Old School Databases Still Work
The world of data has changed drastically in the last decade. Gone are the days when a company could store all their information on just one computer server or use excel to analyze everything. With so much information now stored digitally, it's become more critical than ever for companies to find ways to make sense of this deluge of data and turn it into something valuable to help them grow their business. In this context, old school databases still have value as they provide an easy way for companies to organize and access their data without relying on new technologies like Hadoop or NoSql.
This article explores the top five reasons why Relational Databases are not obsolete and why some companies may prefer to use an old-school database.
The top five reasons why Relational Databases are not obsolete
You can create relationships between tables.
You can query across multiple tables at the same time.
Relationships are dynamic and flexible - they change as you add or delete data.
They have a rich set of functions for querying, updating, and deleting data.
They're easier to use than other database types because they don't require complicated SQL queries to get information from them.
You can create relationships between tables.
Let’s get started.
1. One of the most significant benefits of Relational Databases is that you can create relationships between tables
Relationships allow users to query data more efficiently by using connections instead of repeatedly repeating queries.
For example, suppose we wanted information on how many people are attending a particular event coming up in three weeks. In that case, we could use one table for all attendees (e.g., name, age), another with events (name), and then an intersection table where the two intersect: events_attending, which would include only rows from both tables related to this upcoming event. Then when querying, we don't have to ask about individual attendance but instead pull out everything relevant for this specific event making it much faster than querying each individual separately.
This example makes more sense when we look at the following reason,
2. You can query across multiple tables at the same time
A Relational Database is excellent when it comes to querying across multiple tables at the same time. You can also use SQL scripts to join data from two or more databases and create a table that is not already available in Relational Database. This is a common scenario for data scientists and analysts who might be interested in combining databases to create something new that provides more information than what they have access to on their own.
Unlike relational databases, other database types like NoSQL don't offer the same querying capabilities or as many functions, making it difficult to merge different datasets.
For example, if I query for all rows from my attendance table where column "item_ID" equals a particular ID number, that will return only those rows matching these specific criteria. However, if I then want to know who purchased that item - which would require me pulling out another row per transaction - I can do so by adding additional filtering parameters
3. Relationships are dynamic and flexible - they change as you add or delete data
One of the key benefits of Relational Databases is that relationships are dynamic and flexible - they change as you add or delete data. Adding new tables to a Relational Database means adding more columns, automatically creating more rows in your other tables if there's a relationship between them.
In contrast, with non-relational databases like Hadoop, this doesn't happen because it lacks an actual relational model (i.e., all information stored on different nodes). This makes it difficult when working with any data integration challenge.
4. Relationships have a rich set of functions for querying, updating, and deleting data.
Another key benefit of Relational Databases is that they have a rich set of functions for querying, updating, and deleting data. This can't be done with Hadoop databases as there's no underlying relational model (i.e., all data stored on different nodes).
Relational Database Management systems (RDBMS) are much easier to use when reading the data in subsets or specific intervals - say last year vs. this year, for example. There are also many more programming languages available which allow you to access these database tables.
5. The cost of using relational databases has dropped considerably
Over the last few years, the cost of using RDBMS has sharply declined, making them much cheaper than they used to beAs a result, relational Databases are an excellent option for companies that need to have access to data at all times - like tech companies, retailers, and banks.
Large corporations such as Google, Walmart, and Amazon use Relational Databases for data warehousing.
Banks prefer Relational Databases because they're more efficient at storing transaction types of data than NoSQL or Hadoop databases.
The point here is Old School Databases Still Work
Relational Database Management Systems (RDBMS) are a ubiquitous tool in the world of data manipulation. These systems have been around for decades, and while they may not be as popular as they once were, there will always be new companies that need to maintain and query their legacy databases.
That's where we come in!
We offer an online course on RDBMSs so you can learn more about these powerful tools. So if you want to stay ahead of the curve when it comes to managing your company's data - or if you wish to know more about how relational database management works- our online course is perfect for you! It covers everything from basic concepts like normalization and joining tables up through advanced topics.