DBMS > Amazon DynamoDB vs. Faircom DB vs. Netezza vs. PostgreSQL vs. SQream DB
System Properties Comparison Amazon DynamoDB vs. Faircom DB vs. Netezza vs. PostgreSQL vs. SQream DB
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Name | Amazon DynamoDB Xexclude from comparison | Faircom DB formerly c-treeACE Xexclude from comparison | Netezza Also called PureData System for Analytics by IBM Xexclude from comparison | PostgreSQL Xexclude from comparison | SQream DB Xexclude from comparison | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Description | Hosted, scalable database service by Amazon with the data stored in Amazons cloud | Native high-speed multi-model DBMS for relational and key-value store data simultaneously accessible through SQL and NoSQL APIs. | Data warehouse and analytics appliance part of IBM PureSystems | Widely used open source RDBMS Developed as objectoriented DBMS (Postgres), gradually enhanced with 'standards' like SQL | a GPU-based, columnar RDBMS for big data analytics workloads | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Primary database model | Document store Key-value store | Key-value store Relational DBMS | Relational DBMS | Relational DBMS with object oriented extensions, e.g.: user defined types/functions and inheritance. Handling of key/value pairs with hstore module. | Relational DBMS | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Secondary database models | Document store Graph DBMS with Apache Age Spatial DBMS Vector DBMS with pgvector extension | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Website | aws.amazon.com/dynamodb | www.faircom.com/products/faircom-db | www.ibm.com/products/netezza | www.postgresql.org | sqream.com | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Technical documentation | docs.aws.amazon.com/dynamodb | docs.faircom.com/docs/en/UUID-7446ae34-a1a7-c843-c894-d5322e395184.html | www.postgresql.org/docs | docs.sqream.com | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Developer | Amazon | FairCom Corporation | IBM | PostgreSQL Global Development Group www.postgresql.org/developer | SQream Technologies | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Initial release | 2012 | 1979 | 2000 | 1989 1989: Postgres, 1996: PostgreSQL | 2017 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Current release | V12, November 2020 | 16.3, May 2024 | 2022.1.6, December 2022 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
License Commercial or Open Source | commercial free tier for a limited amount of database operations | commercial Restricted, free version available | commercial | Open Source BSD | commercial | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Cloud-based only Only available as a cloud service | yes | no | no | no | no | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Implementation language | ANSI C, C++ | C | C++, CUDA, Haskell, Java, Scala | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Server operating systems | hosted | AIX FreeBSD HP-UX Linux NetBSD OS X QNX SCO Solaris VxWorks Windows easily portable to other OSs | Linux included in appliance | FreeBSD HP-UX Linux NetBSD OpenBSD OS X Solaris Unix Windows | Linux | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Data scheme | schema-free | schema free, schema optional, schema required, partial schema, | yes | yes | yes | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Typing predefined data types such as float or date | yes | yes, ANSI SQL Types, JSON, typed binary structures | yes | yes | yes, ANSI Standard SQL Types | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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. | no | yes specific XML-type available, but no XML query functionality. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Secondary indexes | yes | yes | yes | yes | no | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
SQL Support of SQL | no | yes, ANSI SQL with proprietary extensions | yes | yes standard with numerous extensions | yes | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
APIs and other access methods | RESTful HTTP API | ADO.NET Direct SQL JDBC JPA ODBC RESTful HTTP/JSON API RESTful MQTT/JSON API RPC | JDBC ODBC OLE DB | ADO.NET JDBC native C library ODBC streaming API for large objects | .Net JDBC ODBC | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Supported programming languages | .Net ColdFusion Erlang Groovy Java JavaScript Perl PHP Python Ruby | .Net C C# C++ Java JavaScript (Node.js and browser) PHP Python Visual Basic | C C++ Fortran Java Lua Perl Python R | .Net C C++ Delphi Java JDBC JavaScript (Node.js) Perl PHP Python Tcl | C++ Java JavaScript (Node.js) Python | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Server-side scripts Stored procedures | no | yes .Net, JavaScript, C/C++ | yes | user defined functions realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc. | user defined