DBMS > Apache Drill vs. Apache Impala vs. Google BigQuery vs. IBM Db2 Event Store

System Properties Comparison Apache Drill vs. Apache Impala vs. Google BigQuery vs. IBM Db2 Event Store

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Editorial information provided by DB-Engines

Name

Description

Schema-free SQL Query Engine for Hadoop, NoSQL and Cloud Storage

Analytic DBMS for Hadoop

Large scale data warehouse service with append-only tables

Distributed Event Store optimized for Internet of Things use cases

Primary database model

Document store
Relational DBMS

Relational DBMS

Event Store
Time Series DBMS

Secondary database models

Document store

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

Score	2.02
Rank	#124	Overall
	#22	Document stores
	#59	Relational DBMS

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	0.27
Rank	#309	Overall
	#2	Event Stores
	#28	Time Series DBMS

Website

drill.apache.org

impala.apache.org

cloud.google.com/bigquery

www.ibm.com/products/db2-event-store

Technical documentation

drill.apache.org/docs

impala.apache.org/impala-docs.html

cloud.google.com/bigquery/docs

www.ibm.com/docs/en/db2-event-store

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Google

IBM

Initial release

2012

2013

2010

2017

Current release

1.20.3, January 2023

4.1.0, June 2022

2.0

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache Version 2

commercial

free developer edition available

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++

Server operating systems

Linux
OS X
Windows

Linux

hosted

Linux

Linux, macOS, Windows for the developer addition

Data scheme

schema-free

yes

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 SELECT statement is SQL:2003 compliant

SQL-like DML and DDL statements

yes

through the embedded Spark runtime

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

JDBC
ODBC

RESTful HTTP/JSON API

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

Supported programming languages

C++

All languages supporting JDBC/ODBC

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C
C#
C++
Cobol
Delphi
Fortran
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Scala
Visual Basic

Server-side scripts

Stored procedures

user defined functions

yes

user defined functions and integration of map-reduce

user defined functions

in JavaScript

yes

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Active-active shard replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

none

Eventual Consistency

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

Transaction concepts

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

Since BigQuery is designed for querying data

Concurrency

Support for concurrent manipulation of data

yes

No - written data is immutable

Durability

Support for making data persistent

Depending on the underlying data source

yes

Yes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storage

In-memory capabilities

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

Depending on the underlying data source

yes

User concepts

Access control

Depending on the underlying data source

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

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