DBMS > Google BigQuery vs. HBase vs. Microsoft Access vs. ScyllaDB

System Properties Comparison Google BigQuery vs. HBase vs. Microsoft Access vs. ScyllaDB

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

Wide-column store based on Apache Hadoop and on concepts of BigTable

Microsoft Access combines a backend RDBMS (JET / ACE Engine) with a GUI frontend for data manipulation and queries.

The Access frontend is often used for accessing other datasources (DBMS, Excel, etc.)

Cassandra and DynamoDB compatible wide column store

Primary database model

Secondary database models

Key-value store

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

Score	61.90
Rank	#19	Overall
	#13	Relational DBMS

Score	31.25
Rank	#26	Overall
	#2	Wide column stores

Score	105.40
Rank	#11	Overall
	#8	Relational DBMS

Score	5.27
Rank	#67	Overall
	#5	Wide column stores

Website

cloud.google.com/bigquery

hbase.apache.org

www.microsoft.com/en-us/microsoft-365/access

www.scylladb.com

Technical documentation

cloud.google.com/bigquery/docs

hbase.apache.org/book.html

developer.microsoft.com/en-us/access

docs.scylladb.com

Developer

Google

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Microsoft

ScyllaDB

Initial release

2010

2008

1992

2015

Current release

2.3.4, January 2021

1902 (16.0.11328.20222), March 2019

ScyllaDB Open Source 5.4.1, January 2024

License

Commercial or Open Source

commercial

Open Source

Apache version 2

commercial

Bundled with Microsoft Office

Open Source

Open Source (AGPL), commercial license available

Cloud-based only

Only available as a cloud service

yes

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Database as a Service

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Implementation language

Java

C++

Server operating systems

hosted

Linux
Unix
Windows

using Cygwin

Windows

Not a real database server, but making use of DLLs

Linux

Data scheme

yes

schema-free, schema definition possible

yes

schema-free

Typing

predefined data types such as float or date

yes

options to bring your own types, AVRO

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

cluster global secondary indices

SQL

Support of SQL

yes

but not compliant to any SQL standard

SQL-like DML and DDL statements (CQL)

APIs and other access methods

RESTful HTTP/JSON API

Java API
RESTful HTTP API
Thrift

ADO.NET
DAO
ODBC
OLE DB

Proprietary protocol (CQL)

compatible with CQL (Cassandra Query Language, an SQL-like language)
RESTful HTTP API (DynamoDB compatible)
Thrift

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C
C#
C++
Groovy
Java
PHP
Python
Scala

C
C#
C++
Delphi
Java (JDBC-ODBC)
VBA
Visual Basic.NET

For CQL interface: C#, C++, Clojure, Erlang, Go, Haskell, Java, JavaScript, Node.js, Perl, PHP, Python, Ruby, Rust, Scala
For DynamoDB interface: .Net, ColdFusion, Erlang, Groovy, Java, JavaScript, Perl, PHP, Python, Ruby

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

Coprocessors in Java

yes

since Access 2010 using the ACE-engine

yes, Lua

Triggers

yes

since Access 2010 using the ACE-engine

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

none

selectable replication factor

Representation of geographical distribution of servers is possible

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency

Eventual Consistency
Tunable Consistency

can be individually decided for each write operation

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

Single row ACID (across millions of columns)

ACID

but no files for transaction logging

Atomicity and isolation are supported for single operations

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

but no files for transaction logging

yes

In-memory capabilities

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

yes

in-memory tables

User concepts

Access control

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

Google Cloud Identity & Access Management (IAM)

Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

a simple user-level security was built in till version Access 2003

Access rights for users can be defined per object

More information provided by the system vendor

Specific characteristics

ScyllaDB is engineered to deliver predictable performance at scale. It’s adopted...
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Competitive advantages

Highly-performant (efficiently utilizes full resources of a node and network; millions...
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Typical application scenarios

ScyllaDB is ideal for applications that require high throughput and low latency at...
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Key customers

Discord, Epic Games, Expedia, Zillow, Comcast, Disney+ Hotstar, Samsung, ShareChat,...
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Market metrics

ScyllaDB typically offers ~75% total cost of ownership savings, with ~5X higher throughput...
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Licensing and pricing models

ScyllaDB Open Source - free open source software (AGPL) ScyllaDB Enterprise - subscription-based...
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