DBMS > Apache Impala vs. Google BigQuery vs. IBM Db2 vs. Sqrrl

System Properties Comparison Apache Impala vs. Google BigQuery vs. IBM Db2 vs. Sqrrl

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Editorial information provided by DB-Engines

Name

Sqrrl has been acquired by Amazon and became a part of Amazon Web Services. It has been removed from the DB-Engines ranking.

Description

Analytic DBMS for Hadoop

Large scale data warehouse service with append-only tables

Common in IBM host environments, 2 different versions for host and Windows/Linux

Adaptable, secure NoSQL built on Apache Accumulo

Primary database model

Relational DBMS

Since Version 10.5 support for JSON/BSON documents compatible with MongoDB

Document store
Graph DBMS
Key-value store
Wide column store

Secondary database models

Document store

Document store
RDF store

in Db2 LUW (Linux, Unix, Windows)
Spatial DBMS

with Db2 Spatial Extender

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	125.90
Rank	#9	Overall
	#6	Relational DBMS

Website

impala.apache.org

cloud.google.com/bigquery

www.ibm.com/products/db2

sqrrl.com

Technical documentation

impala.apache.org/impala-docs.html

cloud.google.com/bigquery/docs

www.ibm.com/docs/en/db2

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Google

IBM

Amazon

originally Sqrrl Data, Inc.

Initial release

2013

2010

1983

host version

2012

Current release

4.1.0, June 2022

12.1, October 2016

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

free version is available

commercial

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++

C and C++

Java

Server operating systems

Linux

hosted

AIX
HP-UX
Linux
Solaris
Windows
z/OS

Linux

Data scheme

yes

schema-free

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.

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

APIs and other access methods

JDBC
ODBC

RESTful HTTP/JSON API

ADO.NET
JDBC
JSON style queries

MongoDB compatible
ODBC
XQuery

Accumulo Shell
Java API
JDBC
ODBC
RESTful HTTP API
Thrift

Supported programming languages

All languages supporting JDBC/ODBC

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C
C#
C++
Cobol
Delphi
Fortran
Java
Perl
PHP
Python
Ruby
Visual Basic

Actionscript
C

using GLib
C#
C++
Cocoa
Delphi
Erlang
Go
Haskell
Java
JavaScript
OCaml
Perl
PHP
Python
Ruby
Smalltalk

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

user defined functions

in JavaScript

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

only with Windows/Unix/Linux Version

Sharding

making use of Hadoop

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes

with separate tools (MQ, InfoSphere)

selectable replication factor

making use of Hadoop

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

Immediate Consistency

Document store kept consistent with combination of global timestamping, row-level transactions, and server-side consistency resolution.

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

ACID

Atomic updates per row, document, or graph entity

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)

fine grained access rights according to SQL-standard

Cell-level Security, Data-Centric Security, Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC)

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