DBMS > Hazelcast vs. Prometheus vs. Snowflake vs. Sqrrl vs. Warp 10

System Properties Comparison Hazelcast vs. Prometheus vs. Snowflake vs. Sqrrl vs. Warp 10

Editorial information provided by DB-Engines

Name

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

Description

A widely adopted in-memory data grid

Open-source Time Series DBMS and monitoring system

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

Adaptable, secure NoSQL built on Apache Accumulo

TimeSeries DBMS specialized on timestamped geo data based on LevelDB or HBase

Primary database model

Key-value store

Time Series DBMS

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Time Series DBMS

Secondary database models

Document store

JSON support with IMDG 3.12

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

Score	5.46
Rank	#61	Overall
	#7	Key-value stores

Score	7.69
Rank	#50	Overall
	#3	Time Series DBMS

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Score	0.14
Rank	#344	Overall
	#32	Time Series DBMS

Website

sqrrl.com

Technical documentation

hazelcast.org/imdg/docs

prometheus.io/docs

docs.snowflake.net/manuals/index.html

www.warp10.io/content/02_Getting_started

Developer

Hazelcast

Snowflake Computing Inc.

Amazon

originally Sqrrl Data, Inc.

SenX

Initial release

2008

2015

2014

2012

2015

Current release

5.3.6, November 2023

License

Commercial or Open Source

Open Source

Apache Version 2; commercial licenses available

Open Source

Apache 2.0

commercial

Open Source

Apache License 2.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

Server operating systems

All OS with a Java VM

Linux
Windows

hosted

Linux

Linux
OS X
Windows

Data scheme

schema-free

yes

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

schema-free

Typing

predefined data types such as float or date

yes

Numeric data only

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

the object must implement a serialization strategy

Import of XML data possible

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

yes

APIs and other access methods

JCache
JPA
Memcached protocol
RESTful HTTP API

RESTful HTTP/JSON API

CLI Client
JDBC
ODBC

Accumulo Shell
Java API
JDBC
ODBC
RESTful HTTP API
Thrift

HTTP API
Jupyter
WebSocket

Supported programming languages

.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala

.Net
C++
Go
Haskell
Java
JavaScript (Node.js)
Python
Ruby

JavaScript (Node.js)
Python

Actionscript
C

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

Server-side scripts

Stored procedures

yes

Event Listeners, Executor Services

user defined functions

yes

WarpScript

Triggers

yes

Events

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

Sharding

making use of Hadoop

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Replicated Map

yes

by Federation

yes

selectable replication factor

making use of Hadoop

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

none

Immediate Consistency

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

Immediate Consistency

based on HBase

Foreign keys

Referential integrity

yes

Transaction concepts

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

one or two-phase-commit; repeatable reads; read commited

ACID

Atomic updates per row, document, or graph entity

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

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

Role-based access control

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)

Mandatory use of cryptographic tokens, containing fine-grained authorizations

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.