DBMS > Amazon Redshift vs. Apache Impala vs. Memgraph vs. Vertica

System Properties Comparison Amazon Redshift vs. Apache Impala vs. Memgraph vs. Vertica

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Analytic DBMS for Hadoop

An open source graph database built for real-time streaming and compatible with Neo4j

Cloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.

Primary database model

Relational DBMS

Graph DBMS

Relational DBMS

Column oriented

Secondary database models

Document store

Spatial DBMS
Time Series DBMS

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	3.02
Rank	#98	Overall
	#8	Graph DBMS

Score	10.68
Rank	#43	Overall
	#27	Relational DBMS

Website

aws.amazon.com/redshift

impala.apache.org

memgraph.com

www.vertica.com

Technical documentation

docs.aws.amazon.com/redshift

impala.apache.org/impala-docs.html

memgraph.com/docs

vertica.com/documentation

Social network pages

Developer

Amazon (based on PostgreSQL)

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Memgraph Ltd

OpenText

previously Micro Focus and Hewlett Packard

Initial release

2012

2013

2017

2005

Current release

4.1.0, June 2022

12.0.3, January 2023

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

Open Source

BSL 1.1; commercial license for enterprise edition available

commercial

Limited community edition free

Cloud-based only

Only available as a cloud service

yes

on-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C++

C and C++

C++

Server operating systems

hosted

Linux

Data scheme

yes

schema-free and schema-optional

Yes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.

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

restricted

yes

No Indexes Required. Different internal optimization strategy, but same functionality included.

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like DML and DDL statements

Full 1999 standard plus machine learning, time series and geospatial. Over 650 functions.

APIs and other access methods

JDBC
ODBC

Bolt protocol
Cypher query language

ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL

character-based, interactive, front-end utility

Supported programming languages

All languages supporting JDBC/ODBC

.Net
C
C++
Elixir
Go
Haskell
Java
JavaScript
PHP
Python
Ruby
Scala

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

Server-side scripts

Stored procedures

user defined functions

in Python

yes

user defined functions and integration of map-reduce

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

yes, called Custom Alerts

Partitioning methods

Methods for storing different data on different nodes

Sharding

dynamic graph partitioning

horizontal partitioning, hierarchical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

Multi-source replication using RAFT

Multi-source replication

One, or more copies of data replicated across nodes, or object-store used for repository.

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Bi-directional Spark integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

relationships in graphs

yes

Transaction concepts

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

ACID

with snapshot isolation

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

with periodic snapshot and write-ahead logging (WAL) of changes

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

fine grained access rights according to SQL-standard

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

Users, roles and permissions

fine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

Graph algorithms in bioinformatics Social network analysis Cryptocurrency network...
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Key customers

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Licensing and pricing models

You can check out our pricing model and licenses on the company website .
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Cost-based models and subscription-based models are both available. One license is...
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