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How to Achieve Both Data and Application Scalability

When a business is growing fast, it demands a lot of adjustments to accommodate change. Business growth means it’s converting more customers, performing more transactions daily, hiring more, and expanding its base. Its systems, too, require upgrading to bigger data storage and more functional applications and devices. 

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Data and application scalability are interlinked because one cannot perform without the other. When system applications get an upgrade, the existing applications should not have downtime but should work at the same speed. When data increases, the system should handle it effectively without affecting productivity. 

What is data and application scalability?

The technology behind data movement and storage determines the scalability level in a business system. One of the technologies is the in memory data grid which helps deploy data in a highly distributed way to make cost manageable and accelerate data and application scalability. The technology helps reduce the movement of data across business networks and thus makes scalability more effective. 

Data scalability

For data volumes to be termed as scalable, the system must effectively handle new data as it grows in volumes. In an environment where business transactional data and external data are growing, it can affect the productivity of the system. This is because the data produced might be too much for the system. Some systems will slow down, others will hang, and others will produce unstable results. However, if the data system is scalable, it will effectively increase workload and perform at the same level of productivity. 

Application scalability

The term is closely related to data scalability, but in this case, it involves the applications needed to enable data movement and storage. Application scalability may involve both the applications and hardware. 

For example, when a company needs to accommodate more data into its system, it might opt to use RAM storage. For RAM to be used as storage, another application will be introduced into the system. Since a single RAM cannot handle all data, more hardware might be introduced for the company to have multiple RAMs. 

When this happens, the system might crash or slow down but if it is built in a way that adding more applications or hardware does not affect its performance, the system can be termed as scalable. 

Why the need for scalability?

In the 1990s, the internet was almost a reserve for professionals where they would exchange intellectual ideas, share scientific discoveries and educational literature. Today, the whole world is immersed in the internet, from the child in first grade to the retired senior member.

Compared to the 1990s, when email and websites were the norm, today, there are more platforms than an individual cannot count. Social media platforms increase and grow daily, there are blogs, websites, journals, entertainment media, news, etc. All these channels produce high quantities of data every second. 

Businesses treasure this data because it helps them understand the market and make profitable decisions. They need to know which platforms customers are using, what they are doing with the information, and what their next actions might be. Businesses use such data to study system security and the best software to use, like cybersecurity software to improve system security. 

For businesses to effectively achieve this goal, they need to store this data and process it while in their systems. To do this, they improve on their systems to help them handle the big volumes of data. 

On the other hand, the data harvested must be processable for analysis and to give real-time market pointers without affecting the existing systems. This is why data and application scalability are important. 

How to achieve data and application scalability

Applications scalability

Application scalability can be improved in several ways.

Upgrade applications: A business’s existing applications could be the older versions and if it updates them to newer versions, they help improve scalability. The business may also order new programs from developers. Better codes can be added to the application to enable it to handle the request pipeline.

Improve hardware: when a business is dealing with complex data, it requires complex applications to handle the demand. Even with the latest apps, they might underperform if the hardware they are operating from is inefficient. It might be the right time to upgrade hardware to allow applications to perform to the maximum.

Go with technology trends: Businesses are no longer using hard disks as storage solutions because they severely affect application scalability. They are using in-memory computing and other technologies to improve scalability to unimaginable levels. 

Data scalability

Use horizontal and vertical scaling methods: The old method of improving systems when a computer can no longer handle data is to buy another one with bigger storage. Some other businesses might upgrade the RAM if the computer is beginning to become slow. 

Horizontal and vertical scaling is something close to this but in this case, instead of buying a new computer, a business buys other computers to support the existing one. Using the software, they get interconnected such that they function as one unit. This is the level where RAM storage becomes very important.

Memory caching: When systems tend to be slower due to bottleneck challenges when a company is producing too much data, memory caching using caching products can be very helpful. 

Cloud solutions: Cloud computing does not provide scaling solutions on its own but relies on other technologies like operational data store, warehousing, Hadoop, and RAM to improve scaling. However, cloud storage is a gamechanger in data storage solutions. 

Splitting services: Handling all data from one system can affect scaling effectiveness but if the data is split into smaller portions, it can be managed easily. A business might not need to analyze all available data at once but it might require one portion of data at a time. 

If the data is split, it is possible to move it to another system separate from the main one and work on it from there, then move it back.

Store data closer to where it’s needed: If data is needed in location A and a business stores it in location D, it can be too expensive to move from there to the right location. The system might also be overworked and the data can become unscalable. Move the data and store it closer to the location where it is needed most. 

Also, store in your databases what is relevant only. You might want to avoid soring login sessions into your databases because they will use space and might affect scalability. 

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