DBMS > Apache Impala vs. Google BigQuery vs. Microsoft Azure Cosmos DB vs. Snowflake vs. SurrealDB

System Properties Comparison Apache Impala vs. Google BigQuery vs. Microsoft Azure Cosmos DB vs. Snowflake vs. SurrealDB

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

Large scale data warehouse service with append-only tables

Globally distributed, horizontally scalable, multi-model database service

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

A fully ACID transactional, developer-friendly, multi-model DBMS

Primary database model

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Document store
Graph DBMS

Secondary database models

Document store

Spatial DBMS

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

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	27.71
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Score	1.02
Rank	#190	Overall
	#33	Document stores
	#18	Graph DBMS

Website

impala.apache.org

cloud.google.com/bigquery

azure.microsoft.com/services/cosmos-db

www.snowflake.com

surrealdb.com

Technical documentation

impala.apache.org/impala-docs.html

cloud.google.com/bigquery/docs

learn.microsoft.com/azure/cosmos-db

docs.snowflake.net/manuals/index.html

surrealdb.com/docs

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Google

Microsoft

Snowflake Computing Inc.

SurrealDB Ltd

Initial release

2013

2010

2014

2022

Current release

4.1.0, June 2022

v1.5.0, May 2024

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

Open Source

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Rust

Server operating systems

Linux

hosted

Linux
macOS
Windows

Data scheme

yes

schema-free

yes

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

schema-free

Typing

predefined data types such as float or date

yes

JSON 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

yes

All properties auto-indexed by default

SQL

Support of SQL

SQL-like DML and DDL statements

yes

SQL-like query language

yes

SQL-like query language

APIs and other access methods

JDBC
ODBC

RESTful HTTP/JSON API

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

CLI Client
JDBC
ODBC

GraphQL
RESTful HTTP API
WebSocket

Supported programming languages

All languages supporting JDBC/ODBC

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

JavaScript (Node.js)
Python

Deno
Go
JavaScript (Node.js)
Rust

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

user defined functions

in JavaScript

JavaScript

user defined functions

Triggers

JavaScript

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Implicit feature of the cloud service

yes

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes

Implicit feature of the cloud service

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Since BigQuery is designed for querying data

Multi-item ACID transactions with snapshot isolation within a partition

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.

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

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

Google Cloud Identity & Access Management (IAM)

Access rights can be defined down to the item level

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

yes, based on authentication and database rules

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