DBMS > FatDB vs. HarperDB vs. Snowflake vs. Sqrrl vs. Teradata Aster

System Properties Comparison FatDB vs. HarperDB vs. Snowflake vs. Sqrrl vs. Teradata Aster

Editorial information provided by DB-Engines

Name

FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.

Sqrrl has been acquired by Amazon and became a part of Amazon Web Services. It has been removed from the DB-Engines ranking.

Teradata Aster has been integrated into other Teradata systems and therefore will be removed from the DB-Engines ranking.

Description

A .NET NoSQL DBMS that can integrate with and extend SQL Server.

Ultra-low latency distributed database with an intuitive REST API supporting NoSQL and SQL (including joins). Deployment of functions and databases simultaneously with a consolidated node-level architecture.

Cloud-based data warehousing service for structured and semi-structured data

Adaptable, secure NoSQL built on Apache Accumulo

Platform for big data analytics on multistructured data sources and types

Primary database model

Document store
Key-value store

Document store

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	0.60
Rank	#244	Overall
	#38	Document stores

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Website

www.harperdb.io

www.snowflake.com

sqrrl.com

Technical documentation

docs.harperdb.io/docs

docs.snowflake.net/manuals/index.html

Developer

FatCloud

HarperDB

Snowflake Computing Inc.

Amazon

originally Sqrrl Data, Inc.

Teradata

Initial release

2012

2017

2014

2012

2005

Current release

3.1, August 2021

License

Commercial or Open Source

commercial

free community edition available

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Node.js

Java

Server operating systems

Windows

Linux
OS X

hosted

Linux

Data scheme

schema-free

dynamic schema

yes

support of semi-structured data formats (JSON, XML, Avro)

schema-free

Flexible Schema (defined schema, partial schema, schema free)

defined schema within the relational store; partial schema or schema free in the Aster File Store

Typing

predefined data types such as float or date

yes

JSON data types

yes

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

yes

in Aster File Store

Secondary indexes

yes

SQL

Support of SQL

Via inetgration in SQL Server

SQL-like data manipulation statements

yes

APIs and other access methods

.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

CLI Client
JDBC
ODBC

Accumulo Shell
Java API
JDBC
ODBC
RESTful HTTP API
Thrift

ADO.NET
JDBC
ODBC
OLE DB

Supported programming languages

.Net
C
C#
C++
ColdFusion
D
Dart
Delphi
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
MatLab
Objective C
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Rust
Scala
Swift

JavaScript (Node.js)
Python

Actionscript
C

using GLib
C#
C++
Cocoa
Delphi
Erlang
Go
Haskell
Java
JavaScript
OCaml
Perl
PHP
Python
Ruby
Smalltalk

C
C#
C++
Java
Python
R

Server-side scripts

Stored procedures

yes

via applications

Custom Functions

since release 3.1

user defined functions

R packages

Triggers

yes

via applications

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

A table resides as a whole on one (or more) nodes in a cluster

yes

Sharding

making use of Hadoop

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes

the nodes on which a table resides can be defined

yes

selectable replication factor

making use of Hadoop

yes

Dimension tables are replicated across all nodes in the cluster. The number of replicas for the file store can be configured.

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

SQL Map-Reduce Framework

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

Immediate Consistency

Document store kept consistent with combination of global timestamping, row-level transactions, and server-side consistency resolution.

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

Atomic execution of specific operations

ACID

Atomic updates per row, document, or graph entity

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, using LMDB

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

Can implement custom security layer via applications

Access rights for users and roles

Users with fine-grained authorization concept, user roles and pluggable authentication

Cell-level Security, Data-Centric Security, Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC)

fine grained access rights according to SQL-standard

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