Greenplum Database vs OrientDB

Overview

OrientDB

Stacks77

Followers107

Votes14

Greenplum Database

Stacks45

Followers111

Votes0

GitHub Stars6.2K

Forks1.7K

Greenplum Database vs OrientDB: What are the differences?

Introduction:

Greenplum Database and OrientDB are two popular databases with distinct features and use cases. Understanding the key differences between these databases can help in making an informed decision for various data management needs.

1. Architecture and Design: Greenplum Database is a massively parallel processing (MPP) data warehouse designed for analytical workloads, while OrientDB is a NoSQL multi-model database that supports graph, document, key/value, and object models within a single, unified engine.

2. Data Model: Greenplum Database utilizes a relational data model with tables, rows, and columns, making it suitable for structured data analysis. In contrast, OrientDB supports multiple data models, offering flexibility to store and retrieve data in various formats such as graphs, documents, or key-values.

3. Scalability and Performance: Greenplum Database excels in handling large volumes of structured data and complex analytical queries efficiently, making it ideal for data warehousing applications. On the other hand, OrientDB provides horizontal scalability and real-time performance for graph-based operations, making it a suitable choice for applications requiring high-speed graph traversal.

4. Query Language: Greenplum Database primarily uses SQL for querying and manipulation of data, which is widely adopted and familiar to users with a background in relational databases. OrientDB provides both SQL-like and native graph traversal query languages, allowing users to perform complex graph operations efficiently.

5. Consistency vs. Flexibility: Greenplum Database ensures strong consistency and ACID compliance, making it suitable for applications requiring transactional integrity and data correctness. In contrast, OrientDB prioritizes flexibility and eventual consistency, allowing for more agile development and scalability at the expense of strict consistency guarantees.

In Summary, Greenplum Database is suited for structured data analytics with a focus on performance and scalability, while OrientDB offers a versatile multi-model approach with a strong emphasis on graph database capabilities and flexibility in data modeling.

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Detailed Comparison

OrientDB	Greenplum Database
It is an open source NoSQL database management system written in Java. It is a Multi-model database, supporting graph, document, key/value, and object models, but the relationships are managed as in graph databases with direct connections between records.	It is a massively parallel processing (MPP) database server with an architecture specially designed to manage large-scale analytic data warehouses and business intelligence workloads. It is based on PostgreSQL open-source technology.
-	Core SQL Conformance; MPP Architecture; Innovative Query Optimization; Polymorphic Data Storage; Integrated In-Database Analytics
Statistics
GitHub Stars -	GitHub Stars 6.2K
GitHub Forks -	GitHub Forks 1.7K
Stacks 77	Stacks 45
Followers 107	Followers 111
Votes 14	Votes 0
Pros & Cons
Pros 4 Great graphdb 2 Open source 2 Great support 1 Highly-available 1 Rest api Cons 4 Unstable	No community feedback yet
Integrations
No integrations available	PostgreSQL Kong Slick Heroku Apache Hive Clever Cloud Couchbase Sequelize Sails.js Metabase

What are some alternatives to OrientDB, Greenplum Database?

MongoDB

MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.

MySQL

The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.

PostgreSQL

PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.

Microsoft SQL Server

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.

SQLite

SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file.

Cassandra

Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.

Memcached

Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering.

MariaDB

Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. MariaDB is designed as a drop-in replacement of MySQL(R) with more features, new storage engines, fewer bugs, and better performance.

RethinkDB

RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn.

ArangoDB

A distributed free and open-source database with a flexible data model for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions.