Amazon Athena vs Amazon RDS

Overview

Amazon RDS

Stacks16.2K

Followers10.8K

Votes761

Amazon Athena

Stacks521

Followers840

Votes49

Amazon Athena vs Amazon RDS: 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; Amazon RDS: Set up, operate, and scale a relational database in the cloud. Amazon RDS gives you access to the capabilities of a familiar MySQL, Oracle or Microsoft SQL Server database engine. This means that the code, applications, and tools you already use today with your existing databases can be used with Amazon RDS. Amazon RDS automatically patches the database software and backs up your database, storing the backups for a user-defined retention period and enabling point-in-time recovery. You benefit from the flexibility of being able to scale the compute resources or storage capacity associated with your Database Instance (DB Instance) via a single API call.

Amazon Athena and Amazon RDS are primarily classified as "Big Data" and "SQL Database as a Service" tools respectively.

"Use SQL to analyze CSV files" is the top reason why over 13 developers like Amazon Athena, while over 162 developers mention "Reliable failovers" as the leading cause for choosing Amazon RDS.

Airbnb, Netflix, and Amazon are some of the popular companies that use Amazon RDS, whereas Amazon Athena is used by CRED, Bagelcode, and Queue-it. Amazon RDS has a broader approval, being mentioned in 2499 company stacks & 8078 developers stacks; compared to Amazon Athena, which is listed in 124 company stacks and 197 developer stacks.

Share your Stack

Help developers discover the tools you use. Get visibility for your team's tech choices and contribute to the community's knowledge.

View Docs

CLI (Node.js)

Manual

Detailed Comparison

Amazon RDS	Amazon Athena
Amazon RDS gives you access to the capabilities of a familiar MySQL, Oracle or Microsoft SQL Server database engine. This means that the code, applications, and tools you already use today with your existing databases can be used with Amazon RDS. Amazon RDS automatically patches the database software and backs up your database, storing the backups for a user-defined retention period and enabling point-in-time recovery. You benefit from the flexibility of being able to scale the compute resources or storage capacity associated with your Database Instance (DB Instance) via a single API call.	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.
Pre-configured Parameters;Monitoring and Metrics;Automatic Software Patching;Automated Backups;DB Snapshots;DB Event Notifications;Multi-Availability Zone (Multi-AZ) Deployments;Provisioned IOPS;Push-Button Scaling;Automatic Host Replacement;Replication;Isolation and Security	-
Statistics
Stacks 16.2K	Stacks 521
Followers 10.8K	Followers 840
Votes 761	Votes 49
Pros & Cons
Pros 165 Reliable failovers 156 Automated backups 130 Backed by amazon 92 Db snapshots 87 Multi-availability	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 Amazon RDS, 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

Amazon Aurora

Amazon Aurora is a MySQL-compatible, relational database engine that combines the speed and availability of high-end commercial databases with the simplicity and cost-effectiveness of open source databases. Amazon Aurora provides up to five times better performance than MySQL at a price point one tenth that of a commercial database while delivering similar performance and availability.

Google Cloud SQL

Run the same relational databases you know with their rich extension collections, configuration flags and developer ecosystem, but without the hassle of self management.

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.

ClearDB

ClearDB uses a combination of advanced replication techniques, advanced cluster technology, and layered web services to provide you with a MySQL database that is "smarter" than usual.

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.