Amazon Athena vs Elasticsearch

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Amazon Athena vs Elasticsearch: What are the differences?

Introduction

In this article, we will explore the key differences between Amazon Athena and Elasticsearch, two popular data analytics tools.

  1. Data Storage and Querying Capabilities: Amazon Athena is a serverless interactive query service that allows you to analyze data directly in Amazon S3 using standard SQL. It is best suited for querying structured and semi-structured data. On the other hand, Elasticsearch is a distributed search and analytics engine that enables real-time data search, exploration, and analysis across various types of data. It is designed for full-text search and is well-suited for unstructured or semi-structured data.

  2. Scalability and Performance: Amazon Athena can handle massive amounts of data as it leverages the power of distributed computing provided by AWS infrastructure. It automatically scales based on the size of the dataset and the complexity of the queries. In contrast, Elasticsearch is highly scalable and can handle large volumes of data efficiently. It is designed to distribute and parallelize search and analytical queries across a cluster of nodes, providing fast response times.

  3. Indexing and Search Functionality: Amazon Athena does not require explicit indexing of data as it directly queries the data stored in Amazon S3. This makes it convenient for ad-hoc analysis and exploration of data in its raw form. Elasticsearch, on the other hand, requires explicit indexing of data for efficient search operations. It uses inverted index data structures to enable fast text search and supports advanced search features like fuzzy search, phrase matching, and relevance scoring.

  4. Real-time Data Ingestion: Elasticsearch is optimized for real-time data ingestion, making it ideal for use cases such as log analysis, monitoring, and real-time analytics. It provides low-latency indexing and near real-time search capabilities. On the other hand, Amazon Athena is not designed for real-time data ingestion. It is more suitable for batch processing of data stored in S3, providing insights over historical data.

  5. Geo-Location and Mapping: Elasticsearch has built-in support for geo-location and mapping functionality. It allows you to store, search, and visualize spatial data efficiently. You can perform complex geo-queries, calculate distances, and aggregate data based on geographic coordinates. Amazon Athena, on the other hand, does not provide native geo-location capabilities. It is primarily focused on SQL-based querying and analysis.

  6. Deployment and Management: Amazon Athena is a fully managed service provided by AWS, so you don't have to worry about infrastructure provisioning, capacity planning, or software maintenance. It automatically scales, handles updates, and provides monitoring and logging features. Elasticsearch, on the other hand, can be self-managed or used as a managed service like Amazon Elasticsearch Service. Self-management requires more effort in terms of infrastructure setup, configuration, and ongoing maintenance.

In Summary, Amazon Athena is a serverless query service for analyzing structured and semi-structured data stored in Amazon S3, while Elasticsearch is a distributed search and analytics engine designed for real-time exploration and analysis of unstructured or semi-structured data. Amazon Athena is best suited for ad-hoc querying, batch processing, and historical analysis, while Elasticsearch is ideal for real-time data ingestion, search, and visualization, with built-in support for geo-location capabilities.

Advice on Amazon Athena and Elasticsearch
Rana Usman Shahid
Chief Technology Officer at TechAvanza · | 6 upvotes · 376.8K views
Needs advice
on
AlgoliaAlgoliaElasticsearchElasticsearch
and
FirebaseFirebase

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 · | 8 upvotes · 281K views
Recommends
on
AlgoliaAlgolia

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
on
Cloud FirestoreCloud 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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Hi all,

Currently, we need to ingest the data from Amazon S3 to DB either Amazon Athena or Amazon Redshift. But the problem with the data is, it is in .PSV (pipe separated values) format and the size is also above 200 GB. The query performance of the timeout in Athena/Redshift is not up to the mark, too slow while compared to Google BigQuery. How would I optimize the performance and query result time? Can anyone please help me out?

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Replies (4)

you can use aws glue service to convert you pipe format data to parquet format , and thus you can achieve data compression . Now you should choose Redshift to copy your data as it is very huge. To manage your data, you should partition your data in S3 bucket and also divide your data across the redshift cluster

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Carlos Acedo
Data Technologies Manager at SDG Group Iberia · | 5 upvotes · 238.7K views
Recommends
on
Amazon RedshiftAmazon Redshift

First of all you should make your choice upon Redshift or Athena based on your use case since they are two very diferent services - Redshift is an enterprise-grade MPP Data Warehouse while Athena is a SQL layer on top of S3 with limited performance. If performance is a key factor, users are going to execute unpredictable queries and direct and managing costs are not a problem I'd definitely go for Redshift. If performance is not so critical and queries will be predictable somewhat I'd go for Athena.

