Data Engineering in Artificial Intelligence

Today, data is a vital aspect of any business. Companies use and construct various data structures to build customer relationships and respond to business queries. A wide range of technologies is applied to the data to understand customers and their requirements. Managed by distinct technologies and stocked up in several structures data analysis becomes a challenging task. However, the tools owned for analysis accept that the data is from a single technology and stored in an identical structure. Data engineering eases the process of providing reliable and secure data to scientists, data analysts, and other professionals who work with data regularly.

The importance of data engineering and its scope as a career choice has been growing exponentially in recent years as companies of varied sizes rely heavily on data. Some of the points that support the above statement are:

• Around 90% of the huge data has been produced in recent years; data is growing exponentially
• Data is managed through complex technologies such as NoSQL, relational databases, and Hadoop
• Engineering the data is the linchpin in many activities such as future prediction, modeling the customers, preventing threats, and a better understanding of business conditions
• The more complex the data, the higher the demand for data engineering roles as data engineering becomes critical in the future

Data engineering organizes and manages suitable data for different systems and various data consumers, such as system architects, business leaders, data analysts, and data scientists. Some vital areas of data engineering are:

• To gather the data requirements
• To maintain metadata
• To ensure the governance and security of data
• To store data
• To process data for specific requirements.

The above responsibilities are well addressed by data engineers through data acquisition, cleansing, conversion, disambiguation, and de-duplication. Hence, data engineers should possess the ability to work on different technologies using specialized tools.

Programming and math/statistics are two of the most critical technical abilities for a data engineer to acquire.

Programming: Software developers transitioning to an AI role or those with a computer science degree are likely to be familiar with a few programming languages. Python and R are two of the most widely used languages in AI, particularly machine learning. Every aspiring data and AI engineer must be well-versed in these two languages, as well as their most often used libraries and packages.

Math / Statistics: More than merely coding, data and AI engineering require math and statistics. Machine learning algorithms are based on statistical and probability notions. When verifying the authenticity and integrity of your data, you will also require a good grasp of terms like statistical significance.

Artificial intelligence and data engineering provide a reliable artificial intelligence solution that can work at scale by providing usable products to clients. Artificial intelligence and machine learning possess high demand for data that is impossible to scale by human engineers. To incorporate artificial intelligence capabilities and feed the data-hungry models many transformative technologies must be implemented for the analysis and storing of data. If the role of a data engineer lies in preparing the data, then the data scientist's responsibility is to use the data for the promotion of business decisions. To summarise, a career in data engineering is both challenging and rewarding because providing artificial intelligence assistance to a wide range of companies in industries such as healthcare, retail, finance, etc. becomes unrealistic without structured data and the technical resources to mine the data. Data engineering experts with technologies to mine the forthcoming zettabytes of data will be the need of the hour in the coming future.