DBMS > BigchainDB vs. CrateDB vs. Snowflake vs. Splice Machine vs. WakandaDB

System Properties Comparison BigchainDB vs. CrateDB vs. Snowflake vs. Splice Machine vs. WakandaDB

Editorial information provided by DB-Engines

Name

Description

BigchainDB is scalable blockchain database offering decentralization, immutability and native assets

Distributed Database based on Lucene

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

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

WakandaDB is embedded in a server that provides a REST API and a server-side javascript engine to access data

Primary database model

Document store

Document store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS

Relational DBMS

Object oriented DBMS

Secondary database models

Relational DBMS

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

Score	0.76
Rank	#216	Overall
	#36	Document stores

Score	0.69
Rank	#226	Overall
	#37	Document stores
	#5	Spatial DBMS
	#16	Search engines
	#19	Time Series DBMS
	#11	Vector DBMS

Score	133.72
Rank	#7	Overall
	#5	Relational DBMS

Score	0.54
Rank	#244	Overall
	#114	Relational DBMS

Score	0.03
Rank	#364	Overall
	#17	Object oriented DBMS

Website

Technical documentation

bigchaindb.readthedocs.io/en/latest

cratedb.com/docs

docs.snowflake.net/manuals/index.html

splicemachine.com/how-it-works

wakanda.github.io/doc

Developer

Crate

Snowflake Computing Inc.

Splice Machine

Wakanda SAS

Initial release

2016

2013

2014

2012

Current release

5.8.1, August 2024

3.1, March 2021

2.7.0 (April 29, 2019), April 2019

License

Commercial or Open Source

Open Source

AGPL v3

Open Source

commercial

Open Source

AGPL 3.0, commercial license available

Open Source

AGPLv3, extended commercial license 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

Python

Java

C++, JavaScript

Server operating systems

Linux

All Operating Systems, including Kubernetes with CrateDB Kubernetes Operator support

hosted

Linux
OS X
Solaris
Windows

Linux
OS X
Windows

Data scheme

schema-free

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

yes

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

yes

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

Secondary indexes

yes

SQL

Support of SQL

yes, but no triggers and constraints, and PostgreSQL compatibility

yes

APIs and other access methods

CLI Client
RESTful HTTP API

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

CLI Client
JDBC
ODBC

JDBC
Native Spark Datasource
ODBC

RESTful HTTP API

Supported programming languages

Go
Haskell
Java
JavaScript
Python
Ruby

.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

JavaScript (Node.js)
Python

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

JavaScript

Server-side scripts

Stored procedures

user defined functions (Javascript)

user defined functions

yes

Java

yes

Triggers

similar concept for controling cloud resources

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

Shared Nothhing Auto-Sharding, Columnar Partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Configurable replication on table/partition-level

yes

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Read-after-write consistency on record level

Immediate Consistency

Foreign keys

Referential integrity

yes

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes,with MongoDB ord RethinkDB

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

yes

rights management via user accounts

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

Access rights for users, groups and roles according to SQL-standard

yes

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