Difference Between Teradata and Oracle

Teradata vs Oracle are two of the prevalent RDBMS systems. For Oracle, RDBMS variation is an Object-Relational Database Management System (ORDBMS). The RDBMS is like a Relational Model that maintains relationships between tables using indexes and primary and foreign keys. Because of this, fetching and storing data is quicker compared to the old DBMS systems. Oracle’s object-oriented database model uses objects and classes supported by database schema and query language. Oracle was the first to make available RDBMS commercially in the seventies when Teradata was only laying the foundation of the first data warehouse. Later, Teradata’s capabilities made it best suitable for big data, Business Intelligence tools, and the Internet of Things.

Head To Head Comparison Between Teradata and Oracle (Infographics)

Below is the top 29 difference between Teradata vs Oracle

Key Differences Between Teradata and Oracle

Both Teradata vs Oracle are popular choices in the market; let us discuss some of the significant Differences:

Oracle is the tailor-made system for transaction processing because of its outstanding architectural flexibility, whereas Teradata’s OLAP is mighty for slicing and dicing data for analysis.
Oracle is mainly used as an online back-end application. It manages inserts, updates, and deletes in a transaction, whereas Teradata is Data Warehousing application which maintains big data for analytics. There is no such thing as real-time transactions in Teradata.
Teradata is an excellent combination of hardware and software, producing a high-end enterprise database appliance. However, Oracle’s launch of its OLAP Exadata system server in 2008 was comparatively late.
Teradata is based on Shared Nothing Architecture; on the other hand, Oracle has Shared Everything Architecture. Shared nothing architecture means a multiprocessor database management system in which memory and hard disk storage are not shared among the processors; only Network bandwidth is shared for data transfer.
In the case of Teradata, the data is stored on servers and is partitioned across a set of servers in which every server is responsible for its data only. In the case of Oracle, the architecture means any machine can access any data. This fundamental difference makes Teradata ideally suited for data warehousing and Oracle suited for OTLP.
Teradata, as an appliance, is quite suitable for data aggregation. And because it is an appliance, buying more storage or power is the only way to buy more appliances. Implementing Teradata requires a return on investment that cannot be forced.
Teradata lacks a lovely and sophisticated data abstraction layer. This makes people treat it as a read-only data source. This is still fine if a simple data warehouse application only requires report generation. Also, Teradata does not have the most competent optimizer at its disposal. It can handle some of the models perfectly. Sometimes, based on the query nature, it can get confused and take a lot of time with complex star schemes. Recursive models of scale are beyond expectations.
On the contrary, Oracle is anything that, if someone has the skill to create, can do so. It is a very inexpensive choice for an app server that does the data foundation of a multi-gigabyte corporate information factory setup. It is customizable at every level, from the SAN to the OS or DB, down to the abstraction layer. Generally, any model which makes sense for an RDBMS will work with any sophisticated optimizer. The one disadvantage is that Oracle is complicated for somebody who does not know how to use it correctly, and this is so because there are so many customization options for so many different use cases that there is a steep learning curve involved. It will not go well if things are not done in Oracle’s way. Scalability is also a concern with Oracle, as running out of available storage space requires purchasing more hardware, resulting in significant costs.