DBMS > Apache Impala vs. Databricks vs. GBase vs. TempoIQ

System Properties Comparison Apache Impala vs. Databricks vs. GBase vs. TempoIQ

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Editorial information provided by DB-Engines

Name

TempoIQ seems to be decommissioned. It will be removed from the DB-Engines ranking.

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.

Widely used RDBMS in China, including analytical, transactional, distributed transactional, and cloud-native data warehousing.

Scalable analytics DBMS for sensor data, provided as a service (SaaS)

Primary database model

Relational DBMS

Document store
Relational DBMS

Relational DBMS

Time Series DBMS

Secondary database models

Document store

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

Score	10.63
Rank	#40	Overall
	#24	Relational DBMS

Score	84.24
Rank	#14	Overall
	#2	Document stores
	#9	Relational DBMS

Score	1.11
Rank	#173	Overall
	#78	Relational DBMS

Website

impala.apache.org

www.databricks.com

www.gbase.cn

tempoiq.com (offline)

Technical documentation

impala.apache.org/impala-docs.html

docs.databricks.com

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Databricks

General Data Technology Co., Ltd.

TempoIQ

Initial release

2013

2004

2012

Current release

4.1.0, June 2022

GBase 8a, GBase 8s, GBase 8c

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

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++

C, Java, Python

Server operating systems

Linux

hosted

Linux

Data scheme

yes

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

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.

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

with Databricks SQL

Standard with numerous extensions

APIs and other access methods

JDBC
ODBC

JDBC
ODBC
RESTful HTTP API

ADO.NET
C API
JDBC
ODBC

HTTP API

Supported programming languages

All languages supporting JDBC/ODBC

Python
R
Scala

C#
Java
JavaScript

Node.js
Python
Ruby

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

user defined functions and aggregates

user defined functions

Triggers

yes

Realtime Alerts

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning (by range, list and hash) and vertical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes

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

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

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

yes

simple authentication-based access control

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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More resources

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Recent citations in the news

Apache Impala 4 Supports Operator Multi-Threading
29 July 2021, iProgrammer

Apache Impala becomes Top-Level Project
28 November 2017, SDTimes.com

Cloudera brings Apache Iceberg data lake format to its Data Platform
30 June 2022, SiliconANGLE News

Hudi: Uber Engineering’s Incremental Processing Framework on Apache Hadoop
12 March 2017, Uber

Updates & Upserts in Hadoop Ecosystem with Apache Kudu
27 October 2017, KDnuggets

provided by Google News

Databricks could launch IPO in two months but biding time despite investor pressure, CEO says
13 September 2024, ION Analytics

Leading Tech Talent Creation Expert Launches Databricks Data Engineer Program
19 September 2024, Business Wire

Databricks reportedly paid $2 billion in Tabular acquisition
14 August 2024, TechCrunch

Building a RAG System on Databricks With Your Unstructured Data Using Tonic Textual
21 September 2024, Security Boulevard

The People in Charge at Databricks as It Moves Toward a Potential IPO
24 July 2024, The Information

provided by Google News

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