NoSQL Databases have four distinct types. Key-value stores, document-stores, graph databases, and column-oriented databases. In this article, we’ll explore column-oriented databases, also known simply as “NoSQL columns”. Column-store remains one of the most used data models out there.
At a very surface level, column-store databases do exactly what is advertised on the tin: namely, that instead of organizing information into rows, it does so in columns. This essentially makes them function the same way that tables work in relational databases. Of course, since this is a NoSQL database, this data model makes them much more flexible.
More specifically, column databases use the concept of keyspace , which is sort of like a schema in relational models. This keyspace contains all the column families, which then contain rows, which then contain columns. It’s a bit tricky to wrap your head around at first but it’s relatively straightforward.
By taking a quick look, we can see that a column family has several rows. Within each row, there can be several different columns, with different names, links, and even sizes (meaning they don’t need to adhere to a standard). Furthermore, these columns only exist within their own row and can contain a value pair, name, and a timestamp.
In SSMS, we many of may noticed System Databases under the Database Folder. But how many of us knows its purpose?. In this article lets discuss about the System Databases in SQL Server.
NoSQL Databases have four distinct types. Key-value stores, document-stores, graph databases, and column-oriented databases. In this article, we’ll explore column-oriented databases, also known simply as “NoSQL columns”. If you are still wondering about it then this article is for you.
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While talking about NoSQL databases, a term we would often use is Column oriented data formats or data stores. While converting a tabular data into a sequence of bytes, we can go column wise or row wise.
NoSQL databases use a variety of data models for accessing and managing data. These types of databases are optimized specifically for applications that require large data volume, low latency, and flexible data models, which are achieved by relaxing some of the data consistency restrictions of other databases.