DBMS > atoti vs. CrateDB vs. Google BigQuery vs. Hazelcast

System Properties Comparison atoti vs. CrateDB vs. Google BigQuery vs. Hazelcast

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Editorial information provided by DB-Engines

Name

Description

An in-memory DBMS combining transactional and analytical processing to handle the aggregation of ever-changing data.

Distributed Database based on Lucene

Large scale data warehouse service with append-only tables

A widely adopted in-memory data grid

Primary database model

Object oriented DBMS

Document store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS

Relational DBMS

Key-value store

Secondary database models

Relational DBMS

Document store

JSON support with IMDG 3.12

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

Score	0.61
Rank	#243	Overall
	#11	Object oriented DBMS

Score	0.71
Rank	#227	Overall
	#37	Document stores
	#5	Spatial DBMS
	#16	Search engines
	#19	Time Series DBMS
	#10	Vector DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

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

Website

atoti.io

cratedb.com

cloud.google.com/bigquery

hazelcast.com

Technical documentation

docs.atoti.io

cratedb.com/docs

cloud.google.com/bigquery/docs

hazelcast.org/imdg/docs

Developer

ActiveViam

Crate

Google

Hazelcast

Initial release

2013

2010

2008

Current release

5.3.6, November 2023

License

Commercial or Open Source

commercial

free versions available

Open Source

commercial

Open Source

Apache Version 2; commercial licenses available

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 Operating Systems, including Kubernetes with CrateDB Kubernetes Operator support

hosted

All OS with a Java VM

Data scheme

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

yes

schema-free

Typing

predefined data types such as float or date

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

Secondary indexes

yes

SQL

Support of SQL

Multidimensional Expressions (MDX)

yes, but no triggers and constraints, and PostgreSQL compatibility

yes

SQL-like query language

APIs and other access methods

ADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API

RESTful HTTP/JSON API

JCache
JPA
Memcached protocol
RESTful HTTP API

Supported programming languages

.NET
Erlang
Go

community maintained client
Java
JavaScript (Node.js)

community maintained client
Perl

community maintained client
PHP
Python
R
Ruby

community maintained client
Scala

community maintained client

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

Server-side scripts

Stored procedures

Python

user defined functions (Javascript)

user defined functions

in JavaScript

yes

Event Listeners, Executor Services

Triggers

yes

Events

Partitioning methods

Methods for storing different data on different nodes

Sharding, horizontal partitioning

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Configurable replication on table/partition-level

yes

Replicated Map

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Read-after-write consistency on record level

Immediate Consistency

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Foreign keys

Referential integrity

Transaction concepts

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

unique row identifiers can be used for implementing an optimistic concurrency control strategy

Since BigQuery is designed for querying data

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

Concurrency

Support for concurrent manipulation of data

yes, multi-version concurrency control (MVCC)

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

rights management via user accounts

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

Role-based access control

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