Amazon Athena vs Mongoid

Overview

Mongoid

Stacks114

Followers72

Votes4

GitHub Stars25

Forks22

Amazon Athena

Stacks521

Followers840

Votes49

Amazon Athena vs Mongoid: What are the differences?

Amazon Athena: Query S3 Using SQL. 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; Mongoid: Ruby ODM framework for MongoDB. The philosophy of Mongoid is to provide a familiar API to Ruby developers who have been using Active Record or Data Mapper, while leveraging the power of MongoDB's schemaless and performant document-based design, dynamic queries, and atomic modifier operations.

Amazon Athena belongs to "Big Data Tools" category of the tech stack, while Mongoid can be primarily classified under "Object Document Mapper (ODM)".

Mongoid is an open source tool with 21 GitHub stars and 15 GitHub forks. Here's a link to Mongoid's open source repository on GitHub.

According to the StackShare community, Amazon Athena has a broader approval, being mentioned in 50 company stacks & 18 developers stacks; compared to Mongoid, which is listed in 7 company stacks and 7 developer stacks.

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Advice on Mongoid, Amazon Athena

Pavithra

Mar 12, 2020

Needs adviceon

Amazon S3

Amazon Athena

Amazon Redshift

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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Detailed Comparison

Mongoid	Amazon Athena
The philosophy of Mongoid is to provide a familiar API to Ruby developers who have been using Active Record or Data Mapper, while leveraging the power of MongoDB's schemaless and performant document-based design, dynamic queries, and atomic modifier operations.	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
GitHub Stars 25	GitHub Stars -
GitHub Forks 22	GitHub Forks -
Stacks 114	Stacks 521
Followers 72	Followers 840
Votes 4	Votes 49
Pros & Cons
Pros 1 Drop-in-and-forget replacement for activerecord 1 Easy to add 'created_at' and 'updated_at'' timestamps 1 Supports Referenced and Embedded Associations 1 Can be used without Rails	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
MongoDB	Amazon S3 Presto

What are some alternatives to Mongoid, Amazon Athena?

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.

Presto

Distributed SQL Query Engine for Big Data

Mongoose

Let's face it, writing MongoDB validation, casting and business logic boilerplate is a drag. That's why we wrote Mongoose. Mongoose provides a straight-forward, schema-based solution to modeling your application data and includes built-in type casting, validation, query building, business logic hooks and more, out of the box.

Apache Flink

Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.

lakeFS

It is an open-source data version control system for data lakes. It provides a “Git for data” platform enabling you to implement best practices from software engineering on your data lake, including branching and merging, CI/CD, and production-like dev/test environments.

Druid

Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.

Apache Kylin

Apache Kylin™ is an open source Distributed Analytics Engine designed to provide SQL interface and multi-dimensional analysis (OLAP) on Hadoop/Spark supporting extremely large datasets, originally contributed from eBay Inc.

Splunk

It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.

Apache Impala

Impala is a modern, open source, MPP SQL query engine for Apache Hadoop. Impala is shipped by Cloudera, MapR, and Amazon. With Impala, you can query data, whether stored in HDFS or Apache HBase – including SELECT, JOIN, and aggregate functions – in real time.