specht
      Tim Specht
      ‎Co-Founder and CTO at Dubsmash · | 16 upvotes · 54.5K views
      atDubsmashDubsmash
      Memcached
      Memcached
      Algolia
      Algolia
      Elasticsearch
      Elasticsearch
      #SearchAsAService

      Although we were using Elasticsearch in the beginning to power our in-app search, we moved this part of our processing over to Algolia a couple of months ago; this has proven to be a fantastic choice, letting us build search-related features with more confidence and speed.

      Elasticsearch is only used for searching in internal tooling nowadays; hosting and running it reliably has been a task that took up too much time for us in the past and fine-tuning the results to reach a great user-experience was also never an easy task for us. With Algolia we can flexibly change ranking methods on the fly and can instead focus our time on fine-tuning the experience within our app.

      Memcached is used in front of most of the API endpoints to cache responses in order to speed up response times and reduce server-costs on our side.

      #SearchAsAService

      See more
      Julien DeFrance
      Julien DeFrance
      Full Stack Engineering Manager at ValiMail · | 16 upvotes · 282.5K views
      atSmartZipSmartZip
      Amazon DynamoDB
      Amazon DynamoDB
      Ruby
      Ruby
      Node.js
      Node.js
      AWS Lambda
      AWS Lambda
      New Relic
      New Relic
      Amazon Elasticsearch Service
      Amazon Elasticsearch Service
      Elasticsearch
      Elasticsearch
      Superset
      Superset
      Amazon Quicksight
      Amazon Quicksight
      Amazon Redshift
      Amazon Redshift
      Zapier
      Zapier
      Segment
      Segment
      Amazon CloudFront
      Amazon CloudFront
      Memcached
      Memcached
      Amazon ElastiCache
      Amazon ElastiCache
      Amazon RDS for Aurora
      Amazon RDS for Aurora
      MySQL
      MySQL
      Amazon RDS
      Amazon RDS
      Amazon S3
      Amazon S3
      Docker
      Docker
      Capistrano
      Capistrano
      AWS Elastic Beanstalk
      AWS Elastic Beanstalk
      Rails API
      Rails API
      Rails
      Rails
      Algolia
      Algolia

      Back in 2014, I was given an opportunity to re-architect SmartZip Analytics platform, and flagship product: SmartTargeting. This is a SaaS software helping real estate professionals keeping up with their prospects and leads in a given neighborhood/territory, finding out (thanks to predictive analytics) who's the most likely to list/sell their home, and running cross-channel marketing automation against them: direct mail, online ads, email... The company also does provide Data APIs to Enterprise customers.

      I had inherited years and years of technical debt and I knew things had to change radically. The first enabler to this was to make use of the cloud and go with AWS, so we would stop re-inventing the wheel, and build around managed/scalable services.

      For the SaaS product, we kept on working with Rails as this was what my team had the most knowledge in. We've however broken up the monolith and decoupled the front-end application from the backend thanks to the use of Rails API so we'd get independently scalable micro-services from now on.

      Our various applications could now be deployed using AWS Elastic Beanstalk so we wouldn't waste any more efforts writing time-consuming Capistrano deployment scripts for instance. Combined with Docker so our application would run within its own container, independently from the underlying host configuration.

      Storage-wise, we went with Amazon S3 and ditched any pre-existing local or network storage people used to deal with in our legacy systems. On the database side: Amazon RDS / MySQL initially. Ultimately migrated to Amazon RDS for Aurora / MySQL when it got released. Once again, here you need a managed service your cloud provider handles for you.

      Future improvements / technology decisions included:

      Caching: Amazon ElastiCache / Memcached CDN: Amazon CloudFront Systems Integration: Segment / Zapier Data-warehousing: Amazon Redshift BI: Amazon Quicksight / Superset Search: Elasticsearch / Amazon Elasticsearch Service / Algolia Monitoring: New Relic

      As our usage grows, patterns changed, and/or our business needs evolved, my role as Engineering Manager then Director of Engineering was also to ensure my team kept on learning and innovating, while delivering on business value.

      One of these innovations was to get ourselves into Serverless : Adopting AWS Lambda was a big step forward. At the time, only available for Node.js (Not Ruby ) but a great way to handle cost efficiency, unpredictable traffic, sudden bursts of traffic... Ultimately you want the whole chain of services involved in a call to be serverless, and that's when we've started leveraging Amazon DynamoDB on these projects so they'd be fully scalable.

      See more
      Interest over time
      Reviews of Amazon Glacier and Elasticsearch
      No reviews found
      How developers use Amazon Glacier and Elasticsearch
      Avatar of imgur
      imgur uses ElasticsearchElasticsearch

      Elasticsearch is the engine that powers search on the site. From a high level perspective, it’s a Lucene wrapper that exposes Lucene’s features via a RESTful API. It handles the distribution of data and simplifies scaling, among other things.

      Given that we are on AWS, we use an AWS cloud plugin for Elasticsearch that makes it easy to work in the cloud. It allows us to add nodes without much hassle. It will take care of figuring out if a new node has joined the cluster, and, if so, Elasticsearch will proceed to move data to that new node. It works the same way when a node goes down. It will remove that node based on the AWS cluster configuration.

      Avatar of Instacart
      Instacart uses ElasticsearchElasticsearch

      The very first version of the search was just a Postgres database query. It wasn’t terribly efficient, and then at some point, we moved over to ElasticSearch, and then since then, Andrew just did a lot of work with it, so ElasticSearch is amazing, but out of the box, it doesn’t come configured with all the nice things that are there, but you spend a lot of time figuring out how to put it all together to add stemming, auto suggestions, all kinds of different things, like even spelling adjustments and tomato/tomatoes, that would return different results, so Andrew did a ton of work to make it really, really nice and build a very simple Ruby gem called SearchKick.

      Avatar of AngeloR
      AngeloR uses ElasticsearchElasticsearch

      We use ElasticSearch for

      • Session Logs
      • Analytics
      • Leaderboards

      We originally self managed the ElasticSearch clusters, but due to our small ops team size we opt to move things to managed AWS services where possible.

      The managed servers, however, do not allow us to manage our own backups and a restore actually requires us to open a support ticket with them. We ended up setting up our own nightly backup since we do per day indexes for the logs/analytics.

      Avatar of Brandon Adams
      Brandon Adams uses ElasticsearchElasticsearch

      Elasticsearch has good tooling and supports a large api that makes it ideal for denormalizing data. It has a simple to use aggregations api that tends to encompass most of what I need a BI tool to do, especially in the early going (when paired with Kibana). It's also handy when you just want to search some text.

      Avatar of Ana Phi Sancho
      Ana Phi Sancho uses ElasticsearchElasticsearch

      Self taught : acquired knowledge or skill on one's own initiative. Open Source Search & Analytics. -time search and analytics engine. Search engine based on Lucene. It provides a distributed, multitenant-capable full-text search engine with an HTTP web interface and schema-free JSON documents.

      Avatar of Loog
      Loog uses Amazon GlacierAmazon Glacier

      All data is backed up and archived into Amazon Glacier.

      Avatar of Vaultize
      Vaultize uses Amazon GlacierAmazon Glacier

      Cold storage in Vaultize public cloud.

      Avatar of Mohamed El-Kalioby
      Mohamed El-Kalioby uses Amazon GlacierAmazon Glacier

      Keep Data Backup

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      How much does Elasticsearch cost?
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