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DBMS > Cloudflare Workers KV vs. CrateDB vs. GridGain vs. Netezza

System Properties Comparison Cloudflare Workers KV vs. CrateDB vs. GridGain vs. Netezza

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Editorial information provided by DB-Engines
NameCloudflare Workers KV  Xexclude from comparisonCrateDB  Xexclude from comparisonGridGain  Xexclude from comparisonNetezza infoAlso called PureData System for Analytics by IBM  Xexclude from comparison
DescriptionA global, low-latency, key-value store for applications on Cloudflare with exceptionally high read volumes and low-latency.Distributed Database based on LuceneGridGain is an in-memory computing platform, built on Apache IgniteData warehouse and analytics appliance part of IBM PureSystems
Primary database modelKey-value storeDocument store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS
Key-value store
Relational DBMS
Relational DBMS
Secondary database modelsRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.32
Rank#283  Overall
#40  Key-value stores
Score0.73
Rank#224  Overall
#37  Document stores
#5  Spatial DBMS
#16  Search engines
#19  Time Series DBMS
#8  Vector DBMS
Score1.47
Rank#154  Overall
#26  Key-value stores
#72  Relational DBMS
Score9.06
Rank#46  Overall
#29  Relational DBMS
Websitewww.cloudflare.com/­developer-platform/­workers-kvcratedb.comwww.gridgain.comwww.ibm.com/­products/­netezza
Technical documentationdevelopers.cloudflare.com/­kv/­apicratedb.com/­docswww.gridgain.com/­docs/­index.html
DeveloperCloudflareCrateGridGain Systems, Inc.IBM
Initial release2018201320072000
Current releaseGridGain 8.5.1
License infoCommercial or Open SourcecommercialOpen Sourcecommercialcommercial
Cloud-based only infoOnly available as a cloud serviceyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
CrateDB Cloud: a distributed SQL database that spreads data and processing across an elastic cluster of shared nothing nodes. CrateDB Cloud enables data insights at scale on Microsoft Azure, AWS and Google Cloud Platform.
Implementation languageJavaJava, C++, .Net
Server operating systemshostedAll Operating Systems, including Kubernetes with CrateDB Kubernetes Operator supportLinux
OS X
Solaris
Windows
Linux infoincluded in appliance
Data schemeschema-freeFlexible Schema (defined schema, partial schema, schema free)yesyes
Typing infopredefined data types such as float or datenoyesyesyes
XML support infoSome form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.nonoyes
Secondary indexesnoyesyesyes
SQL infoSupport of SQLnoyes, but no triggers and constraints, and PostgreSQL compatibilityANSI-99 for query and DML statements, subset of DDLyes
APIs and other access methodsHTTP REST
Proprietary protocol
ADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API
HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data
JDBC
ODBC
OLE DB
Supported programming languagesC
C++
Dart
JavaScript
Kotlin
Python
Rust
Scala
.NET
Erlang
Go infocommunity maintained client
Java
JavaScript (Node.js) infocommunity maintained client
Perl infocommunity maintained client
PHP
Python
R
Ruby infocommunity maintained client
Scala infocommunity maintained client
C#
C++
Java
PHP
Python
Ruby
Scala
C
C++
Fortran
Java
Lua
Perl
Python
R
Server-side scripts infoStored proceduresnouser defined functions (Javascript)yes (compute grid and cache interceptors can be used instead)yes
Triggersnonoyes (cache interceptors and events)no
Partitioning methods infoMethods for storing different data on different nodesShardingShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesyesConfigurable replication on table/partition-levelyes (replicated cache)Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonoyes (compute grid and hadoop accelerator)yes
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyEventual Consistency
Read-after-write consistency on record level
Immediate Consistency
Foreign keys infoReferential integritynononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanono infounique row identifiers can be used for implementing an optimistic concurrency control strategyACIDACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonoyes
User concepts infoAccess controlrights management via user accountsSecurity Hooks for custom implementationsUsers with fine-grained authorization concept
More information provided by the system vendor
Cloudflare Workers KVCrateDBGridGainNetezza infoAlso called PureData System for Analytics by IBM
Specific characteristicsThe enterprise database for time series, documents, and vectors. Distributed - Native...
» more
Competitive advantagesResponse time in milliseconds: e ven for complex ad-hoc queries. Massive scaling...
» more
Typical application scenarios​ IoT: accelerate your IIoT projects with CrateDB, delivering real-time analytics...
» more
Key customersAcross all continents, CrateDB is used by companies of all sizes to meet the most...
» more
Market metricsThe CrateDB open source project was started in 2013 Honorable Mention in 2021 Gartner®...
» more
Licensing and pricing modelsSee CrateDB pricing >
» more

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Cloudflare Workers KVCrateDBGridGainNetezza infoAlso called PureData System for Analytics by IBM
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