DBMS > Alibaba Cloud MaxCompute vs. Amazon DynamoDB vs. Faircom DB vs. HEAVY.AI vs. PostgreSQL
System Properties Comparison Alibaba Cloud MaxCompute vs. Amazon DynamoDB vs. Faircom DB vs. HEAVY.AI vs. PostgreSQL
| Editorial information provided by DB-Engines | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Name | Alibaba Cloud MaxCompute Xexclude from comparison | Amazon DynamoDB Xexclude from comparison | Faircom DB  formerly c-treeACE Xexclude from comparison | HEAVY.AI Formerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022 Xexclude from comparison | PostgreSQL Xexclude from comparison | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Description | MaxCompute (previously known as ODPS) is a general purpose, fully managed, multi-tenancy data processing platform for large-scale data warehousing | 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. | A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware | Widely used open source RDBMS Developed as objectoriented DBMS (Postgres), gradually enhanced with 'standards' like SQL | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Primary database model | Relational DBMS a graph-processing framework is available with the graphs being stored in tables | 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Secondary database models | Spatial DBMS | Document store Graph DBMS with Apache AgeSpatial DBMS Vector DBMS with pgvector extension | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
|
|
|
|
| |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Website | www.alibabacloud.com/product/maxcompute | aws.amazon.com/dynamodb | www.faircom.com/products/faircom-db | github.com/heavyai/heavydb www.heavy.ai | www.postgresql.org | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Technical documentation | www.alibabacloud.com/help/en/maxcompute | docs.aws.amazon.com/dynamodb | docs.faircom.com/docs/en/UUID-7446ae34-a1a7-c843-c894-d5322e395184.html | docs.heavy.ai | www.postgresql.org/docs | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Developer | Alibaba | Amazon | FairCom Corporation | HEAVY.AI, Inc. | PostgreSQL Global Development Group www.postgresql.org/developer | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Initial release | 2016 | 2012 | 1979 | 2016 | 1989 1989: Postgres, 1996: PostgreSQL | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Current release | V12, November 2020 | 5.10, January 2022 | 16.3, May 2024 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| License Commercial or Open Source | commercial | commercial free tier for a limited amount of database operations | commercial Restricted, free version available | Open Source Apache Version 2; enterprise edition available | Open Source BSD | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Cloud-based only Only available as a cloud service | yes | yes | no | no | no | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| DBaaS offerings (sponsored links) Database as a Service Providers of DBaaS offerings, please contact us to be listed. |
| |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Implementation language | ANSI C, C++ | C++ and CUDA | C | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Server operating systems | hosted | hosted | AIX FreeBSD HP-UX Linux NetBSD OS X QNX SCO Solaris VxWorks Windows easily portable to other OSs | Linux | FreeBSD HP-UX Linux NetBSD OpenBSD OS X Solaris Unix Windows | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Data scheme | yes | schema-free | schema free, schema optional, schema required, partial schema, | yes | yes | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Typing predefined data types such as float or date | yes | yes | yes, ANSI SQL Types, JSON, typed binary structures | yes | 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. | no | no | no | yes specific XML-type available, but no XML query functionality. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Secondary indexes | no | yes | yes | no | yes | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| SQL Support of SQL | SQL-like query language | no | yes, ANSI SQL with proprietary extensions | yes | yes standard with numerous extensions | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| APIs and other access methods | Fluentd Flume MaxCompute Console | RESTful HTTP API | ADO.NET Direct SQL JDBC JPA ODBC RESTful HTTP/JSON API RESTful MQTT/JSON API RPC | JDBC ODBC Thrift Vega | ADO.NET JDBC native C library ODBC streaming API for large objects | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Supported programming languages | Java | .Net ColdFusion Erlang Groovy Java JavaScript Perl PHP Python Ruby | .Net C C# C++ Java JavaScript (Node.js and browser) PHP Python Visual Basic | All languages supporting JDBC/ODBC/Thrift Python | .Net C C++ Delphi Java JDBCJavaScript (Node.js) Perl PHP Python Tcl | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Server-side scripts Stored procedures | user defined functions in Java | no | yes .Net, JavaScript, C/C++ | no | user defined functions realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Triggers | no | yes by integration with AWS Lambda | yes | no | yes | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Partitioning methods Methods for storing different data on different nodes | Sharding Implicit feature of the cloud service | Sharding | File partitioning, horizontal partitioning, sharding Customizable business rules for table partitioning | Sharding Round robin | partitioning by range, list and (since PostgreSQL 