DBMS > chDB vs. IBM Db2 Event Store vs. Pinecone vs. Teradata

System Properties Comparison chDB vs. IBM Db2 Event Store vs. Pinecone vs. Teradata

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

An embedded SQL OLAP Engine powered by ClickHouse

Distributed Event Store optimized for Internet of Things use cases

A managed, cloud-native vector database

A hybrid cloud data analytics software platform (Teradata Vantage)

Primary database model

Relational DBMS

Event Store
Time Series DBMS

Vector DBMS

Relational DBMS

Secondary database models

Time Series DBMS

Document store
Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	0.05
Rank	#358	Overall
	#152	Relational DBMS

Score	0.18
Rank	#315	Overall
	#2	Event Stores
	#26	Time Series DBMS

Score	3.02
Rank	#87	Overall
	#3	Vector DBMS

Score	41.47
Rank	#22	Overall
	#15	Relational DBMS

Website

github.com/chdb-io/chdb

www.ibm.com/products/db2-event-store

www.pinecone.io

www.teradata.com

Technical documentation

doc.chdb.io

www.ibm.com/docs/en/db2-event-store

docs.pinecone.io/docs/overview

docs.teradata.com

Developer

IBM

Pinecone Systems, Inc

Teradata

Initial release

2023

2017

2019

1984

Current release

2.0

Teradata Vantage 1.0 MU2, January 2019

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

free developer edition available

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 and C++

Server operating systems

server-less

Linux

Linux, macOS, Windows for the developer addition

hosted

hosted
Linux

Data scheme

yes

Typing

predefined data types such as float or date

yes

String, Number, Boolean

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

Close to ANSI SQL (SQL/JSON + extensions)

yes

through the embedded Spark runtime

yes

SQL 2016 + extensions

APIs and other access methods

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

RESTful HTTP API

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

Supported programming languages

Bun
C
C++
Go
JavaScript (Node.js)
Python
Rust

C
C#
C++
Cobol
Delphi
Fortran
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Scala
Visual Basic

Python

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

Server-side scripts

Stored procedures

yes

UDFs, stored procedures, table functions in parallel

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Hashing

Replication methods

Methods for redundantly storing data on multiple nodes

Active-active shard replication

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

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

No - written data is immutable

yes

Durability

Support for making data persistent

Yes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storage

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

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

DB-Engines blog posts

Vector databases
2 June 2023, Matthias Gelbmann

show all

Teradata is the most popular data warehouse DBMS
2 April 2013, Paul Andlinger

show all

Recent citations in the news

Mudhakar Srivatsa
11 December 2023, IBM Research

Best cloud databases of 2022
4 October 2022, ITPro

IBM Confronts AI Resistance
23 October 2019, RTInsights

Why a robust data management strategy is essential today | IBM HDM
19 September 2019, Express Computer

How IBM Is Turning Db2 into an ‘AI Database’
3 June 2019, Datanami

provided by Google News

Using the Pinecone vector database in .NET
12 September 2024, InfoWorld

Pinecone serverless goes multicloud as vector database market heats up
27 August 2024, VentureBeat

Pinecone launches its serverless vector database out of preview
21 May 2024, TechCrunch

Pinecone vector database available on AWS, Azure, and Google clouds
27 August 2024, Blocks & Files

Pinecone Makes Accurate, Fast, Scalable Generative AI Accessible to Organizations Large and Small with Launch of its Serverless Vector Database
21 May 2024, PR Newswire

provided by Google News

Teradata’s Trusted AI powers innovation at Teradata Possible 2024
24 September 2024, SiliconANGLE News

Sei Investments Co. Sells 17,525 Shares of Teradata Co. (NYSE:TDC)
26 September 2024, MarketBeat

Why Teradata (TDC) is a Top Value Stock for the Long-Term
25 September 2024, Yahoo Finance

Why Teradata (TDC) is a Top Value Stock for the Long-Term
25 September 2024, Nasdaq

Teradata wants you to think differently about data and innovation – and take inspiration from the likes of Shazam
17 September 2024, ITPro

provided by Google News

Share this page