DBMS > Google BigQuery vs. Manticore Search vs. Microsoft Azure Synapse Analytics vs. QuestDB vs. Snowflake

System Properties Comparison Google BigQuery vs. Manticore Search vs. Microsoft Azure Synapse Analytics vs. QuestDB vs. Snowflake

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

Multi-storage database for search, including full-text search.

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

A high performance open source SQL database for time series data

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

Primary database model

Secondary database models

Time Series DBMS

using the Manticore Columnar Library

Relational DBMS

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

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	0.29
Rank	#302	Overall
	#21	Search engines

Score	19.93
Rank	#31	Overall
	#19	Relational DBMS

Score	2.70
Rank	#105	Overall
	#8	Time Series DBMS

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Website

cloud.google.com/bigquery

manticoresearch.com

azure.microsoft.com/services/synapse-analytics

questdb.io

www.snowflake.com

Technical documentation

cloud.google.com/bigquery/docs

manual.manticoresearch.com

docs.microsoft.com/azure/synapse-analytics

questdb.io/docs

docs.snowflake.net/manuals/index.html

Developer

Google

Manticore Software

Microsoft

QuestDB Technology Inc

Snowflake Computing Inc.

Initial release

2010

2017

2016

2014

Current release

6.0, February 2023

License

Commercial or Open Source

commercial

Open Source

GPL version 2

commercial

Open Source

Apache 2.0

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++

Java (Zero-GC), C++, Rust

Server operating systems

hosted

FreeBSD
Linux
macOS
Windows

hosted

Linux
macOS
Windows

hosted

Data scheme

yes

Fixed schema

yes

schema-free via InfluxDB Line Protocol

yes

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

Typing

predefined data types such as float or date

yes

Int, Bigint, Float, Timestamp, Bit, Int array, Bigint array, JSON, Boolean

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.

Can index from XML

yes

Secondary indexes

yes

full-text index on all search fields

yes

SQL

Support of SQL

yes

SQL-like query language

yes

SQL with time-series extensions

yes

APIs and other access methods

RESTful HTTP/JSON API

Binary API
RESTful HTTP/JSON API
RESTful HTTP/SQL API
SQL over MySQL

ADO.NET
JDBC
ODBC

HTTP REST
InfluxDB Line Protocol (TCP/UDP)
JDBC
PostgreSQL wire protocol

CLI Client
JDBC
ODBC

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Elixir
Go
Java
JavaScript (Node.js)
Perl
PHP
Python

C#
Java
PHP

PostgreSQL driver
C++
Go
Java
JavaScript (Node.js)
Python
Rust

over HTTP

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions

in JavaScript

user defined functions

Transact SQL

user defined functions

Triggers

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Partitioning is done manually, search queries against distributed index is supported

Sharding, horizontal partitioning

horizontal partitioning (by timestamps)

yes

Replication methods

Methods for redundantly storing data on multiple nodes

Synchronous replication based on Galera library

yes

Source-replica replication with eventual consistency

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

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

yes

Transaction concepts

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

Since BigQuery is designed for querying data

yes

isolated transactions for atomic changes and binary logging for safe writes

ACID

ACID for single-table writes

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

The original contents of fields are not stored in the Manticore index.

yes

In-memory capabilities

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

yes

through memory mapped files

User concepts

Access control

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

Google Cloud Identity & Access Management (IAM)

yes

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

More information provided by the system vendor

Specific characteristics

Relational model with native time series support Column-based storage and time partitioned...
» more

Competitive advantages

High ingestion throughput: peak of 4M rows/sec (TSBS Benchmark) Code optimizations...
» more

Typical application scenarios

Financial tick data Industrial IoT Application Metrics Monitoring
» more

Key customers

Banks & Hedge funds, Yahoo, OKX, Airbus, Aquis Exchange, Net App, Cloudera, Airtel,...
» more

Licensing and pricing models

Open source Apache 2.0 QuestDB Enterprise QuestDB Cloud
» more

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