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  1. Stackups
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  4. Databases
  5. Amazon Athena vs SQLite

Amazon Athena vs SQLite

OverviewDecisionsComparisonAlternatives

Overview

SQLite
SQLite
Stacks19.9K
Followers15.2K
Votes535
Amazon Athena
Amazon Athena
Stacks519
Followers840
Votes49

Amazon Athena vs SQLite: What are the differences?

Introduction

Amazon Athena and SQLite are both popular database systems that offer different features and functionalities. While Amazon Athena is a query service that allows you to analyze data directly from Amazon S3, SQLite is a lightweight, file-based database management system. Let's explore the key differences between the two.

  1. Scalability: Amazon Athena is designed to handle large-scale datasets and can process massive amounts of data in parallel, making it highly scalable. On the other hand, SQLite is more suitable for small to medium-sized databases and may not perform as efficiently when dealing with large datasets.

  2. Deployment: Amazon Athena is a cloud-based service and is fully managed by Amazon Web Services (AWS). It eliminates the need for infrastructure management and provides a serverless experience. SQLite, on the other hand, is a self-contained, serverless database engine that does not require any additional infrastructure or server deployment.

  3. Data Source: Amazon Athena is primarily designed to query data stored in Amazon S3, which offers easy integration with other AWS services. SQLite, on the other hand, can be used as an embedded database within applications or can directly work with file-based databases.

  4. Query Language: Amazon Athena uses SQL (Structured Query Language) for querying data, making it familiar to users who are already proficient in SQL. SQLite also supports SQL, but it provides additional language extensions and features specific to SQLite.

  5. Performance: Amazon Athena leverages distributed computing capabilities and optimized data processing techniques to accelerate query execution on large datasets. SQLite, being a file-based database, may not offer the same level of performance as it lacks parallel processing and distributed computing capabilities.

  6. Cost: Amazon Athena follows a pay-per-query pricing model, where you only pay for the amount of data scanned during a query. This can be cost-effective for sporadic and ad-hoc querying. SQLite, being an open-source database system, is free to use and does not incur any direct costs.

In summary, Amazon Athena is a highly scalable cloud-based query service optimized for large-scale datasets stored in Amazon S3, whereas SQLite is a lightweight, file-based database engine suitable for smaller datasets and can be used as an embedded database.

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Advice on SQLite, Amazon Athena

Dimelo
Dimelo

Nov 5, 2020

Needs adviceonSQLiteSQLiteMySQLMySQLPostgreSQLPostgreSQL

I need to add a DBMS to my stack, but I don't know which. I'm tempted to learn SQLite since it would be useful to me with its focus on local access without concurrency. However, doing so feels like I would be defeating the purpose of trying to expand my skill set since it seems like most enterprise applications have the opposite requirements.

To be able to apply what I learn to more projects, what should I try to learn? MySQL? PostgreSQL? Something else? Is there a comfortable middle ground between high applicability and ease of use?

670k views670k
Comments
Stephen
Stephen

Senior DevOps Engineer at Vital Beats

Nov 9, 2020

Review

A question you might want to think about is "What kind of experience do I want to gain, by using a DBMS?". If your aim is to have experience with SQL and any related libraries and frameworks for your language of choice (python, I think?), then it kind of doesn't matter too much which you pick so much. As others have said, SQLite would offer you the ability to very easily get started, and would give you a reasonably standard (if a little basic) SQL dialect to work with.

If your aim is actually to have a bit of "operational" experience, in terms of things like what command line tools might be available as standard for the DBMS, understanding how the DBMS handles multiple databases, when to use multiple schemas vs multiple databases, some basic privilege management etc. Then I would recommend PostgreSQL. SQLite's simplicity actually avoids most of these experiences, which is not helpful to you if that is what you hope to learn. MySQL has a few "quirks" to how it manages things like multiple databases, which may lead you to making less good decisions if you tried to take your experience over to different DBMS, especially in bigger enterprise roles. PostgreSQL is kind of a happy middle ground here, with the ability to start PostgreSQL servers via docker or docker-compose making the actual day-to-day management pretty easy, while still giving you experience of the kinds of considerations I have listed above.

At Vital Beats we make use of PostgreSQL, largely because it offers us a happy balance between good management and backup of data, and good standard command line tools, which is essential for us where we are deploying our solutions within Kubernetes / docker, and so more graphical tools are not always appropriate for us. PostgreSQL is also pretty universally supported in terms of language libraries and frameworks, without having to make compromises on how we want to store and layout our data.

316k views316k
Comments
Anonymous
Anonymous

Oct 29, 2019

Needs advice

Hi everyone! I am a high school student, starting a massive project. I'm building a system for a boarding school to be better connected to their students and be more efficient with information. In the meantime, I am developing a website and an android app. What's the best datastore I can use? I need to be able to access student data on the app from the main database and send push notifications. Also feed updates. What's the best approach? What's the best tool I can use to deploy the website and the database? One for testing and prototyping, and an official one... Thanks in advance!!!!

366k views366k
Comments

Detailed Comparison

SQLite
SQLite
Amazon Athena
Amazon Athena

SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file.

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.

Statistics
Stacks
19.9K
Stacks
519
Followers
15.2K
Followers
840
Votes
535
Votes
49
Pros & Cons
Pros
  • 163
    Lightweight
  • 135
    Portable
  • 122
    Simple
  • 81
    Sql
  • 29
    Preinstalled on iOS and Android
Cons
  • 2
    Not for multi-process of multithreaded apps
  • 1
    Needs different binaries for each platform
Pros
  • 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
Integrations
No integrations available
Amazon S3
Amazon S3
Presto
Presto

What are some alternatives to SQLite, Amazon Athena?

MongoDB

MongoDB

MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.

MySQL

MySQL

The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.

PostgreSQL

PostgreSQL

PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.

Microsoft SQL Server

Microsoft SQL Server

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.

Cassandra

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.

Memcached

Memcached

Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering.

MariaDB

MariaDB

Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. MariaDB is designed as a drop-in replacement of MySQL(R) with more features, new storage engines, fewer bugs, and better performance.

RethinkDB

RethinkDB

RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn.

ArangoDB

ArangoDB

A distributed free and open-source database with a flexible data model for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions.

InfluxDB

InfluxDB

InfluxDB is a scalable datastore for metrics, events, and real-time analytics. It has a built-in HTTP API so you don't have to write any server side code to get up and running. InfluxDB is designed to be scalable, simple to install and manage, and fast to get data in and out.

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