DBMS > Databend vs. Drizzle vs. Google BigQuery vs. Google Cloud Bigtable vs. Memgraph

System Properties Comparison Databend vs. Drizzle vs. Google BigQuery vs. Google Cloud Bigtable vs. Memgraph

Editorial information provided by DB-Engines

Name

Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.

Description

An open-source, elastic, and workload-aware cloud data warehouse designed to meet businesses' massive-scale analytics needs at low cost and with low complexity

MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.

Large scale data warehouse service with append-only tables

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

An open source graph database built for real-time streaming and compatible with Neo4j

Primary database model

Relational DBMS

Key-value store
Wide column store

Graph DBMS

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

Score	0.28
Rank	#287	Overall
	#131	Relational DBMS

Score	52.67
Rank	#19	Overall
	#13	Relational DBMS

Score	2.97
Rank	#92	Overall
	#15	Key-value stores
	#8	Wide column stores

Score	2.91
Rank	#94	Overall
	#7	Graph DBMS

Website

github.com/datafuselabs/databend
www.databend.com

cloud.google.com/bigquery

cloud.google.com/bigtable

memgraph.com

Technical documentation

docs.databend.com

cloud.google.com/bigquery/docs

cloud.google.com/bigtable/docs

memgraph.com/docs

Social network pages

Developer

Databend Labs

Drizzle project, originally started by Brian Aker

Google

Memgraph Ltd

Initial release

2021

2008

2010

2015

2017

Current release

1.0.59, April 2023

7.2.4, September 2012

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

GNU GPL

commercial

Open Source

BSL 1.1; commercial license for enterprise edition 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

Rust

C++

C and C++

Server operating systems

hosted
Linux
macOS

FreeBSD
Linux
OS X

hosted

Linux

Data scheme

yes

schema-free

schema-free and schema-optional

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.

Secondary indexes

yes

SQL

Support of SQL

yes

with proprietary extensions

yes

APIs and other access methods

CLI Client
JDBC
RESTful HTTP API

JDBC

RESTful HTTP/JSON API

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

Bolt protocol
Cypher query language

Supported programming languages

Go
Java
JavaScript (Node.js)
Python
Rust

C
C++
Java
PHP

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

.Net
C
C++
Elixir
Go
Haskell
Java
JavaScript
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

user defined functions

in JavaScript

Triggers

hooks for callbacks inside the server can be used.

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

Sharding

dynamic graph partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication
Source-replica replication

Internal replication in Colossus, and regional replication between two clusters in different zones

Multi-source replication using RAFT

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

Immediate Consistency

Foreign keys

Referential integrity

yes

relationships in graphs

Transaction concepts

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

yes

ACID

Since BigQuery is designed for querying data

Atomic single-row operations

ACID

with snapshot isolation

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

Durability

Support for making data persistent

yes

with periodic snapshot and write-ahead logging (WAL) of changes

In-memory capabilities

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

yes

User concepts

Access control

Users with fine-grained authorization concept, user roles

Pluggable authentication mechanisms

e.g. LDAP, HTTP

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

Google Cloud Identity & Access Management (IAM)

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

Users, roles and permissions

More information provided by the system vendor

Specific characteristics

Memgraph directly connects to your streaming infrastructure so you and your team...
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Competitive advantages

Business Source License ensures a future for the Memgraph community MAGE algorithm...
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Typical application scenarios

Graph algorithms in bioinformatics Social network analysis Cryptocurrency network...
» more

Licensing and pricing models

You can check out our pricing model and licenses on the company website .
» more

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