StackShareStackShare
Follow on
StackShare

Discover and share technology stacks from companies around the world.

Follow on

© 2025 StackShare. All rights reserved.

Product

  • Stacks
  • Tools
  • Feed

Company

  • About
  • Contact

Legal

  • Privacy Policy
  • Terms of Service
  1. Stackups
  2. Application & Data
  3. NoSQL Databases
  4. NOSQL Database As A Service
  5. Amazon DynamoDB vs VelocityDB

Amazon DynamoDB vs VelocityDB

OverviewDecisionsComparisonAlternatives

Overview

Amazon DynamoDB
Amazon DynamoDB
Stacks4.0K
Followers3.2K
Votes195
VelocityDB
VelocityDB
Stacks1
Followers19
Votes0

Amazon DynamoDB vs VelocityDB: What are the differences?

What is Amazon DynamoDB? Fully managed NoSQL database service. All data items are stored on Solid State Drives (SSDs), and are replicated across 3 Availability Zones for high availability and durability. With DynamoDB, you can offload the administrative burden of operating and scaling a highly available distributed database cluster, while paying a low price for only what you use.

What is VelocityDB? A NoSQL Object Database, extended as Graph Database is VelocityGraph. It is a C# .NET NoSQL Object Database that can be Embedded/Distributed, extended as Graph Database is VelocityGraph. It supports both embedded and distributed deployments.

Amazon DynamoDB and VelocityDB belong to "NoSQL Database as a Service" category of the tech stack.

Some of the features offered by Amazon DynamoDB are:

  • Automated Storage Scaling – There is no limit to the amount of data you can store in a DynamoDB table, and the service automatically allocates more storage, as you store more data using the DynamoDB write APIs.
  • Provisioned Throughput – When creating a table, simply specify how much request capacity you require. DynamoDB allocates dedicated resources to your table to meet your performance requirements, and automatically partitions data over a sufficient number of servers to meet your request capacity. If your throughput requirements change, simply update your table's request capacity using the AWS Management Console or the Amazon DynamoDB APIs. You are still able to achieve your prior throughput levels while scaling is underway.
  • Fully Distributed, Shared Nothing Architecture – Amazon DynamoDB scales horizontally and can seamlessly scale a single table over hundreds of servers.

On the other hand, VelocityDB provides the following key features:

  • Acid Transactional
  • Android
  • Any CPU (32bit/64bit)

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)
or
Manual

Advice on Amazon DynamoDB, VelocityDB

Doru
Doru

Solution Architect

Jun 9, 2019

ReviewonAmazon DynamoDBAmazon DynamoDB

I use Amazon DynamoDB because it integrates seamlessly with other AWS SaaS solutions and if cost is the primary concern early on, then this will be a better choice when compared to AWS RDS or any other solution that requires the creation of a HA cluster of IaaS components that will cost money just for being there, the costs not being influenced primarily by usage.

1.34k views1.34k
Comments

Detailed Comparison

Amazon DynamoDB
Amazon DynamoDB
VelocityDB
VelocityDB

With it , you can offload the administrative burden of operating and scaling a highly available distributed database cluster, while paying a low price for only what you use.

It is a C# .NET NoSQL Object Database that can be Embedded/Distributed, extended as Graph Database is VelocityGraph. It supports both embedded and distributed deployments.

Automated Storage Scaling – There is no limit to the amount of data you can store in a DynamoDB table, and the service automatically allocates more storage, as you store more data using the DynamoDB write APIs;Provisioned Throughput – When creating a table, simply specify how much request capacity you require. DynamoDB allocates dedicated resources to your table to meet your performance requirements, and automatically partitions data over a sufficient number of servers to meet your request capacity;Fully Distributed, Shared Nothing Architecture
Acid Transactional; Android; Any CPU (32bit/64bit); Array support; Auto Increment on a field; Backup & Restore; Choice of data structure to use; Compression of data; Data Fragmentation
Statistics
Stacks
4.0K
Stacks
1
Followers
3.2K
Followers
19
Votes
195
Votes
0
Pros & Cons
Pros
  • 62
    Predictable performance and cost
  • 56
    Scalable
  • 35
    Native JSON Support
  • 21
    AWS Free Tier
  • 7
    Fast
Cons
  • 4
    Only sequential access for paginate data
  • 1
    Scaling
  • 1
    Document Limit Size
No community feedback yet
Integrations
Amazon RDS for PostgreSQL
Amazon RDS for PostgreSQL
PostgreSQL
PostgreSQL
MySQL
MySQL
SQLite
SQLite
Azure Database for MySQL
Azure Database for MySQL
MySQL
MySQL
PostgreSQL
PostgreSQL
Velocity.js
Velocity.js
.NET
.NET
GraphQL
GraphQL
C#
C#

What are some alternatives to Amazon DynamoDB, VelocityDB?

MongoDB

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

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

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

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.

SQLite

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

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

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

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

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

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

Bootstrap
Materialize

Bootstrap vs Materialize

Laravel
Django

Django vs Laravel vs Node.js

Bootstrap
Foundation

Bootstrap vs Foundation vs Material UI

Node.js
Spring Boot

Node.js vs Spring-Boot

Liquibase
Flyway

Flyway vs Liquibase