DBMS > Google Cloud Bigtable vs. Kinetica vs. Microsoft Azure Synapse Analytics

System Properties Comparison Google Cloud Bigtable vs. Kinetica vs. Microsoft Azure Synapse Analytics

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Editorial information provided by DB-Engines

Name

Description

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

Fully vectorized database across both GPUs and CPUs

Elastic, large scale data warehouse service leveraging the broad eco-system of SQL Server

Primary database model

Key-value store
Wide column store

Relational DBMS

Secondary database models

Spatial DBMS
Time Series DBMS

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

Score	3.58
Rank	#92	Overall
	#14	Key-value stores
	#8	Wide column stores

Score	0.69
Rank	#234	Overall
	#107	Relational DBMS

Score	22.71
Rank	#30	Overall
	#18	Relational DBMS

Website

cloud.google.com/bigtable

www.kinetica.com

azure.microsoft.com/services/synapse-analytics

Technical documentation

cloud.google.com/bigtable/docs

docs.kinetica.com

docs.microsoft.com/azure/synapse-analytics

Developer

Google

Kinetica

Microsoft

Initial release

2015

2012

2016

Current release

7.1, August 2021

License

Commercial or Open Source

commercial

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

C, C++

C++

Server operating systems

hosted

Linux

hosted

Data scheme

schema-free

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.

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

APIs and other access methods

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

JDBC
ODBC
RESTful HTTP API

ADO.NET
JDBC
ODBC

Supported programming languages

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

C++
Java
JavaScript (Node.js)
Python

C#
Java
PHP

Server-side scripts

Stored procedures

user defined functions

Transact SQL

Triggers

yes

triggers when inserted values for one or more columns fall within a specified range

Partitioning methods

Methods for storing different data on different nodes

Sharding

Sharding, horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

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

Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

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

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

yes

docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-table-constraints

Transaction concepts

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

Atomic single-row operations

ACID

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

GPU vRAM or System RAM

User concepts

Access control

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

Access rights for users and roles on table level

yes

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