DBMS > Amazon Redshift vs. Google Cloud Bigtable vs. IBM Db2 Event Store vs. InfluxDB

System Properties Comparison Amazon Redshift vs. Google Cloud Bigtable vs. IBM Db2 Event Store vs. InfluxDB

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

Distributed Event Store optimized for Internet of Things use cases

DBMS for storing time series, events and metrics

Primary database model

Relational DBMS

Key-value store
Wide column store

Event Store
Time Series DBMS

Time Series DBMS

Secondary database models

Spatial DBMS

with GEO package

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	3.26
Rank	#92	Overall
	#13	Key-value stores
	#8	Wide column stores

Score	0.19
Rank	#323	Overall
	#2	Event Stores
	#28	Time Series DBMS

Score	25.83
Rank	#28	Overall
	#1	Time Series DBMS

Website

aws.amazon.com/redshift

cloud.google.com/bigtable

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

www.influxdata.com/products/influxdb-overview

Technical documentation

docs.aws.amazon.com/redshift

cloud.google.com/bigtable/docs

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

docs.influxdata.com/influxdb

Developer

Amazon (based on PostgreSQL)

Google

IBM

Initial release

2012

2015

2017

2013

Current release

2.0

2.7.6, April 2024

License

Commercial or Open Source

commercial

free developer edition available

Open Source

MIT-License; commercial enterprise version available

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 and C++

Server operating systems

hosted

Linux

Linux, macOS, Windows for the developer addition

Linux
OS X

through Homebrew

Data scheme

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

Numeric data and Strings

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

restricted

SQL

Support of SQL

yes

does not fully support an SQL-standard

yes

through the embedded Spark runtime

SQL-like query language

APIs and other access methods

JDBC
ODBC

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

HTTP API
JSON over UDP

Supported programming languages

All languages supporting JDBC/ODBC

C#
C++
Go
Java
JavaScript (Node.js)
Python

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

.Net
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Perl
PHP
Python
R
Ruby
Rust
Scala

Server-side scripts

Stored procedures

user defined functions

in Python

yes

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

in enterprise version only

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Internal replication in Colossus, and regional replication between two clusters in different zones

Active-active shard replication

selectable replication factor

in enterprise version only

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 (for a single cluster), Eventual consistency (for two or more replicated clusters)

Eventual Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

Transaction concepts

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

ACID

Atomic single-row operations

Concurrency

Support for concurrent manipulation of data

yes

No - written data is immutable

yes

Durability

Support for making data persistent

yes

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

yes

In-memory capabilities

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

yes

Depending on used storage engine

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

fine grained access rights according to SQL-standard

simple rights management via user accounts

More information provided by the system vendor

Specific characteristics

InfluxData is the creator of InfluxDB , the open source time series database. It...
» more

Competitive advantages

Time to Value InfluxDB is available in all the popular languages and frameworks,...
» more

Typical application scenarios

IoT & Sensor Monitoring Developers are witnessing the instrumentation of every available...
» more

Key customers

InfluxData has more than 1,900 paying customers, including customers include MuleSoft,...
» more

Market metrics

Fastest-growing database to drive 27,500 GitHub stars Over 750,000 daily active instances
» more

Licensing and pricing models

Open source core with closed source clustering available either on-premise or on...
» more

News

Using Parquet’s Bloom Filters
28 May 2024

Efficiency Unleashed: Streamlining Workflows with the InfluxDB Management API
23 May 2024

What is DevRel at InfluxData
21 May 2024

An Introductory Guide to Grafana Alerts
16 May 2024

What to Expect When You’re Expecting InfluxDB: A Guide
14 May 2024

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.