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DBMS > Amazon DynamoDB vs. Apache Spark (SQL)

System Properties Comparison Amazon DynamoDB vs. Apache Spark (SQL)

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Our visitors often compare Amazon DynamoDB and Apache Spark (SQL) with PostgreSQL, MySQL and Oracle.

Editorial information provided by DB-Engines
NameAmazon DynamoDB  Xexclude from comparisonApache Spark (SQL)  Xexclude from comparison
DescriptionHosted, scalable database service by Amazon with the data stored in Amazons cloudApache Spark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelDocument store
Key-value store
Relational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score79.30
Rank#15  Overall
#3  Document stores
#2  Key-value stores
Score21.62
Rank#29  Overall
#18  Relational DBMS
Websiteaws.amazon.com/­dynamodbspark.apache.org/­sql
Technical documentationdocs.aws.amazon.com/­dynamodbspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperAmazonApache Software Foundation
Initial release20122014
Current release3.5.0 ( 2.13), September 2023
License infoCommercial or Open Sourcecommercial infofree tier for a limited amount of database operationsOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud serviceyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageScala
Server operating systemshostedLinux
OS X
Windows
Data schemeschema-freeyes
Typing infopredefined data types such as float or dateyesyes
XML support infoSome form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.no
Secondary indexesyesno
SQL infoSupport of SQLnoSQL-like DML and DDL statements
APIs and other access methodsRESTful HTTP APIJDBC
ODBC
Supported programming languages.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby
Java
Python
R
Scala
Server-side scripts infoStored proceduresnono
Triggersyes infoby integration with AWS Lambdano
Partitioning methods infoMethods for storing different data on different nodesShardingyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesyesnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsno infomay be implemented via Amazon Elastic MapReduce (Amazon EMR)
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Immediate Consistency infocan be specified for read operations
Foreign keys infoReferential integritynono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACID infoACID across one or more tables within a single AWS account and regionno
Concurrency infoSupport for concurrent manipulation of datayesyes
Durability infoSupport for making data persistentyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.no
User concepts infoAccess controlAccess rights for users and roles can be defined via the AWS Identity and Access Management (IAM)no

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More resources
Amazon DynamoDBApache Spark (SQL)
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Recent citations in the news

Announcing the end of support for AWS DynamoDB Session State Provider
7 May 2025, Amazon Web Services (AWS)

Announcing configurable point-in-time recovery periods for Amazon DynamoDB
7 January 2025, Amazon Web Services (AWS)

Amazon DynamoDB re:Invent 2024 recap
19 December 2024, Amazon Web Services (AWS)

New Amazon DynamoDB zero-ETL integration with Amazon SageMaker Lakehouse | Amazon Web Services
3 December 2024, Amazon Web Services (AWS)

What version of Amazon DynamoDB are you running?
15 October 2024, Amazon Web Services (AWS)

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Scala vs Python for Apache Spark: An In-depth Comparison With Use Cases For Each
21 April 2025, Simplilearn.com

How to run Pandas code on Spark
25 January 2025, Theodo Data & AI

The 6 Best Apache Spark Courses on Udemy to Consider for 2025
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