5 Must Have Feature Your Big Data Analytics Model Should Have

Big Data analytics tools and practices have grown leaps and bounds in the last 2-3 years.

Big Data analytics tools and practices have grown leaps and bounds in the last 2-3 years. The rise of Artificial Intelligence, Machine Learning, Hyper automation, and no-code / Low code techniques have ensured that the advancements in Big Data practices remain dynamic and fast-paced.

Big Data Analytics software development involves tools that help reduce manual coding and programming of software and match unprecedented results in real time.

Here are the five must have features your developing Big data analytics model should practically demonstrate.

Easy Result Formats

Results are vital and the way your data model depicts reporting and visualization are critical. If you are learning data visualization techniques using R and Python programming languages, you might gain an upper hand in designing the best in class data modeling. Data Scientists, no matter how much experience they bring to the business, still look out for easy formats that support the decision making processes that are increasingly becoming a trend in the current big data market.

Your tool should be able to maintain consistent formatting and reformatting features that can help improve the overall decision making process in a short period of time.

The majority of the Process Action should be Automated

Automation is a very important feature in any data analytics software. You can improve the overall performance by increasing the “drag and drop” interface, automated column / row updates, or advanced data reporting using SAS or Python programming for better data visualization in the form of bars, Pie charts, or burger menus.

Using AutoML for Raw Data Processing

Now, we are getting into deeper aspects of the data collection and mining process where we bring in Machine Learning models to meet the high standards of Big Data analytics courses.
Tools must be able to incorporate the Automated Machine Learning models and upgrades to import raw data from various data sources, including image and voice data. From collecting data from various datasets and sources to managing them using directories and archives, you can use machine learning for more than analyses.

Data Conversions

If you are advancing in big data analytics courses, focus on data conversions. It could be automated for simple conversion from various formats like Excel, PDF, or JPEG.
Your data model should be able to handle these usefully and without losing volume, variety, and value.

Security and Authority

Finally, we are at the most important stage of principally saving any business from falling into wrong hands. Data security, governance, and compliance practices are beginning to be taught at data analytics courses, and this is a good sign considering the rampant cyber attacks on Big Data storage.

If you are looking for a career in Big Data analytics, there are more than 50 different roles and activities you can undertake as part of your internship, or project development. In this article, I am focusing on one of the many ways a Big Data analytics course could help succeed in your project development.

Alen Parker

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