Data Engineering: The Rising Star Poised to Overtake Data Science in the Near Future?

In recent years, the world has seen an explosion of data, and it has become increasingly evident that organizations that can make sense of this data will have a competitive advantage. With the rise of data science and analytics, companies have been able to extract valuable insights from their data, and make informed decisions based on these insights. However, while data science and analytics have received a lot of attention, it is data engineering that is poised to become the next big thing in the software industry.

Data engineering is the process of designing, building, and maintaining the infrastructure required for storing and processing large amounts of data. This infrastructure includes everything from databases and data pipelines, to the tools and platforms required to analyze and visualize the data. While data science and analytics have been in the spotlight for some time now, it is data engineering that is becoming increasingly important, simply because it is the foundation on which data science and analytics are built.

One of the reasons why data engineering is becoming increasingly important is that more and more tools and resources are becoming available for data science. With the rise of low code and no code platforms, the complexity of data science is being reduced to an extent. This means that the demand for data scientists may begin to plateau in the coming years. However, data engineering is a different story altogether. There are fewer tools available for data engineering, and data engineers are still required to perform most of the tasks. This means that data engineering is likely to become even more in demand in the coming years.

Despite the complexity of data engineering, cloud platforms are doing a lot of research on reducing the complexities. Cloud platforms such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform offer a range of services that make it easier to store, process, and analyze large amounts of data. For example, AWS offers services such as Amazon S3 for storage, Amazon Redshift for data warehousing, and Amazon EMR for data processing. Microsoft Azure offers similar services such as Azure Blob Storage, Azure Synapse Analytics, and Azure HDInsight. These cloud platforms are making it easier to build and maintain data engineering infrastructure, but they still require programming and coding skills to make scalable solutions.

Another reason why data engineering is becoming increasingly important is that it is the foundation on which machine learning is built. Machine learning is a subfield of artificial intelligence that involves building algorithms that can learn from data, and make predictions or decisions based on that data. However, before machine learning can be applied, the data needs to be processed and cleaned, and the infrastructure needs to be in place. This is where data engineering comes in. Without proper data engineering, it is impossible to build robust and scalable machine learning models.

To wrap up, data engineering is becoming increasingly important in the software industry. While data science and analytics have received a lot of attention in recent years, it is data engineering that is the foundation on which these fields are built. With more and more data being generated every day, the demand for data engineering is likely to continue to grow. While cloud platforms are making it easier to build and maintain data engineering infrastructure, programming and coding skills will still be required to make scalable solutions. Data engineers will play a vital role in helping organizations make sense of their data and gain a competitive advantage in the years to come.