DBMS > Apache Impala vs. Couchbase vs. Snowflake

System Properties Comparison Apache Impala vs. Couchbase vs. Snowflake

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

A distributed document store with integrated cache, a powerful search engine, in-built operational and analytical capabilities, and an embedded mobile database

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

Primary database model

Relational DBMS

Document store
Vector DBMS

Relational DBMS

Secondary database models

Document store

Key-value store

originating from the former Membase product and supporting the Memcached protocol
Spatial DBMS

using the Geocouch extension
Search engine
Time Series DBMS
Vector DBMS
Wide column store

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

Score	11.22
Rank	#41	Overall
	#25	Relational DBMS

Score	14.04
Rank	#38	Overall
	#5	Document stores
	#3	Vector DBMS

Score	168.09
Rank	#6	Overall
	#5	Relational DBMS

Website

impala.apache.org

www.couchbase.com

www.snowflake.com

Technical documentation

impala.apache.org/impala-docs.html

docs.couchbase.com

docs.snowflake.net/manuals/index.html

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Couchbase, Inc.

Snowflake Computing Inc.

Initial release

2013

2011

2014

Current release

4.1.0, June 2022

Server: 7.6, March 2024; Mobile: 3.3, August 2024; Couchbase Capella (DBaaS), September 2024

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Business Source License (BSL 1.1); Commercial licenses also available

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++, Go and Erlang

Server operating systems

Linux

Linux
OS X
Windows

hosted

Data scheme

yes

schema-free

yes

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

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.

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

SQL++, extends ANSI SQL to JSON for operational, transactional, and analytic use cases

yes

APIs and other access methods

JDBC
ODBC

CLI Client
HTTP REST
Kafka Connector
Native language bindings for CRUD, Query, Search and Analytics APIs
Spark Connector
Spring Data

CLI Client
JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

.Net
C
Go
Java
JavaScript

Node.js
Kotlin
PHP
Python
Ruby
Scala

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

Functions and timers in JavaScript and UDFs in Java, Python, SQL++

user defined functions

Triggers

yes

via the TAP protocol

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

Automatic Sharding

yes

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Multi-source replication

including cross data center replication
Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Eventual Consistency
Immediate Consistency

selectable on a per-operation basis

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

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

Ephemeral buckets

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

User and Administrator separation with password-based and LDAP integrated Authentication. Role-base access control.

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

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More resources

DB-Engines blog posts

Couchbase climbs up the DB-Engines Ranking, increasing its popularity by 10% every month
2 June 2014, Matthias Gelbmann

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Snowflake is the Database Management System of the Year 2024
16 January 2025, Tom Russell

Snowflake is the DBMS of the Year 2022, defending the title from last year
3 January 2023, Matthias Gelbmann, Paul Andlinger

Snowflake is the DBMS of the Year 2021
3 January 2022, Paul Andlinger, Matthias Gelbmann

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Recent citations in the news

Apache Impala becomes Top-Level Project
28 November 2017, SD Times

Hudi: Uber Engineering’s Incremental Processing Framework on Apache Hadoop
12 March 2017, Uber

Cloudera brings Apache Iceberg data lake format to its Data Platform
30 June 2022, SiliconANGLE

Apache Doris just ‘graduated’: Why care about this SQL data warehouse
24 June 2022, InfoWorld

Apache Iceberg is now available on the Cloudera Data Platform
4 July 2022, techzine.eu

provided by Google News

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4 March 2025, PR Newswire

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9 April 2025, Benzinga

How Much Upside is Left in Couchbase (BASE)? Wall Street Analysts Think 46.58%
15 April 2025, Yahoo Finance

Couchbase price target lowered to $16 from $22 at Piper Sandler
23 April 2025, TipRanks

Couchbase’s Regine Nelson brings authentic leadership to employee engagement
26 February 2025, Ragan Communications

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28 April 2025, atrium.ai

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17 February 2025, Snowflake

Snowflake Horizon Catalog: Data Governance & Discovery
17 February 2025, Snowflake

provided by Google News

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