Once you select the technology you'll need to optimize your data in order to get the queries executed as fast as possible. In both cases you may need to adapt the data model to fit your queries better. In the case you go for Athena you'd also proabably need to change your file format to Parquet or Avro and review your partition strategy depending on your most frequent type of query. If you choose Redshift you'll need to ingest the data from your files into it and maybe carry out some tuning tasks for performance gain.

I'll recommend Redshift for now since it can address a wider range of use cases, but we could give you better advice if you described your use case in depth.

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Alexis Blandin
Recommends
on
Amazon AthenaAmazon Athena

It depend of the nature of your data (structured or not?) and of course your queries (ad-hoc or predictible?). For example you can look at partitioning and columnar format to maximize MPP capabilities for both Athena and Redshift

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Recommends

you can change your PSV fomat data to parquet file format with AWS GLUE and then your query performance will be improved

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Pros of Amazon Athena
Pros of Elasticsearch
  • 16
    Use SQL to analyze CSV files
  • 8
    Glue crawlers gives easy Data catalogue
  • 7
    Cheap
  • 6
    Query all my data without running servers 24x7
  • 4
    No data base servers yay
  • 3
    Easy integration with QuickSight
  • 2
    Query and analyse CSV,parquet,json files in sql
  • 2
    Also glue and athena use same data catalog
  • 1
    No configuration required
  • 0
    Ad hoc checks on data made easy
  • 328
    Powerful api
  • 315
    Great search engine
  • 231
    Open source
  • 214
    Restful
  • 200
    Near real-time search
  • 98
    Free
  • 85
    Search everything
  • 54
    Easy to get started
  • 45
    Analytics
  • 26
    Distributed
  • 6
    Fast search
  • 5
    More than a search engine
  • 4
    Great docs
  • 4
    Awesome, great tool
  • 3
    Highly Available
  • 3
    Easy to scale
  • 2
    Potato
  • 2
    Document Store
  • 2
    Great customer support
  • 2
    Intuitive API
  • 2
    Nosql DB
  • 2
    Great piece of software
  • 2
    Reliable
  • 2
    Fast
  • 2
    Easy setup
  • 1
    Open
  • 1
    Easy to get hot data
  • 1
    Github
  • 1
    Elaticsearch
  • 1
    Actively developing
  • 1
    Responsive maintainers on GitHub
  • 1
    Ecosystem
  • 1
    Not stable
  • 1
    Scalability
  • 0
    Community

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Cons of Amazon Athena
Cons of Elasticsearch
    Be the first to leave a con
    • 7
      Resource hungry
    • 6
      Diffecult to get started
    • 5
      Expensive
    • 4
      Hard to keep stable at large scale

    Sign up to add or upvote consMake informed product decisions

    What is Amazon Athena?

    Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.

    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 companies use Elasticsearch?
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    What tools integrate with Amazon Athena?
    What tools integrate with Elasticsearch?

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    Blog Posts

    Aug 28 2019 at 3:10AM

    Segment

    PythonJavaAmazon S3+16
    7
    2569
    Jul 2 2019 at 9:34PM

    Segment

    Google AnalyticsAmazon S3New Relic+25
    10
    6787
    May 21 2019 at 12:20AM

    Elastic

    ElasticsearchKibanaLogstash+4
    12
    5193
    GitHubPythonReact+42
    49
    40782
    What are some alternatives to Amazon Athena and Elasticsearch?
    Presto
    Distributed SQL Query Engine for Big Data
    Amazon Redshift Spectrum
    With Redshift Spectrum, you can extend the analytic power of Amazon Redshift beyond data stored on local disks in your data warehouse to query vast amounts of unstructured data in your Amazon S3 “data lake” -- without having to load or transform any data.
    Amazon Redshift
    It is optimized for data sets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions.
    Cassandra
    Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.
    Spectrum
    The community platform for the future.
    See all alternatives