DBMS > Apache Impala vs. Databricks vs. Microsoft Azure Table Storage vs. TinkerGraph vs. XTDB

System Properties Comparison Apache Impala vs. Databricks vs. Microsoft Azure Table Storage vs. TinkerGraph vs. XTDB

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

The Databricks Lakehouse Platform combines elements of data lakes and data warehouses to provide a unified view onto structured and unstructured data. It is based on Apache Spark.

A Wide Column Store for rapid development using massive semi-structured datasets

A lightweight, in-memory graph engine that serves as a reference implementation of the TinkerPop3 API

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Relational DBMS

Document store
Relational DBMS

Wide column store

Graph DBMS

Document store

Secondary database models

Document store

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

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	78.61
Rank	#15	Overall
	#2	Document stores
	#10	Relational DBMS

Score	4.48
Rank	#75	Overall
	#6	Wide column stores

Score	0.08
Rank	#348	Overall
	#35	Graph DBMS

Score	0.11
Rank	#343	Overall
	#46	Document stores

Website

impala.apache.org

www.databricks.com

azure.microsoft.com/en-us/services/storage/tables

tinkerpop.apache.org/docs/current/reference/#tinkergraph-gremlin

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

impala.apache.org/impala-docs.html

docs.databricks.com

www.xtdb.com/docs

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Databricks

Microsoft

Juxt Ltd.

Initial release

2013

2012

2009

2019

Current release

4.1.0, June 2022

1.19, September 2021

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

Open Source

Apache 2.0

Open Source

MIT License

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++

Java

Clojure

Server operating systems

Linux

hosted

All OS with a Java 8 (and higher) VM
Linux

Data scheme

yes

Flexible Schema (defined schema, partial schema, schema free)

schema-free

Typing

predefined data types such as float or date

yes

yes, extensible-data-notation format

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.

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

with Databricks SQL

limited SQL, making use of Apache Calcite

APIs and other access methods

JDBC
ODBC

JDBC
ODBC
RESTful HTTP API

RESTful HTTP API

TinkerPop 3

HTTP REST
JDBC

Supported programming languages

All languages supporting JDBC/ODBC

Python
R
Scala

.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby

Groovy
Java

Clojure
Java

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

user defined functions and aggregates

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

none

yes, each node contains all data

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

Eventual Consistency

Immediate Consistency

none

Foreign keys

Referential integrity

yes

Relationships in graphs

Transaction concepts

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

ACID

optimistic locking

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

optional

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

In-memory capabilities

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

yes

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

Access rights based on private key authentication or shared access signatures

More information provided by the system vendor

Specific characteristics

Supported database models : In addition to the Document store and Relational DBMS...
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