DBMS > Google BigQuery vs. Hawkular Metrics vs. Microsoft Azure Synapse Analytics vs. Microsoft Azure Table Storage vs. RRDtool

System Properties Comparison Google BigQuery vs. Hawkular Metrics vs. Microsoft Azure Synapse Analytics vs. Microsoft Azure Table Storage vs. RRDtool

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

Hawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.

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

A Wide Column Store for rapid development using massive semi-structured datasets

Industry standard data logging and graphing tool for time series data. RRD is an acronym for round-robin database.

The data is stored in a circular buffer, thus the system storage footprint remains constant over time.

Primary database model

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

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	0.08
Rank	#366	Overall
	#39	Time Series DBMS

Score	19.93
Rank	#31	Overall
	#19	Relational DBMS

Score	4.04
Rank	#77	Overall
	#6	Wide column stores

Score	1.90
Rank	#132	Overall
	#11	Time Series DBMS

Website

cloud.google.com/bigquery

www.hawkular.org

azure.microsoft.com/services/synapse-analytics

azure.microsoft.com/en-us/services/storage/tables

oss.oetiker.ch/rrdtool

Technical documentation

cloud.google.com/bigquery/docs

www.hawkular.org/hawkular-metrics/docs/user-guide

docs.microsoft.com/azure/synapse-analytics

oss.oetiker.ch/rrdtool/doc

Developer

Google

Community supported by Red Hat

Microsoft

Tobias Oetiker

Initial release

2010

2014

2016

2012

1999

Current release

1.8.0, 2022

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

Open Source

GPL V2 and FLOSS

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

C++

Implementations in Java (e.g. RRD4J) and C# available

Server operating systems

hosted

Linux
OS X
Windows

hosted

HP-UX
Linux

Data scheme

yes

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

Numeric data only

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.

Exporting into and restoring from XML files possible

Secondary indexes

yes

SQL

Support of SQL

yes

APIs and other access methods

RESTful HTTP/JSON API

HTTP REST

ADO.NET
JDBC
ODBC

RESTful HTTP API

in-process shared library
Pipes

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Go
Java
Python
Ruby

C#
Java
PHP

.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby

with librrd library
C#

with a different implementation of RRDTool
Java

with a different implementation of RRDTool
JavaScript (Node.js)

with a different implementation of RRDTool
Lua
Perl
PHP

with a wrapper library
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

in JavaScript

Transact SQL

Triggers

yes

via Hawkular Alerting

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

based on Cassandra

Sharding, horizontal partitioning

Sharding

Implicit feature of the cloud service

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

yes

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

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

none

Foreign keys

Referential integrity

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

Since BigQuery is designed for querying data

ACID

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

by using the rrdcached daemon

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

Google Cloud Identity & Access Management (IAM)

yes

Access rights based on private key authentication or shared access signatures

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