Amazon Athena vs MongoDB

Overview

MongoDB

Stacks96.6K

Followers82.0K

Votes4.1K

GitHub Stars27.7K

Forks5.7K

Amazon Athena

Stacks519

Followers840

Votes49

Amazon Athena vs MongoDB: What are the differences?

Introduction

In this article, we will explore the key differences between Amazon Athena and MongoDB. Amazon Athena is a serverless query service that allows you to analyze data in Amazon S3 using standard SQL. On the other hand, MongoDB is a document database that provides high performance, scalability, and flexibility. Now let's dive into the key differences between these two data storage solutions.

Data Model: Amazon Athena follows a schema-on-read approach, meaning the schema is applied at the time of query execution. It allows you to run ad-hoc queries on a variety of data formats stored in Amazon S3. On the other hand, MongoDB follows a schema-on-write approach, where the schema needs to be defined upfront before data insertion. This allows for better data consistency and predefined data structures.
Scalability: In terms of scalability, Amazon Athena is highly scalable as it automatically scales the underlying resources based on the query workload. It can handle large-scale data processing with ease. MongoDB, on the other hand, offers horizontal scalability through sharding. It allows you to distribute data across multiple shards, ensuring high availability and performance.
Query Language: Amazon Athena uses SQL as its query language, making it easy for SQL-savvy users to write and execute queries. It supports a wide range of SQL functions and operators for data manipulation and analysis. MongoDB, on the other hand, uses its own query language called the MongoDB Query Language (MQL). It offers a powerful set of query operators and methods for retrieving and manipulating data.
Indexing: When it comes to indexing, Amazon Athena does not support indexing directly on the underlying data stored in Amazon S3. It relies on the metadata stored in the AWS Glue Data Catalog to optimize query performance. On the other hand, MongoDB supports various types of indexes like single-field, compound, text, and geospatial indexes. This allows for efficient querying and faster data retrieval.
Data Storage: Amazon Athena stores data in Amazon S3, which provides unlimited storage capacity and durability. It supports a wide range of data formats like CSV, JSON, Avro, and Parquet. MongoDB, on the other hand, stores data in a binary JSON-like format called BSON. It offers rich data structures like arrays and nested documents, making it suitable for complex data models.
Data Replication: Amazon Athena does not provide built-in data replication capabilities. However, since it uses Amazon S3 as its storage backend, you can leverage AWS S3 data replication features to replicate data across different AWS regions for data backup and disaster recovery. MongoDB, on the other hand, provides built-in replication features like replica sets, which allow for automatic failover and data redundancy.

In Summary, Amazon Athena and MongoDB have key differences in terms of data model, scalability, query language, indexing, data storage, and data replication. Amazon Athena is a serverless query service that excels at ad-hoc querying of data stored in Amazon S3, while MongoDB is a flexible and scalable document database suitable for various use cases.

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Advice on MongoDB, Amazon Athena

George

Student

Mar 18, 2020

Needs adviceon

PostgreSQL

Python

Django

Hello everyone,

Well, I want to build a large-scale project, but I do not know which ORDBMS to choose. The app should handle real-time operations, not chatting, but things like future scheduling or reminders. It should be also really secure, fast and easy to use. And last but not least, should I use them both. I mean PostgreSQL with Python / Django and MongoDB with Node.js? Or would it be better to use PostgreSQL with Node.js?

*The project is going to use React for the front-end and GraphQL is going to be used for the API.

Thank you all. Any answer or advice would be really helpful!

620k views620k

Comments

Ido

Mar 6, 2020

Decided

My data was inherently hierarchical, but there was not enough content in each level of the hierarchy to justify a relational DB (SQL) with a one-to-many approach. It was also far easier to share data between the frontend (Angular), backend (Node.js) and DB (MongoDB) as they all pass around JSON natively. This allowed me to skip the translation layer from relational to hierarchical. You do need to think about correct indexes in MongoDB, and make sure the objects have finite size. For instance, an object in your DB shouldn't have a property which is an array that grows over time, without limit. In addition, I did use MySQL for other types of data, such as a catalog of products which (a) has a lot of data, (b) flat and not hierarchical, (c) needed very fast queries.

575k views575k

Comments

Mike

Mar 20, 2020

Needs advice

We Have thousands of .pdf docs generated from the same form but with lots of variability. We need to extract data from open text and more important - from tables inside the docs. The output of Couchbase/Mongo will be one row per document for backend processing. ADOBE renders the tables in an unusable form.

241k views241k

Comments

Detailed Comparison

MongoDB	Amazon Athena
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.	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.
Flexible data model, expressive query language, secondary indexes, replication, auto-sharding, in-place updates, aggregation, GridFS	-
Statistics
GitHub Stars 27.7K	GitHub Stars -
GitHub Forks 5.7K	GitHub Forks -
Stacks 96.6K	Stacks 519
Followers 82.0K	Followers 840
Votes 4.1K	Votes 49
Pros & Cons
Pros 829 Document-oriented storage 594 No sql 554 Ease of use 465 Fast 410 High performance Cons 6 Very slowly for connected models that require joins 3 Not acid compliant 2 Proprietary query language	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 Presto

What are some alternatives to MongoDB, Amazon Athena?

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

SQLite

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.

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 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

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 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

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 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.

Related Comparisons

Amazon Athena vs MongoDB: What are the differences?

Introduction

Data Model: Amazon Athena follows a schema-on-read approach, meaning the schema is applied at the time of query execution. It allows you to run ad-hoc queries on a variety of data formats stored in Amazon S3. On the other hand, MongoDB follows a schema-on-write approach, where the schema needs to be defined upfront before data insertion. This allows for better data consistency and predefined data structures.
Scalability: In terms of scalability, Amazon Athena is highly scalable as it automatically scales the underlying resources based on the query workload. It can handle large-scale data processing with ease. MongoDB, on the other hand, offers horizontal scalability through sharding. It allows you to distribute data across multiple shards, ensuring high availability and performance.
Query Language: Amazon Athena uses SQL as its query language, making it easy for SQL-savvy users to write and execute queries. It supports a wide range of SQL functions and operators for data manipulation and analysis. MongoDB, on the other hand, uses its own query language called the MongoDB Query Language (MQL). It offers a powerful set of query operators and methods for retrieving and manipulating data.
Indexing: When it comes to indexing, Amazon Athena does not support indexing directly on the underlying data stored in Amazon S3. It relies on the metadata stored in the AWS Glue Data Catalog to optimize query performance. On the other hand, MongoDB supports various types of indexes like single-field, compound, text, and geospatial indexes. This allows for efficient querying and faster data retrieval.
Data Storage: Amazon Athena stores data in Amazon S3, which provides unlimited storage capacity and durability. It supports a wide range of data formats like CSV, JSON, Avro, and Parquet. MongoDB, on the other hand, stores data in a binary JSON-like format called BSON. It offers rich data structures like arrays and nested documents, making it suitable for complex data models.
Data Replication: Amazon Athena does not provide built-in data replication capabilities. However, since it uses Amazon S3 as its storage backend, you can leverage AWS S3 data replication features to replicate data across different AWS regions for data backup and disaster recovery. MongoDB, on the other hand, provides built-in replication features like replica sets, which allow for automatic failover and data redundancy.

Amazon Athena vs MongoDB

Overview