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The IT industry has always been in the need for highly scalable systems. Over almost a decade now, the industry has experimented with different things and came up with many things to learn in the process. It does the job of connecting users with remote resources via the internet and distributed across multiple servers.
Importance Of Scaling
In this world of technology it is important to keep aware of the latest trends and evolve according to them, to keep your data pipelines updates. It is important to build scalable technology to leverage data science effectively. It is necessary to see what pieces can fit together perfectly and the appropriate tradeoffs for profit. Adopting scaling before analysing whether it is needed or not is not the right way to go. A prior knowledge can save a great amount of time and resources for the future.
Tips For Building Scalable Architecture
Scalable systems are important because it helps to make better decisions and to let use data science effectively. It is one of the important steps in future proofing company technologies. Here are some of the things to keep in mind for building a successful scalable database architecture.
1.Make sure that scalability does not negatively affect the whole system:
Scalability can be described as an effort required to increase capacity to handle greater load in the organisation or the data science team. It could mean the amount of traffic that the system can handle, or how easy is it to add more storage capacity.
It is important to build scalable database architecture for the growth, but at the same time, it should also be known that by scaling up, you must know the implications of it on the entire data pipeline. Adding scalability might affect the entire system and add additional stresses to it, and that is not what is desired. Connections in systems with respect to data sources, ingestion and storage, cleaning and processing , are all interconnected and need to be maintained between technologies in a system. It is important to make sure that adding functionality at one stage does not add demands on the rest of the system.
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2.Get the data model right:
Make sure that the model that you are building fits the best for your knowledge and expertise, and not just improves the accuracy of the model. The models should be easy to maintain and the modellers do not have to master new unfamiliar optimization parameters. If there is research been done ahead of time, this problem will not arise and can easily point tp positive cost-benefit analysis in the long run and these kinds of short-term loses will not be very difficult to address.
Described as an example on O’Reilly, adding a neural network component to a predictive model may improve its accuracy, but it demands the learning of new parameters.
3.Adopt new technologies wisely:
Getting attracted to new technologies is easy and often they are immediately tested by modellers to see its performance and features. But to put it into use an architecture, make sure that it suits with the type of project in hand and consider the technologies that are already involved. Do not throw away the older technology, since there is always an established knowledge base surrounding it and the organisation is aware of it.
Do not just go ahead with adopting new technologies. Keep in mind the differences that it would introduce in the functioning with respect to the new technology, as it may not be seamless with the old process. Even if the performance metrics you care about are demonstrably better, there’s still a disruptive effect. It is important to understand hat adopting new technologies will need to master them as well. This kind of built-in knowledge transfer is hugely important down the road, when other developers need to build on earlier work.
4.Upskill the team:
Adopting new technologies will eventually require mastering them for the long tearn. The entire team has the responsibility to do that. An informal knowledge sharing platform at organisations is a great way to upskill the team. It allows everyone to contribute to the discussion. It is important to make sure that everyone in your data science team is upskilling themselves and that goes parallel to the technology adoption of the team, or the organisation.
Cost considerations as one of the important factors to consider while building a scalable database architecture. Cost in terms of not just adding the hardware and software functionality, but also of maintaining the added software and hardware to the system, is important. The amount involving operational effort to run the system, the amount required in training new technologies, need to be taken into consideration. Cost benefit in the long run is an important aspect to analyse, pointing to a positive-cost benefit. Do the most you can at the least cost and then decide if it’s worth investing more.
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Found a way to Data Science and AI though her fascination for Technology. Likes to read, watch football and has an enourmous amount affection for Astrophysics.