Amazon Athena vs Oracle

Overview

Oracle

Stacks2.6K

Followers1.8K

Votes113

Amazon Athena

Stacks521

Followers840

Votes49

Amazon Athena vs Oracle: What are the differences?

Introduction

Amazon Athena and Oracle are two widely used data query and analysis tools. While both offer similar functionalities, there are key differences that set them apart from each other. In this comparison, we will highlight six major differences between Amazon Athena and Oracle.

Deployment and Infrastructure: Amazon Athena is a serverless query service that operates on AWS infrastructure. It requires no setup or management of servers, making it highly scalable. On the other hand, Oracle is an on-premises database management system that requires infrastructure setup and management, including installation and configuration of servers.
Data Source Compatibility: Amazon Athena is primarily designed to work with data stored in Amazon S3, allowing you to query any file formats supported by Athena. In contrast, Oracle can work with various data sources including Oracle Database, third-party databases, and other file formats.
Query Performance: Amazon Athena uses a highly optimized parallel execution engine that automatically parallelizes and scales the query execution. This enables it to quickly analyze large datasets and provide results in seconds. Oracle, on the other hand, may require performance optimization techniques like indexing and materialized views to achieve optimal query performance.
Cost Model: Amazon Athena follows a pay-as-you-go pricing model, where you pay only for the queries you run and the amount of data scanned. This flexible pricing structure makes it highly cost-effective for ad-hoc analysis. In contrast, Oracle typically requires a significant upfront investment for licensing, hardware, and ongoing maintenance costs.
Ease of Use: With its serverless architecture, Amazon Athena provides a user-friendly interface for querying and analyzing data without the need for complex setup or management. It also integrates well with other AWS services for data integration and visualization. Oracle, while offering a comprehensive set of tools and features, generally requires more expertise and administrative effort to set up and maintain.
Scalability and Elasticity: Amazon Athena automatically scales its resources based on the volume of data and query complexity, providing near-instantaneous scalability and elasticity. Oracle, on the other hand, requires manual capacity planning and resource allocation to handle increasing workloads, making it less flexible in terms of scalability.

In summary, Amazon Athena is a serverless query service with scalability, cost-effectiveness, and ease of use as its key advantages, whereas Oracle is an on-premises database management system that provides comprehensive functionality and greater control, but requires more infrastructure setup and management efforts.

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Advice on Oracle, Amazon Athena

Daniel

Data Engineer at Dimensigon

Jul 18, 2020

Decided

We have chosen Tibero over Oracle because we want to offer a PL/SQL-as-a-Service that the users can deploy in any Cloud without concerns from our website at some standard cost. With Oracle Database, developers would have to worry about what they implement and the related costs of each feature but the licensing model from Tibero is just 1 price and we have all features included, so we don't have to worry and developers using our SQLaaS neither. PostgreSQL would be open source. We have chosen Tibero over Oracle because we want to offer a PL/SQL that you can deploy in any Cloud without concerns. PostgreSQL would be the open source option but we need to offer an SQLaaS with encryption and more enterprise features in the background and best value option we have found, it was Tibero Database for PL/SQL-based applications.

496k views496k

Comments

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?

522k views522k

Comments

Abigail

Dec 6, 2019

Decided

In the field of bioinformatics, we regularly work with hierarchical and unstructured document data. Unstructured text data from PDFs, image data from radiographs, phylogenetic trees and cladograms, network graphs, streaming ECG data... none of it fits into a traditional SQL database particularly well. As such, we prefer to use document oriented databases.

MongoDB is probably the oldest component in our stack besides Javascript, having been in it for over 5 years. At the time, we were looking for a technology that could simply cache our data visualization state (stored in JSON) in a database as-is without any destructive normalization. MongoDB was the perfect tool; and has been exceeding expectations ever since.

Trivia fact: some of the earliest electronic medical records (EMRs) used a document oriented database called MUMPS as early as the 1960s, prior to the invention of SQL. MUMPS is still in use today in systems like Epic and VistA, and stores upwards of 40% of all medical records at hospitals. So, we saw MongoDB as something as a 21st century version of the MUMPS database.

540k views540k

Comments

Detailed Comparison

Oracle	Amazon Athena
Oracle Database is an RDBMS. An RDBMS that implements object-oriented features such as user-defined types, inheritance, and polymorphism is called an object-relational database management system (ORDBMS). Oracle Database has extended the relational model to an object-relational model, making it possible to store complex business models in a relational database.	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 2.6K	Stacks 521
Followers 1.8K	Followers 840
Votes 113	Votes 49
Pros & Cons
Pros 44 Reliable 33 Enterprise 15 High Availability 5 Expensive 5 Hard to maintain Cons 14 Expensive	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 Oracle, Amazon Athena?

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

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.

Related Comparisons

Amazon Athena vs Oracle: What are the differences?

Introduction

Deployment and Infrastructure: Amazon Athena is a serverless query service that operates on AWS infrastructure. It requires no setup or management of servers, making it highly scalable. On the other hand, Oracle is an on-premises database management system that requires infrastructure setup and management, including installation and configuration of servers.
Data Source Compatibility: Amazon Athena is primarily designed to work with data stored in Amazon S3, allowing you to query any file formats supported by Athena. In contrast, Oracle can work with various data sources including Oracle Database, third-party databases, and other file formats.
Query Performance: Amazon Athena uses a highly optimized parallel execution engine that automatically parallelizes and scales the query execution. This enables it to quickly analyze large datasets and provide results in seconds. Oracle, on the other hand, may require performance optimization techniques like indexing and materialized views to achieve optimal query performance.
Cost Model: Amazon Athena follows a pay-as-you-go pricing model, where you pay only for the queries you run and the amount of data scanned. This flexible pricing structure makes it highly cost-effective for ad-hoc analysis. In contrast, Oracle typically requires a significant upfront investment for licensing, hardware, and ongoing maintenance costs.
Ease of Use: With its serverless architecture, Amazon Athena provides a user-friendly interface for querying and analyzing data without the need for complex setup or management. It also integrates well with other AWS services for data integration and visualization. Oracle, while offering a comprehensive set of tools and features, generally requires more expertise and administrative effort to set up and maintain.
Scalability and Elasticity: Amazon Athena automatically scales its resources based on the volume of data and query complexity, providing near-instantaneous scalability and elasticity. Oracle, on the other hand, requires manual capacity planning and resource allocation to handle increasing workloads, making it less flexible in terms of scalability.

Amazon Athena vs Oracle

Overview