TeraData vs Oracle

Oracle-vs-Teradata

Difference Between Teradata vs Oracle

Teradata vs Oracle are two of the very popular RDBMS systems. For Oracle, RDBMS variation is an Object-Relational Database Management System (ORDBMS). The RDBMS is like a Relational Model which maintains relationships between tables using what we call 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 make use of objects and classes which are 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 also the Internet of Things.

Head To Head Comparison Between Teradata vs Oracle (Infographics)

Below is the top 29 difference between Teradata vs Oracle Oracle-vs-Teradata infographics

Key Differences Between Teradata vs Oracle

Both Teradata vs Oracle are popular choices in the market; let us discuss some of the major Difference Between Teradata vs Oracle

Oracle is the tailor-made system for transaction processing because of its great architectural flexibility whereas Teradata’s OLAP is very powerful 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 can be taken as a good combination between hardware and software resulting in a production of a high-end enterprise database appliance. But Oracle launched its OLAP Exadata system Server in 2008 which was quite late if compared.
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 and across a set of servers in which every server is responsible for its own 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 good for data aggregation. And because it is an appliance the only way to get more storage or more power is to buy more appliances. Teradata implementation requires that return on investment cannot be forced.
Teradata lacks a nice and sophisticated data abstraction layer. This makes people treat it as a read-only data source. This is still fine if one has a simple data warehouse application which only requires report generation and stuff like that. Also, Teradata does not have the smartest optimizer for 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 then can do so. It is a very inexpensive choice for an app server that does data foundation of a multi-gigabytes of corporate information factory setup. It is available to be customized at every level from the SAN to the OS or DB to even 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 difficult 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. If things are not done Oracle way, it will not go well. Scalability is an issue with Oracle as well, where more hardware needs to be purchased if the data volume is running out of available storage space which results in a huge cost.