Amazon Machine Learning vs Elasticsearch

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Amazon Machine Learning

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Elasticsearch

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Amazon Machine Learning vs Elasticsearch: What are the differences?

Developers describe Amazon Machine Learning as "Visualization tools and wizards that guide you through the process of creating ML models w/o having to learn complex ML algorithms & technology". This new AWS service helps you to use all of that data you’ve been collecting to improve the quality of your decisions. You can build and fine-tune predictive models using large amounts of data, and then use Amazon Machine Learning to make predictions (in batch mode or in real-time) at scale. You can benefit from machine learning even if you don’t have an advanced degree in statistics or the desire to setup, run, and maintain your own processing and storage infrastructure. On the other hand, Elasticsearch is detailed as "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 Machine Learning belongs to "Machine Learning as a Service" category of the tech stack, while Elasticsearch can be primarily classified under "Search as a Service".

Some of the features offered by Amazon Machine Learning are:

  • Easily Create Machine Learning Models
  • From Models to Predictions in Seconds
  • Scalable, High Performance Prediction Generation Service

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

Elasticsearch is an open source tool with 41.9K GitHub stars and 14K GitHub forks. Here's a link to Elasticsearch's open source repository on GitHub.

Instacart, Slack, and Stack Exchange are some of the popular companies that use Elasticsearch, whereas Amazon Machine Learning is used by Apli, Cymatic Security, and FetchyFox. Elasticsearch has a broader approval, being mentioned in 1976 company stacks & 936 developers stacks; compared to Amazon Machine Learning, which is listed in 8 company stacks and 9 developer stacks.

Advice on Amazon Machine Learning and Elasticsearch
André Ribeiro
at Federal University of Rio de Janeiro · | 4 upvotes · 18.2K views

Hi, community, I'm planning to build a web service that will perform a text search in a data set off less than 3k well-structured JSON objects containing config data. I'm expecting no more than 20 MB of data. The general traits I need for this search are: - Typo tolerant (fuzzy query), so it has to match the entries even though the query does not match 100% with a word on that JSON - Allow a strict match mode - Perform the search through all the JSON values (it can reach 6 nesting levels) - Ignore all Keys of the JSON; I'm interested only in the values.

The only thing I'm researching at the moment is Elasticsearch, and since the rest of the stack is on AWS the Amazon ElasticSearch is my favorite candidate so far. Although, the only knowledge I have on it was fetched from some articles and Q&A that I read here and there. Is ElasticSearch a good path for this project? I'm also considering Amazon DynamoDB (which I also don't know of), but it does not look to cover the requirements of fuzzy-search and ignore the JSON properties. Thank you in advance for your precious advice!

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Replies (3)
Julien DeFrance
Principal Software Engineer at Tophatter · | 3 upvotes · 12K views

The Amazon Elastic Search service will certainly help you do most of the heavy lifting and you won't have to maintain any of the underlying infrastructure. However, elastic search isn't trivial in nature. Typically, this will mean several days worth of work.

Over time and projects, I've over the years leveraged another solution called Algolia Search. Algolia is a fully managed, search as a service solution, which also has SDKs available for most common languages, will answer your fuzzy search requirements, and also cut down implementation and maintenance costs significantly. You should be able to get a solution up and running within a couple of minutes to an hour.

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Ted Elliott

I think elasticsearch should be a great fit for that use case. Using the AWS version will make your life easier. With such a small dataset you may also be able to use an in process library for searching and possibly remove the overhead of using a database. I don’t if it fits the bill, but you may also want to look into lucene.

I can tell you that Dynamo DB is definitely not a good fit for your use case. There is no fuzzy matching feature and you would need to have an index for each field you want to search or convert your data into a more searchable format for storing in Dynamo, which is something a full text search tool like elasticsearch is going to do for you.

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Roel van den Brand
Lead Developer at Di-Vision Consultion · | 3 upvotes · 13.3K views
Recommends
Amazon Athena

Maybe you can do it with storing on S3, and query via Amazon Athena en AWS Glue. Don't know about the performance though. Fuzzy search could otherwise be done with storing a soundex value of the fields you want to search on in a MongoDB. In DynamoDB you would need indexes on every searchable field if you want it to be efficient.

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Rana Usman Shahid
Chief Technology Officer at TechAvanza · | 5 upvotes · 154.2K views
Needs advice
on
Firebase
Elasticsearch
and
Algolia

Hey everybody! (1) I am developing an android application. I have data of around 3 million record (less than a TB). I want to save that data in the cloud. Which company provides the best cloud database services that would suit my scenario? It should be secured, long term useable, and provide better services. I decided to use Firebase Realtime database. Should I stick with Firebase or are there any other companies that provide a better service?

(2) I have the functionality of searching data in my app. Same data (less than a TB). Which search solution should I use in this case? I found Elasticsearch and Algolia search. It should be secure and fast. If any other company provides better services than these, please feel free to suggest them.

Thank you!

