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Greenplum Database vs Oracle: What are the differences?
Comparison between Greenplum Database and Oracle
Greenplum Database and Oracle are two popular relational database management systems (RDBMS) widely used in the industry. While both databases offer similar functionalities, they also have significant differences that make them suitable for different use cases.
Scalability: One key difference between Greenplum Database and Oracle is their scalability. Greenplum Database is designed for high scalability and can handle large amounts of data efficiently. It uses a shared-nothing architecture, where each node in the system operates independently, allowing for parallel processing and improved performance. In contrast, Oracle uses a shared-disk architecture, where multiple nodes share a common storage system. While Oracle can also handle large amounts of data, its scalability is limited compared to Greenplum Database.
Data Partitioning: Greenplum Database excels in its ability to handle partitioned data. It can distribute large tables across multiple nodes based on user-defined partition strategies, such as range or list partitioning. This enhances query performance by minimizing the amount of data that needs to be processed. In contrast, while Oracle also supports data partitioning, it has fewer built-in partitioning strategies and requires more manual setup.
Parallel Processing: Greenplum Database is specifically designed for parallel processing. It leverages the power of multiple nodes to process queries simultaneously, enabling faster analysis of large datasets. It automatically parallelizes queries and distributes the workload across its nodes. On the other hand, while Oracle supports parallel processing, it requires manual configuration for parallelism and does not parallelize all queries by default.
Open Source vs. Proprietary: Greenplum Database is an open-source RDBMS, licensed under the Apache License 2.0. This means it can be freely downloaded, used, and modified, making it a cost-effective choice for organizations. Oracle, on the other hand, is a proprietary database management system and comes with commercial licensing fees.
Community Support: Greenplum Database benefits from a thriving open-source community that contributes to its development and provides support. This community-driven approach ensures regular updates, bug fixes, and feature enhancements. Oracle, being a proprietary database, has support directly from Oracle Corporation and offers enterprise-level support options. While Oracle's support is reliable, it may come at a higher cost compared to the community-driven support for Greenplum Database.
In summary, Greenplum Database offers superior scalability, efficient data partitioning, and optimized parallel processing compared to Oracle. It is an open-source solution with strong community support, making it an attractive choice for organizations looking to handle large datasets effectively. However, Oracle provides enterprise-level support and has broad market acceptance, making it suitable for businesses with specific requirements and budget for a proprietary database management system.
We have chosen Tibero over Oracle because we want to offer a PL/SQL-as-a-Service that the users can deploy in any Cloud without concerns from our website at some standard cost. With Oracle Database, developers would have to worry about what they implement and the related costs of each feature but the licensing model from Tibero is just 1 price and we have all features included, so we don't have to worry and developers using our SQLaaS neither. PostgreSQL would be open source. We have chosen Tibero over Oracle because we want to offer a PL/SQL that you can deploy in any Cloud without concerns. PostgreSQL would be the open source option but we need to offer an SQLaaS with encryption and more enterprise features in the background and best value option we have found, it was Tibero Database for PL/SQL-based applications.
We wanted a JSON datastore that could save the state of our bioinformatics visualizations without destructive normalization. As a leading NoSQL data storage technology, MongoDB has been a perfect fit for our needs. Plus it's open source, and has an enterprise SLA scale-out path, with support of hosted solutions like Atlas. Mongo has been an absolute champ. So much so that SQL and Oracle have begun shipping JSON column types as a new feature for their databases. And when Fast Healthcare Interoperability Resources (FHIR) announced support for JSON, we basically had our FHIR datalake technology.
In the field of bioinformatics, we regularly work with hierarchical and unstructured document data. Unstructured text data from PDFs, image data from radiographs, phylogenetic trees and cladograms, network graphs, streaming ECG data... none of it fits into a traditional SQL database particularly well. As such, we prefer to use document oriented databases.
MongoDB is probably the oldest component in our stack besides Javascript, having been in it for over 5 years. At the time, we were looking for a technology that could simply cache our data visualization state (stored in JSON) in a database as-is without any destructive normalization. MongoDB was the perfect tool; and has been exceeding expectations ever since.
Trivia fact: some of the earliest electronic medical records (EMRs) used a document oriented database called MUMPS as early as the 1960s, prior to the invention of SQL. MUMPS is still in use today in systems like Epic and VistA, and stores upwards of 40% of all medical records at hospitals. So, we saw MongoDB as something as a 21st century version of the MUMPS database.
Pros of Greenplum Database
Pros of Oracle
- Reliable44
- Enterprise33
- High Availability15
- Hard to maintain5
- Expensive5
- Maintainable4
- Hard to use4
- High complexity3
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Cons of Greenplum Database
Cons of Oracle
- Expensive14
