The most fundamental decision in enterprise information technology administration is how to adopt the right method to store the data. Going wrong in this will put you handcuffed forever, and by getting it right, you will set open many new reassuring avenues in terms of growing your business. There are many database systems and plenty of tools available in enterprise database administration, but not all of them are the same.
So, how to begin at the most confusing and crowded space of database engines? What to choose among relational DBMS or NoSQL? Whether with schema or no schema? Which model to choose as a document, columnar, or unstructured data? What is the significance of graph databases, and whether it is worth to choose?
There are many questions to consider and the right answer to which will decide the scope of your database administration and its success. These answers will also lead you to the most reasonable solutions for ineffective enterprise data management. While doing this, the most important things to understand are key attributes of the data you handle to compare the same with the capabilities and shortfalls of various storage technologies to choose the right one.
The scope of data storage
Before discussing the factors to consider, we may notice the first thing while searching for answers to the above questions, and you are most likely to end up with a traditional SQL-based RDBMS. The good reason for the same is that SQL DBS are there for more than three decades now and still is the major avenue to store enterprise data.
If you think there is a lot of data to be handle, there are many SQL DBs that handle Terabytes of data and still doing the job well. In most cases, a relational database management system can be designed and implemented effectively to meet the application’s needs.
Choosing the right database engine – major factors to consider
Keeping all the disclaimers aside, we need to consider the major attributes of your database management system to be considered while choosing the right engine. Let us explore.
- You should first consider the default structure and nature of your data. Identify the structure of your data while you write it and how you have to read it from the DB. It would help if you got a clear insight into the data structure or an idea about it to decide. You may also consider how large the data set will be overtime in terms of the volume and the working sets you may need.
- While considering the size, you also need to think of the scalability considerations like the throughput, concurrent users, and latency. Most importantly, it would help if you also considered the computations to be performed on the given set of data and how to execute the same.
- While looking at the data structure and use cases, considering which of the storage models followed by various database engines match the most to your application needs and intentions, there are various storage models available like column store, row store, key-value pair, document, time series, graph, and unstructured, etc. Let us explore each of these, as explained by the RemoteDBA.com experts:
- Row store model can keep the transactional data very well. The traditional databases with row-based stores function well when you need to do the joins or to group various operations in different transactions. While an RDBMS can effectively support large data volumes, the row structure performs poorly with large working sets of data.
- Column data stores are largely used for analysis, which can be very much space-efficient and computational oriented. You can take advantage of repeating data using column stores. Columnar DBs are an ideal choice for handling unbounded data sets and also to manage analytical operations.
- Document stores are also known as key-value stores, which will work well when you can tie them up using a fixed schema or if you need to search across various entities. Like many of the NoSQL DBs out there, they work well with a larger set of data and can be more write efficiently.
- Graph databases are meant to meet the special need of understanding the relationships between various data types.
- Another type of database system is with no structure at all. If you tend to rely on fully heterogeneous databases, then you have to ask for ways to put the database into the DB in the same unstructured form and get it out when needed. With such a database system in place, you can bring more computing power to data than working the other way around. Unstructured data storage is a good choice if you want to effectively store the binary data alongside the metadata, which describes the same.
So, by considering the type of data structure and how the computation on your data is done, you can make a perfect choice of the database engine. For this, you need to discuss how much data to be handled and what type of operations needed to be performed on the same. For computational purposes, the traditional relational database management systems tend to have very robust support for computation. At the same time, the NoSQL set relies largely on third-party computing systems like big data applications.
Databases like document-based, key-value, and columnar tend to work well with the need for aggregate analysis. In contrast, the SQL databases tend to perform extremely well in operational settings. While choosing the best database for you, you may always choose a general-purpose database, and there are many feature-rich and matured RDBMS systems out there in the market. However, with different databases available out there, it is also possible to take a new approach by treating the disparate data sources independently by using the best available tools. This is happening so for some time now as enterprises’ daily transactional data are stored in traditional RDBMS and then transferred through ETL to the columnar data store for better analysis. While choosing the right database engine, never compromise yourself with just one, which may force you to live with some trade-offs.
