If you happen to read a lot of data-related material, you might occasionally find the word “data” being treated in different ways. In some cases you’ll see “this data is…” and in other cases “these data are…”. You might even think “they obviously made a mistake with their grammar”.
Not so fast!
OLAP (Online Analytical Processing) is a category of database processing that facilitates business intelligence.
OLAP provides analysts, managers, and executives with the information they need to make effective decisions about an organization’s strategic directions. OLAP can provide valuable insights into how their business is performing, as well as how they can make improvements.
OLTP (Online Transactional Processing) is a category of data processing that is focused on transaction-oriented tasks. OLTP typically involves inserting, updating, and/or deleting small amounts of data in a database.
OLTP mainly deals with large numbers of transactions by a large number of users.
Normalization is the process of organizing a database to reduce redundancy and improve data integrity.
Normalization also simplifies the database design so that it achieves the optimal structure composed of atomic elements (i.e. elements that cannot be broken down into smaller parts).
Also referred to as database normalization or data normalization, normalization is an important part of relational database design, as it helps with the speed, accuracy, and efficiency of the database.
There’s a lot of confusion regarding the difference between an RDBMSs and a DBMS. I’ve even seen “RDBMS vs DBMS” forum posts where the accepted answer outlines the differences between RDBMSs and DBMSs, as though they were two distinct and different things.
However, this can be misleading.
The fact is, an RDBMS is a DBMS. But a DBMS is not always an RDBMS (but it often is).
So, is there a difference between an RDBMS and a DBMS or not? Is “RDBMS vs DBMS” the right way of looking at it, or is there more to it?
A column store database is a type of database that stores data using a column oriented model.
A column store database can also be referred to as a:
A document store database (also known as a document-oriented database, aggregate database, or simply document store or document database) is a database that uses a document-oriented model to store data.
Document store databases store each record and its associated data within a single document. Each document contains semi-structured data that can be queried against using various query and analytics tools of the DBMS.
A key-value database (also known as a key-value store and key-value store database) is a type of NoSQL database that uses a simple key/value method to store data.
The key-value part refers to the fact that the database stores data as a collection of key/value pairs. This is a simple method of storing data, and it is known to scale well.
NoSQL is a term that refers loosely to a particular type of database model, or database management system (DBMS).
NoSQL is a very broad term that doesn’t refer to one particular database model. Rather, it refers to a whole variety of different models that don’t fit into the relational model.
Although NoSQL databases have been around since the 1960s, it wasn’t until the early 2000s that the NoSQL approach started to pick up steam, and a whole new generation of NoSQL systems began to hit the market.
In database systems, ACID (Atomicity, Consistency, Isolation, Durability) refers to a standard set of properties that guarantee database transactions are processed reliably.
ACID is especially concerned with how a database recovers from any failure that might occur while processing a transaction.
An ACID-compliant DBMS ensures that the data in the database remains accurate and consistent despite any such failures.