11) by hash | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Replication methods Methods for redundantly storing data on multiple nodes | yes Implicit feature of the cloud service | 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). | Multi-source replication | Source-replica replication other methods possible by using 3rd party extensions | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| MapReduce Offers an API for user-defined Map/Reduce methods | yes | no may be implemented via Amazon Elastic MapReduce (Amazon EMR) | no | no | no | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Consistency concepts Methods to ensure consistency in a distributed system | Immediate Consistency | 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 | no | yes | no | yes | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Transaction concepts Support to ensure data integrity after non-atomic manipulations of data | no | ACID ACID across one or more tables within a single AWS account and region | tunable from ACID to Eventually Consistent | no | ACID | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Concurrency Support for concurrent manipulation of data | yes | yes | yes | yes | yes | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Durability Support for making data persistent | yes | yes | Yes, tunable from durable to delayed durability to in-memory | yes | yes | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| In-memory capabilities Is there an option to define some or all structures to be held in-memory only. | no | yes | yes | no | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| User concepts Access control | Access rights for users and roles | 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 | fine grained access rights according to SQL-standard | fine grained access rights according to SQL-standard | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
More information provided by the system vendorWe invite representatives of system vendors to contact us for updating and extending the system information, | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Related products and services | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 3rd parties | CData: Connect to Big Data & NoSQL through standard Drivers. » more | CYBERTEC is your professional partner in PostgreSQL topics for over 20 years. As our main aim is to be your single-source all-in-one IT service provider, we offer a wide range of products and services. Visit our website for more details. » more Aiven for PostgreSQL: Fully managed PostgreSQL for developers with 70+ extensions and flexible orchestration tools. » more Navicat for PostgreSQL is an easy-to-use graphical tool for PostgreSQL database development. » more pgDash: In-Depth PostgreSQL Monitoring. » more SharePlex is the reliable and affordable data replication solution for PostgreSQL migrations, high availability and more. » more Redgate webinars: A series of key topics for new PostgreSQL users. » more Instaclustr: Fully Hosted & Managed PostgreSQL » more Navicat Monitor is a safe, simple and agentless remote server monitoring tool for PostgreSQL and many other database management systems. » more | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
We invite representatives of vendors of related products to contact us for presenting information about their offerings here. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| More resources | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Alibaba Cloud MaxCompute | Amazon DynamoDB | Faircom DB formerly c-treeACE | HEAVY.AI Formerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022 | PostgreSQL | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| DB-Engines blog posts | Cloud-based DBMS's popularity grows at high rates The popularity of cloud-based DBMSs has increased tenfold in four years Increased popularity for consuming DBMS services out of the cloud | PostgreSQL is the DBMS of the Year 2023 Snowflake is the DBMS of the Year 2022, defending the title from last year Snowflake is the DBMS of the Year 2021 Alibaba Cloud To Launch Big Data Service in Europe Alibaba Cloud achieves 'record' in big data performance Denodo and Alibaba Cloud Partner to Bring Data Virtualization to the Global Market Streaming Data Services Comparison: Alibaba Cloud, AWS, Azure, Google Cloud, IBM Cloud How Big Data Platform Helped Power Last Year's Double 11 provided by Google News Use Amazon DynamoDB incremental exports to drive continuous data retention | Amazon Web Services 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 Simplify private connectivity to Amazon DynamoDB with AWS PrivateLink | Amazon Web Services 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 HEAVY.AI Launches HEAVY 7.0, Introducing Real-Time Machine Learning Capabilities Big Data Analytics: A Game Changer for Infrastructure HEAVY.AI Partners with Bain, Maxar, and Nvidia to Provide Digital Twins for Telecom Networks Making the most of geospatial intelligence The insideBIGDATA IMPACT 50 List for Q4 2023 provided by Google News PostgreSQL is Now Faster than Pinecone, 75% Cheaper, with New Open Source Extensions Timescale unveils high-performance AI vector database extensions for PostgreSQL PostgreSQL Tutorial: Definition, Commands, & Features Raise the bar on AI-powered app development with Azure Database for PostgreSQL How to implement a better like, views, comment counters in PostgreSQL? provided by Google News | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Share this page
