Data is a very important part of any organization. If data is used carefully and strategically it can be used to make the right decision at the right time. In fact, companies should use both real-time and historical data to make decisions. Data analytics is a huge field and it is growing in recent times because of the high demand of specialists in this field who can make the complete data analytics process easier and effective. The data analytics can help predict losses, frauds and even give insights into the customer behavior. The data companies receive can help companies bring change that is needed in and outside of the organization. Thus the optimal use of these databases is very essential to drive effective growth.

Scalability is the capability of a system to handle an increasing amount of work, or its potential to be enlarged to accommodate that growth. Scalability is the buzzword these days around the cloud ecosystem. In fact, the central focus is the scalability of the databases, which is very popular these days. While evaluating databases it is ideal to take notice that the database scales in a way that is optimal to the business.

To scale efficiently the first consideration is traffic. When scaling database, it is ideal that nay application should be able to handle any amount of traffic at any given time. Now, some applications might have more traffic at certain times of the day, the week, the month or even the year. This has to be anticipated and accommodated. Another consideration is the automatic scaling of database according to the traffic. This will not only save costs but also man power. This is true elasticity and what you should be aiming for in this day.

Another consideration for scaling is that it should be done online which will remove any issues that might arise with downtime. So that the application runs smoothly at all times and so does the database scaling. This will save any potential losses. There are different types of scaling these should be decided on the kind of application performance you are looking at. There is scaling up, scaling down, scaling out and scaling in. Scaling up is the most common and it happens when your database needs more performance. This however has limitations because scaling depends on the performance of the system. Scaling out means that the frontend will give you more throughputs and the backend will give you more size. Another very important consideration for scaling is investment which will help you decide how much to sped and when.

When it comes to database sizing it is more on the hardware side along with some analytics. It mostly involves discussion and thought to be put in for the estimate requirements for a database. This means thought has to be put in for how much the database might grow and what are the things that might come in handy keeping in mind this database. Sizing database also involves the anticipation of disk requirements.

To see the disk requirements, it is ideal to consider a few forecasts in terms of the future database and the growth. However, it is impractical to consider all the things as this might require making a lot of detailed forecasts of all the aspects this requires a lot of time and effort to be put it. In fact, it might be the case that you might not know the answers to all these questions. It is ideal that you put confidence in the database forecasts and the the purchase decision. After the purchase and product deployment it is ideal to start to change the disk size and update it according to the change in the database size as and when it happens.

The best practice is to trace the actual database usage and records on regular intervals this will help you plan future disk requirements in advance. Also, accurately tracking the storage for each order line etc. will help plan future growth.

So it is very important to do database scaling and sizing to make optimal and timely decisions. Because these decisions are based on the accurate and prompt use of the data.