DBMS > Apache Impala vs. Apache Pinot vs. Google Cloud Bigtable

System Properties Comparison Apache Impala vs. Apache Pinot vs. Google Cloud Bigtable

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

Realtime distributed OLAP datastore, designed to answer OLAP queries with low latency

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

Primary database model

Relational DBMS

Key-value store
Wide column store

Secondary database models

Document store

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

Score	11.22
Rank	#41	Overall
	#25	Relational DBMS

Score	0.46
Rank	#252	Overall
	#118	Relational DBMS

Score	2.67
Rank	#93	Overall
	#15	Key-value stores
	#6	Wide column stores

Website

impala.apache.org

pinot.apache.org

cloud.google.com/bigtable

Technical documentation

impala.apache.org/impala-docs.html

docs.pinot.apache.org

cloud.google.com/bigtable/docs

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Apache Software Foundation and contributors

Google

Initial release

2013

2015

Current release

4.1.0, June 2022

1.0.0, September 2023

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache Version 2.0

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++

Java

Server operating systems

Linux

All OS with a Java JDK11 or higher

hosted

Data scheme

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.

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

SQL-like query language

APIs and other access methods

JDBC
ODBC

JDBC

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

Supported programming languages

All languages supporting JDBC/ODBC

Go
Java
Python

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

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

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

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

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

Foreign keys

Referential integrity

Transaction concepts

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

Atomic single-row operations

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

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

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More resources

Recent citations in the news

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28 November 2017, SD Times

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13 December 2024, The New Stack

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11 March 2025, GBHackers News

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7 March 2025, CybersecurityNews

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1 August 2024, ZDNet

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1 August 2024, TechCrunch

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