functions in Python | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Triggers | yes by integration with AWS Lambda | yes | no | yes | no | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Partitioning methods Methods for storing different data on different nodes | Sharding | File partitioning, horizontal partitioning, sharding Customizable business rules for table partitioning | Sharding | partitioning by range, list and (since PostgreSQL 11) by hash | horizontal and vertical partitioning | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Replication methods Methods for redundantly storing data on multiple nodes | yes | yes, configurable to be parallel or serial, synchronous or asynchronous, uni-directional or bi-directional, ACID-consistent or eventually consistent (with custom conflict resolution). | Source-replica replication | Source-replica replication other methods possible by using 3rd party extensions | none | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
MapReduce Offers an API for user-defined Map/Reduce methods | no may be implemented via Amazon Elastic MapReduce (Amazon EMR) | no | yes | no | no | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Consistency concepts Methods to ensure consistency in a distributed system | Eventual Consistency Immediate Consistency can be specified for read operations | Eventual Consistency Immediate Consistency Tunable consistency per server, database, table, and transaction | Immediate Consistency | Immediate Consistency | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Foreign keys Referential integrity | no | yes | no | yes | no | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Transaction concepts Support to ensure data integrity after non-atomic manipulations of data | ACID ACID across one or more tables within a single AWS account and region | tunable from ACID to Eventually Consistent | ACID | ACID | ACID | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Concurrency Support for concurrent manipulation of data | yes | yes | yes | yes | yes | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Durability Support for making data persistent | yes | Yes, tunable from durable to delayed durability to in-memory | yes | yes | yes | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
In-memory capabilities Is there an option to define some or all structures to be held in-memory only. | yes | no | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
User concepts Access control | Access rights for users and roles can be defined via the AWS Identity and Access Management (IAM) | Fine grained access rights according to SQL-standard with additional protections for files | Users with fine-grained authorization concept | fine grained access rights according to SQL-standard | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Amazon DynamoDB | Faircom DB formerly c-treeACE | Netezza Also called PureData System for Analytics by IBM | PostgreSQL | SQream DB | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Recent citations in the news | AWS announces Amazon DynamoDB zero-ETL integration with Amazon OpenSearch Service Simplify cross-account access control with Amazon DynamoDB using resource-based policies | Amazon Web Services Using the circuit-breaker pattern with AWS Lambda extensions and Amazon DynamoDB | Amazon Web Services Continuously replicate Amazon DynamoDB changes to Amazon Aurora PostgreSQL using AWS Lambda | Amazon ... Bulk update Amazon DynamoDB tables with AWS Step Functions | Amazon Web Services provided by Google News | FairCom Unveils New Look, FairCom DB v13: Introducing 'DB Made Simple' FairCom kicks off new era of database technology USA - English provided by Google News | Roundup: Telehouse, Cloudera, Netezza, EMC IBM announces availability of the high-performance, cloud-native Netezza Performance Server as a Service on AWS AWS and IBM Netezza come out in support of Iceberg in table format face-off How to migrate a large data warehouse from IBM Netezza to Amazon Redshift with no downtime | Amazon Web Services Netezza Performance Server provided by Google News | Raise the bar on AI-powered app development with Azure Database for PostgreSQL How to implement a better like, views, comment counters in PostgreSQL? Automatically Generate Types for Your PostgreSQL Database First Principles: Optimizing PostgreSQL for the cloud Continuously replicate Amazon DynamoDB changes to Amazon Aurora PostgreSQL using AWS Lambda | Amazon ... provided by Google News | I SQream, you SQream, we all SQream for … data analytics? SQream Announces Strategic Integration for Powerful Big Data Analytics with Dataiku GPU data warehouse startup SQream lands $39.4M funding round SQream Technologies raises $39.4 million for GPU-accelerated databases Chinese giant Alibaba leads investment round in Israel big-data startup provided by Google News |
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