As of late, NoSQL databases gain largely in popularity over the last several years; however, the idea behind it is not fully new. What has changed lately with the advancement of NoSQL DBs is the availability of newer solutions and their enhanced performance and reliability, which leads to increased use of it from a niche audience. We may not stick stubbornly to a solution simply for subjective reasons thanking the wider range of tools for developing a robust DBMS. Still, we may use the combination of the very best tools available for the unique requirements of a project. Sometimes, the best bet may be leveraging the combined benefits of two contradicting technologies, too as like relying on the hybrid database model with a combination of relational and NoSQL Dbs.
Differences between relational and NoSQL
The relational database requires you to structure the database into different tables, and then each table is further divided into various columns based on the data types. These databases’ relational aspect comes into the picture when we define certain columns in the table as foreign keys by linking these to another table. In this way, we make links between various entities. This is an ideal approach to follow, but it does not let you restructure the data on the go or remove some of the columns without losing the entire data. It will also not add more columns without migrating the existing entries into the new scheme.
In the case of NoSQL databases, you do not need to stick to any predefined structure of data. Enter the data in its raw form to be stored. This will ideally suit a lot of standard use cases but will create many potential problems too. The freedom NoSQL offers to add any type and structure of data to the database means that some developers do not custom design their database. Instead of designing the database, they add stuff, and they tend to worry about it later.
So, could you expect to be worried about it later? You may have to analyze and review the data at the first point itself, even when setting up a NoSQL DB. Another downside of the NoSQL DBs is that it may require more storage than relational databases where you need to take care of all given relations. You may avail of services of Remoted, which is offering best-in-class database services.
Modern database administration – remaining relevant in the DBaaS age
There are many standard DB scenarios for which it may be wise to add NoSQL DB to the existing relational database or wise versa. The reason for doing this may vary from case to case. It may just the need to cache the reads to enhance the queries to scale your data across servers or something else. Let us discuss a few typical use cases for hybrid database setup.
Use case #1: Document database
Enterprise resource planning applications are the stronghold of relational databases historically. However, they are now lacking in allowing flexibility to the same set of enterprise users in volume data handling. With relational DBs, the users have limitations in handling entry forms without updating database schema in full. With the addition of a NoSQL document-based database into the existing system; however, the users can easily create and edit such forms quickly. These data will get stored as a document that can sufficiently meet any parameter changes in the future.
Some of these database vendors have also recognized the need for such a combined solution and started offering something like a document DB instead of the pure relational databases. For example, Microsoft SQL Server 2016, a relational database, now offers dedicated support for JSON document storing inside the relational data cells, which will further ease up the workflow to a larger extent but may complicate updating data compared to updating data in normal database tables.
Document databases can now store everything in a document form, usually as a JSON object. Since this model doesn’t require any storage structure, you can add various types of fields as you need to each object while also because it is up to you to make complete sense out of data that you retrieve from it. Some of the top popular document-based databases are Couchbase and MongoDB etc.
Use case #2: In-memory DBs
The success of e-com websites largely relies on their ability to recommend identify the user interests and recommend something they may be interested in buying at the given point in time. We can see that many top-grossing e-com sites do this task well. How do they achieve the same? They tend to analyze the previous purchase data and browsing history of the users to identify what you have searched for and not purchased yet. They will do the same kind of analysis of our friends or other users related to you and correlate this data to show the trendiest options in front of you.
The challenge here for the database is that these types of analyses need to happen quickly in real-time for each page opening in front of each user. This is an impossible fleet for the relational databases, which have to join multiple tables together to get this done.
While thinking of a possible solution for this in terms of hybrid databases, an ideal choice is in-memory databases. The relational database that you already use cache the data needed to perform the desirable in-memory queries instead of running to the disk each time. Another better solution is a graph database for the same purpose, which keeps track of all linked relations to identify user-circle preferences.
In-memory DBs, in a hybrid database environment, mostly function as key-value stores that run on your RAM, but many of them do have the ability to persist the hard drive. They also offer replication support, transaction log, snapshots, etc. Radis and Me cached are two of the most popular in-memory databases in use. The graph databases tend to store data in graphic graphs, optimized for faster querying and real-time data lookups. This is ideally done by adding pointers on each entry to another connected entry. You may consider Infinite Graph and Neo4j, etc., which do the same.
Along with the above, you may consider the use case of fraud detection and scalability while considering hybrid database implementation for your enterprise database administration.