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Replies (2)
Josh Dzielak
Co-Founder & CTO at Orbit · | 7 upvotes · 117K views
Recommends
Algolia

Hi Rana, good question! From my Firebase experience, 3 million records is not too big at all, as long as the cost is within reason for you. With Firebase you will be able to access the data from anywhere, including an android app, and implement fine-grained security with JSON rules. The real-time-ness works perfectly. As a fully managed database, Firebase really takes care of everything. The only thing to watch out for is if you need complex query patterns - Firestore (also in the Firebase family) can be a better fit there.

To answer question 2: the right answer will depend on what's most important to you. Algolia is like Firebase is that it is fully-managed, very easy to set up, and has great SDKs for Android. Algolia is really a full-stack search solution in this case, and it is easy to connect with your Firebase data. Bear in mind that Algolia does cost money, so you'll want to make sure the cost is okay for you, but you will save a lot of engineering time and never have to worry about scale. The search-as-you-type performance with Algolia is flawless, as that is a primary aspect of its design. Elasticsearch can store tons of data and has all the flexibility, is hosted for cheap by many cloud services, and has many users. If you haven't done a lot with search before, the learning curve is higher than Algolia for getting the results ranked properly, and there is another learning curve if you want to do the DevOps part yourself. Both are very good platforms for search, Algolia shines when buliding your app is the most important and you don't want to spend many engineering hours, Elasticsearch shines when you have a lot of data and don't mind learning how to run and optimize it.

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Mike Endale
Recommends
Cloud Firestore

Rana - we use Cloud Firestore at our startup. It handles many million records without any issues. It provides you the same set of features that the Firebase Realtime Database provides on top of the indexing and security trims. The only thing to watch out for is to make sure your Cloud Functions have proper exception handling and there are no infinite loop in the code. This will be too costly if not caught quickly.

For search; Algolia is a great option, but cost is a real consideration. Indexing large number of records can be cost prohibitive for most projects. Elasticsearch is a solid alternative, but requires a little additional work to configure and maintain if you want to self-host.

Hope this helps.

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Decisions about Amazon Machine Learning and Elasticsearch
Phillip Manwaring
Developer at Coach Align · | 5 upvotes · 13.4K views

The new pricing model Algolia introduced really sealed the deal for us on this one - much closer to pay-as-you-go. And didn't want to spend time learning more about hosting/optimizing Elasticsearch when that isn't our core business problem - would much rather pay others to solve that problem for us.

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Pros of Amazon Machine Learning
Pros of Elasticsearch
    Be the first to leave a pro
    • 321
      Powerful api
    • 311
      Great search engine
    • 231
      Open source
    • 213
      Restful
    • 200
      Near real-time search
    • 96
      Free
    • 83
      Search everything
    • 54
      Easy to get started
    • 45
      Analytics
    • 26
      Distributed
    • 6
      Fast search
    • 5
      More than a search engine
    • 3
      Great docs
    • 3
      Awesome, great tool
    • 3
      Easy to scale
    • 2
      Intuitive API
    • 2
      Great piece of software
    • 2
      Fast
    • 2
      Nosql DB
    • 2
      Easy setup
    • 2
      Highly Available
    • 2
      Document Store
    • 2
      Great customer support
    • 1
      Reliable
    • 1
      Not stable
    • 1
      Potato
    • 1
      Open
    • 1
      Github
    • 1
      Elaticsearch
    • 1
      Actively developing
    • 1
      Responsive maintainers on GitHub
    • 1
      Ecosystem
    • 1
      Scalability
    • 0
      Easy to get hot data
    • 0
      Community

    Sign up to add or upvote prosMake informed product decisions

    Cons of Amazon Machine Learning
    Cons of Elasticsearch
      Be the first to leave a con
      • 6
        Resource hungry
      • 6
        Diffecult to get started
      • 5
        Expensive
      • 3
        Hard to keep stable at large scale

      Sign up to add or upvote consMake informed product decisions

      - No public GitHub repository available -

      What is Amazon Machine Learning?

      This new AWS service helps you to use all of that data you’ve been collecting to improve the quality of your decisions. You can build and fine-tune predictive models using large amounts of data, and then use Amazon Machine Learning to make predictions (in batch mode or in real-time) at scale. You can benefit from machine learning even if you don’t have an advanced degree in statistics or the desire to setup, run, and maintain your own processing and storage infrastructure.

      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).

      Need advice about which tool to choose?Ask the StackShare community!

      What companies use Amazon Machine Learning?
      What companies use Elasticsearch?
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      What tools integrate with Amazon Machine Learning?
      What tools integrate with Elasticsearch?
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        Blog Posts

        May 21 2019 at 12:20AM

        Elastic

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        What are some alternatives to Amazon Machine Learning and Elasticsearch?
        TensorFlow
        TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
        Apache Spark
        Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
        Amazon SageMaker
        A fully-managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale.
        RapidMiner
        It is a software platform for data science teams that unites data prep, machine learning, and predictive model deployment.
        Azure Machine Learning
        Azure Machine Learning is a fully-managed cloud service that enables data scientists and developers to efficiently embed predictive analytics into their applications, helping organizations use massive data sets and bring all the benefits of the cloud to machine learning.
        See all alternatives