DBMS > Google BigQuery vs. KeyDB vs. Riak TS vs. SAP HANA

System Properties Comparison Google BigQuery vs. KeyDB vs. Riak TS vs. SAP HANA

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

An ultra-fast, open source Key-value store fully compatible with Redis API, modules, and protocols

Riak TS is a distributed NoSQL database optimized for time series data and based on Riak KV

In-memory, column based data store. Available as appliance or cloud service

Primary database model

Secondary database models

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

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

Score	52.67
Rank	#19	Overall
	#13	Relational DBMS

Score	0.63
Rank	#232	Overall
	#32	Key-value stores

Score	0.17
Rank	#318	Overall
	#27	Time Series DBMS

Score	41.32
Rank	#23	Overall
	#16	Relational DBMS

Website

cloud.google.com/bigquery

github.com/Snapchat/KeyDB
keydb.dev

www.sap.com/products/hana.html

Technical documentation

cloud.google.com/bigquery/docs

docs.keydb.dev

www.tiot.jp/riak-docs/riak/ts/latest

help.sap.com/hana

Developer

Google

EQ Alpha Technology Ltd.

Open Source, formerly Basho Technologies

SAP

Initial release

2010

2019

2015

2010

Current release

3.0.0, September 2022

2.0 SPS07 (April 4, 2023), April 2023

License

Commercial or Open Source

commercial

Open Source

BSD-3

Open Source

commercial

Cloud-based only

Only available as a cloud service

yes

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++

Erlang

Server operating systems

hosted

Linux

Linux
OS X

Appliance or cloud-service

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

partial

Supported data types are strings, hashes, lists, sets and sorted sets, bit arrays, hyperloglogs and geospatial indexes

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.

Secondary indexes

yes

by using the Redis Search module

restricted

yes

SQL

Support of SQL

yes

yes, limited

yes

APIs and other access methods

RESTful HTTP/JSON API

Proprietary protocol

RESP - REdis Serialization Protoco

HTTP API
Native Erlang Interface

JDBC
ODBC

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C
C#
C++
Clojure
Crystal
D
Dart
Elixir
Erlang
Fancy
Go
Haskell
Haxe
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Objective-C
OCaml
Pascal
Perl
PHP
Prolog
Pure Data
Python
R
Rebol
Ruby
Rust
Scala
Scheme
Smalltalk
Swift
Tcl
Visual Basic

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Server-side scripts

Stored procedures

user defined functions

in JavaScript

Lua

Erlang

SQLScript, R

Triggers

yes

pre-commit hooks and post-commit hooks

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

yes

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

selectable replication factor

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Strong eventual consistency with CRDTs

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

links between datasets can be stored

yes

Transaction concepts

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

Since BigQuery is designed for querying data

Optimistic locking, atomic execution of commands blocks and scripts

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Configurable mechanisms for persistency via snapshots and/or operations logs

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)

simple password-based access control and ACL

yes

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