What is Data Engineering?
In the modern era, data is being generated and stored at an unprecedented rate. In the last two years alone, 90% of the world’s data was created, and the pace is only set to accelerate.
The Internet of Things, social media, web services, mobile devices, transaction data, and databases are among the sources responsible for producing massive amounts of structured and unstructured information, known as big data.
Data science applications are allowing organizations to use big data to take a data-driven approach to solving complex business problems, allowing them to reduce operational costs, create new products and services, and identify new sources of revenue. To do this successfully, they must have access to the right data, in the right format, at the right time.
In most organizations, however, data sets are stored in various formats and rely on different technologies. This is where data engineering provides the solution. While data scientists are concerned with producing insights from a set of data, data engineers focus on getting that data production-ready.
To make data both clear and actionable, it must be cleaned, validated, and prepared for whatever the data scientist is trying to achieve, and allow queries to be run against it. This often means taking a disorganized or unrefined source of data, and converting it into something usable.
Data engineers are also responsible for building and maintaining an organization’s data pipeline. This incorporates everything from gathering the necessary data, processing it, storing it, and enabling access to the end user, whilst taking account of the various technologies and frameworks involved.
Who can benefit from Data Engineering?
A growing number of companies, both large and small, are capturing their data and taking advantage of the insights stored within it. Rapid technological advances have made big data analytics more widely accessible, meaning that any organization which depends on high quality information for decision-making can benefit from data engineering and its subsequent application in data science.
A data-driven approach can help your business become more dynamic, agile, and profitable. From enhancing customer experience with a recommendation engine, to predicting future demand, to detecting anomalies and preventing fraud, the possibilities are endless.
Although becoming mainstream in many areas, data engineering and data science have revolutionized certain industries. In healthcare, organizations are using data to recommend treatment options and make lifesaving diagnoses. The financial services industry is using machine learning to identify and reduce fraudulent transactions, along with advances in anti-money laundering, credit risk management, and regulatory compliance. And in manufacturing, artificial intelligence is being used to increase the efficiency of operations and reduce costs.