DBMS > QuestDB vs. SAP HANA vs. Spark SQL vs. Teradata

System Properties Comparison QuestDB vs. SAP HANA vs. Spark SQL vs. Teradata

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Editorial information provided by DB-Engines

Name

Description

A high performance open source SQL database for time series data

In-memory, column based data store. Available as appliance or cloud service

Spark SQL is a component on top of 'Spark Core' for structured data processing

A hybrid cloud data analytics software platform (Teradata Vantage)

Primary database model

Time Series DBMS

Relational DBMS

Secondary database models

Relational DBMS

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

Document store
Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	2.52
Rank	#109	Overall
	#9	Time Series DBMS

Score	44.69
Rank	#22	Overall
	#16	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	45.33
Rank	#21	Overall
	#15	Relational DBMS

Website

questdb.io

www.sap.com/products/hana.html

spark.apache.org/sql

www.teradata.com

Technical documentation

questdb.io/docs

help.sap.com/hana

spark.apache.org/docs/latest/sql-programming-guide.html

docs.teradata.com

Developer

QuestDB Technology Inc

SAP

Apache Software Foundation

Teradata

Initial release

2014

2010

2014

1984

Current release

2.0 SPS07 (April 4, 2023), April 2023

3.5.0 ( 2.13), September 2023

Teradata Vantage 1.0 MU2, January 2019

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

Apache 2.0

commercial

Cloud-based only

Only available as a cloud service

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Java (Zero-GC), C++, Rust

Scala

Server operating systems

Linux
macOS
Windows

Appliance or cloud-service

Linux
OS X
Windows

hosted
Linux

Data scheme

yes

schema-free via InfluxDB Line Protocol

yes

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

Join-index to prejoin tables, aggregate index, sparse index, hash index

SQL

Support of SQL

SQL with time-series extensions

yes

SQL-like DML and DDL statements

yes

SQL 2016 + extensions

APIs and other access methods

HTTP REST
InfluxDB Line Protocol (TCP/UDP)
JDBC
PostgreSQL wire protocol

JDBC
ODBC

.NET Client API
HTTP REST
JDBC
JMS Adapter
ODBC
OLE DB

Supported programming languages

PostgreSQL driver
C++
Go
Java
JavaScript (Node.js)
Python
Rust

over HTTP

Java
Python
R
Scala

C
C++
Cobol
Java (JDBC-ODBC)
Perl
PL/1
Python
R
Ruby

Server-side scripts

Stored procedures

SQLScript, R

yes

UDFs, stored procedures, table functions in parallel

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning (by timestamps)

yes

yes, utilizing Spark Core

Sharding

Hashing

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication with eventual consistency

yes

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID for single-table writes

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.

yes

through memory mapped files

yes

User concepts

Access control

yes

fine grained access rights according to SQL-standard

More information provided by the system vendor

Specific characteristics

Relational model with native time series support Column-based storage and time partitioned...
» more

Competitive advantages

High ingestion throughput: peak of 4M rows/sec (TSBS Benchmark) Code optimizations...
» more

Typical application scenarios

Financial tick data Industrial IoT Application Metrics Monitoring
» more

Key customers

Banks & Hedge funds, Yahoo, OKX, Airbus, Aquis Exchange, Net App, Cloudera, Airtel,...
» more

Licensing and pricing models

Open source Apache 2.0 QuestDB Enterprise QuestDB Cloud
» more

News

QuestDB and Raspberry Pi 5 benchmark, a pocket-sized powerhouse
8 May 2024

Build your own resource monitor with QuestDB and Grafana
6 May 2024

Does "vpmovzxbd" Scare You? Here's Why it Doesn't Have To
12 April 2024

Create an ADS-B flight radar with QuestDB and a Raspberry Pi
8 April 2024

Build a temperature IoT sensor with Raspberry Pi Pico & QuestDB
5 April 2024

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