DBMS > Google Cloud Bigtable vs. Microsoft Azure Data Explorer vs. Snowflake vs. Splice Machine

System Properties Comparison Google Cloud Bigtable vs. Microsoft Azure Data Explorer vs. Snowflake vs. Splice Machine

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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 managed big data interactive analytics platform

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

Primary database model

Key-value store
Wide column store

Relational DBMS

column oriented

Relational DBMS

Secondary database models

Document store

If a column is of type dynamic docs.microsoft.com/en-us/azure/kusto/query/scalar-data-types/dynamic then it's possible to add arbitrary JSON documents in this cell
Event Store

this is the general usage pattern at Microsoft. Billing, Logs, Telemetry events are stored in ADX and the state of an individual entity is defined by the arg_max(timestamps)
Spatial DBMS
Search engine

support for complex search expressions docs.microsoft.com/en-us/azure/kusto/query/parseoperator FTS, Geospatial docs.microsoft.com/en-us/azure/kusto/query/geo-point-to-geohash-function distributed search -> ADX acts as a distributed search engine
Time Series DBMS

see docs.microsoft.com/en-us/azure/data-explorer/time-series-analysis

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

Score	3.26
Rank	#92	Overall
	#13	Key-value stores
	#8	Wide column stores

Score	4.38
Rank	#77	Overall
	#41	Relational DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

Website

cloud.google.com/bigtable

azure.microsoft.com/services/data-explorer

www.snowflake.com

splicemachine.com

Technical documentation

cloud.google.com/bigtable/docs

docs.microsoft.com/en-us/azure/data-explorer

docs.snowflake.net/manuals/index.html

splicemachine.com/how-it-works

Developer

Google

Microsoft

Snowflake Computing Inc.

Splice Machine

Initial release

2015

2019

2014

Current release

cloud service with continuous releases

3.1, March 2021

License

Commercial or Open Source

commercial

Open Source

AGPL 3.0, 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

Java

Server operating systems

hosted

Linux
OS X
Solaris
Windows

Data scheme

schema-free

Fixed schema with schema-less datatypes (dynamic)

yes

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

yes

Typing

predefined data types such as float or date

yes

bool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/en-us/azure/kusto/query/scalar-data-types

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

all fields are automatically indexed

yes

SQL

Support of SQL

Kusto Query Language (KQL), SQL subset

yes

APIs and other access methods

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

Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API

CLI Client
JDBC
ODBC

JDBC
Native Spark Datasource
ODBC

Supported programming languages

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

.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R

JavaScript (Node.js)
Python

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

Server-side scripts

Stored procedures

Yes, possible languages: KQL, Python, R

user defined functions

yes

Java

Triggers

yes

see docs.microsoft.com/en-us/azure/kusto/management/updatepolicy

similar concept for controling cloud resources

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

yes

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

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

yes

Implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

yes

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Spark connector (open source): github.com/Azure/azure-kusto-spark

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

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

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

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

yes, multi-version concurrency control (MVCC)

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

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

Azure Active Directory Authentication

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

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

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