Big Data testing and transformation
When a business decides to implement Big Data analytics, one of the key challenges is allocating the computing resources correctly. The Machine Learning model that will perform the data analysis of your structured, semi-structured and unstructured data must have enough resources to process the huge data sets and deliver the results you require, but not too many resources, or they will go to waste.
One of the key components in ensuring the correct allocation of resources is building the correct Big Data testing process. The Artificial Intelligence algorithm you use will have to process the previously prepared data, or valuable time and resources would be spent analyzing irrelevant data or simply noise. Thus said, you must specify beforehand what results you want to achieve from your Big Data analytics, and what parameters must be monitored in order to deliver these results. The choice of parameters determines the need for specific databases, using particular features like Map-Reduce and so on.
The results you need can be ensured by performing Big Data testing before starting the analytics. The data you operate vill come in a variety of forms, with different speeds and will differ in volume. Thus said, it should be tested to ensure consistency, compatibility, remove duplicates, normalize the values and leave only the important parameters to be tracked and processed. This can be done using Python scripts and IT Svit has an ample experience with configuring such systems and building the tools to enable Big Data testing.
IT Svit helps with Big Data testing strategy
If you ever decide to analyze all the unstructured, semi-structured or structured data generated by your business, you will lose lots of time and effort in wain. As a matter of fact, the models you would have to employ would require such huge data sets for training and such a large volume of cloud computing resources for processing, that your business would have to pay exorbitant sums.
This is why a holistic Big Data testing strategy must be in place, as it can ensure you extract, transform and load only the relevant data so that your resources allocated correctly. Designing and creating such a strategy requires a good working knowledge of Data Science principles, as well as thorough understanding of DevOps practices of cloud infrastructure management required to store and process the data. Here is how IT Svit actually does it:
- First of all, we must determine whether your project actually needs a Big Data analytics model, or a set of mathematical equations will do. Sometimes, especially when only the statistical analysis is required, this can be the best approach. A mathematical tool can extrapolate the historical data sets available and extrapolate the results for ongoing calculations.
- If the Big Data model is needed indeed, we select it based on your project requirements. Sometimes it is a naive Bayesian model, sometimes it is a Deep Learning neural network, sometimes it is a decision forest.
- Once the most appropriate ML model is selected, it must be applied against the available historical data in order to teach it to monitor the required project parameters.
- The model must not be overtrained, as it would be a waste of resources. Therefore, once some precision threshold is reached (80% for some projects, 92% for others), the training must be stopped.
- The model must be deployed to production and begin analyzing your data to deliver the value you wanted to get out of the Big Data project.
If you want IT Svit team to help implement your Big Data analytics project and configure the correct Big Data testing for your business — contact us today, we are always ready to assist!
