Amazon Athena vs Amazon RDS for Aurora

Overview

Amazon Aurora

Stacks803

Followers744

Votes55

Amazon Athena

Stacks519

Followers840

Votes49

Amazon Athena vs Amazon RDS for Aurora: What are the differences?

Amazon Athena is a serverless interactive query service to analyze data in Amazon S3 using standard SQL while Amazon RDS for Aurora is a fully managed relational database service compatible with MySQL and PostgreSQL. Let's explore the key differences between them:

Architecture: Amazon Athena is a serverless query service that allows you to run SQL queries on data stored in Amazon S3. It leverages Presto, an open-source distributed SQL engine, to execute queries on data files directly from S3 without requiring any infrastructure provisioning. On the other hand, Amazon RDS for Aurora is a managed relational database service that is compatible with MySQL and PostgreSQL. It provides a traditional database management system with features such as data storage, transaction management, and advanced querying capabilities.
Querying: Amazon Athena is a serverless query service that enables ad-hoc querying and analysis of data stored in Amazon S3 using standard SQL queries. On the other hand, Amazon RDS for Aurora is a fully managed relational database service that provides a traditional SQL interface for querying and managing structured data.
Data Storage: Amazon Athena does not require you to load data into a separate database. It directly queries data stored in Amazon S3, which allows for flexible and cost-effective storage of large datasets. Amazon RDS for Aurora, however, requires you to load and manage your data within the Aurora database engine, which provides optimized storage and indexing capabilities.
Scalability and Management: Amazon Athena automatically scales resources to handle query workloads and does not require any infrastructure management. It is a serverless service where you pay for the queries you run. Amazon RDS for Aurora, on the other hand, provides a scalable and managed relational database environment that takes care of infrastructure provisioning, scaling, and backups, but requires more management overhead compared to Athena.

In summary, Amazon Athena is a serverless query service for analyzing large datasets stored in Amazon S3, offering automatic scaling and flexible schema-less querying. Amazon RDS for Aurora is a managed relational database service optimized for high-performance transactional workloads, providing strong consistency and durability with customizable scaling options.

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Advice on Amazon Aurora, 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

Amazon Aurora	Amazon Athena
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.	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.
High Throughput with Low Jitter;Push-button Compute Scaling;Storage Auto-scaling;Amazon Aurora Replicas;Instance Monitoring and Repair;Fault-tolerant and Self-healing Storage;Automatic, Continuous, Incremental Backups and Point-in-time Restore;Database Snapshots;Resource-level Permissions;Easy Migration;Monitoring and Metrics	-
Statistics
Stacks 803	Stacks 519
Followers 744	Followers 840
Votes 55	Votes 49
Pros & Cons
Pros 14 MySQL compatibility 12 Better performance 10 Easy read scalability 9 Speed 7 Low latency read replica Cons 2 Vendor locking 1 Rigid schema	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
PostgreSQL MySQL	Amazon S3 Presto

What are some alternatives to Amazon Aurora, Amazon Athena?

Amazon RDS

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.

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

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.

Splunk

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

Related Comparisons

Architecture: Amazon Athena is a serverless query service that allows you to run SQL queries on data stored in Amazon S3. It leverages Presto, an open-source distributed SQL engine, to execute queries on data files directly from S3 without requiring any infrastructure provisioning. On the other hand, Amazon RDS for Aurora is a managed relational database service that is compatible with MySQL and PostgreSQL. It provides a traditional database management system with features such as data storage, transaction management, and advanced querying capabilities.
Querying: Amazon Athena is a serverless query service that enables ad-hoc querying and analysis of data stored in Amazon S3 using standard SQL queries. On the other hand, Amazon RDS for Aurora is a fully managed relational database service that provides a traditional SQL interface for querying and managing structured data.
Data Storage: Amazon Athena does not require you to load data into a separate database. It directly queries data stored in Amazon S3, which allows for flexible and cost-effective storage of large datasets. Amazon RDS for Aurora, however, requires you to load and manage your data within the Aurora database engine, which provides optimized storage and indexing capabilities.
Scalability and Management: Amazon Athena automatically scales resources to handle query workloads and does not require any infrastructure management. It is a serverless service where you pay for the queries you run. Amazon RDS for Aurora, on the other hand, provides a scalable and managed relational database environment that takes care of infrastructure provisioning, scaling, and backups, but requires more management overhead compared to Athena.

Amazon Athena vs Amazon RDS for Aurora