The market for Big Data services becomes more and more popular. This is not a surprise considering the technology diffusion. Big Data is quite popular nowadays because it helps to process a lot of information in a limited time. We produce a huge amount of data every second. And a lot of companies can work with this data to invent new technologies, approaches and have a profit of course.
Big Data is a set of technology that helps to process structured and unstructured data fast and efficiently. Machine Learning (ML) and Artificial Intelligence (AI) are possible because of Big Data. Without Big Data you can’t create and teach a neural network that will be a base for AI or ML.
Before start using Big Data, you should pay attention to three main marks of this technology. Maybe you’ll understand that you need a different approach to your project.
Big Data features
There are three main features: volume, velocity and variety. They also called “three V’s”. Sometimes you can face four, five or seven V’s including viability, variability, visualization and value.
Volume means the amount of data. Using Big Data is have a sense when you need to process really a lot of data. In other cases, other methods will be more efficient. Velocity means the speed of data processing. Big Data needs to process a lot of data very fast. Traditional data processing approaches and the human brain can’t work with such volumes. And variety means different types of data. This technology helps to process multiple types and find the dependencies and regularities. We also want to highlight value because this V is very important for the business. Big Data is great and profitable but it might have an expensive implementation. So, you should count expenses and profit to make the right decision.
How to implement Big Data for your project?
If you’re sure Big Data is what you need, the question is how to implement it to your project. You have two common ways – to hire an in-house or outsourcing team. Let’s have a look at the pros and cons of every variant.
Pros and cons of in-house Big Data team
With an in-house team, you’ll have better communication and one timezone. If you like to control everything, the in-house team definitely better than outsourcing. But you’ll need to spend a lot of time and resources for hiring. Also, you’ll need to equip workplaces and maybe find a new office for a bigger number of employees. The next question is what to do when the project ends? After implementation, you might not need the whole team and should fire the part of employees.
Pros and cons of outsourcing Big Data team
With a dedicated team, you might have communication problems because of the timezone and the language barrier. But fortunately, a lot of contractors know English. Considered the timezone, there are a lot of convenient countries for remote work. Big Data service providers have a lot of advantages. For example, you don’t need to spend time on hiring one by one, because you hire all team at once. The dedicated team doesn’t need equipped workplaces and you can say goodbye without pangs of conscience after the end of the project. Such teams have a wide experience because they work with very different projects, thus you’ll receive an experienced and cohesive team without waste of resources.
Where to find Big data service provider?
There are a lot of Big Data service providers but it’s very important to find the reliable one. An unscrupulous contractor can spoil the project or increase your expenses. Looking for a dependable provider company, try to find feedback from previous customers and provider’s awards. There are a lot of platforms with a rating of Managed Service Providers (MSP) where you can find necessary information. Also, don’t forget to talk with your future team, Team Lead or Project Manager. You might ask about their experience and what strategy they will use on your project.
The result of collaboration with experienced MSP is a profitable project and the prosperity of your business.