Data engineers have an important job of transforming data into valuable insights for businesses.
Given the exponential growth of big data, and the ability of data engineers to manage and manipulate this data, data engineers are essential to a company’s success.
Challenges Of Data Engineering
That said, data engineering is far from easy.
The more data you have, the harder it is to make sense of it. After all, the average person can only focus on four pieces of information at a time. Even if we’re told that “data is the new oil,” more data also means more complexity.
One major obstacle is that the infrastructure needed to handle the data is costly and not available in most organizations.
This is particularly the case when building artificial intelligence (AI) models, which require a ton of computational power and specialized infrastructure. For instance, when building models for complex tasks like fraud detection, the size of the data required is huge. When you factor in the cost of renting hardware from cloud providers, model training and retraining and deployment, it can get very expensive.
Continue reading: https://www.forbes.com/sites/theyec/2021/08/17/how-data-engineers-can-achieve-competitive-advantage/?sh=6293a84a7db5
Given the exponential growth of big data, and the ability of data engineers to manage and manipulate this data, data engineers are essential to a company’s success.
Challenges Of Data Engineering
That said, data engineering is far from easy.
The more data you have, the harder it is to make sense of it. After all, the average person can only focus on four pieces of information at a time. Even if we’re told that “data is the new oil,” more data also means more complexity.
One major obstacle is that the infrastructure needed to handle the data is costly and not available in most organizations.
This is particularly the case when building artificial intelligence (AI) models, which require a ton of computational power and specialized infrastructure. For instance, when building models for complex tasks like fraud detection, the size of the data required is huge. When you factor in the cost of renting hardware from cloud providers, model training and retraining and deployment, it can get very expensive.
Continue reading: https://www.forbes.com/sites/theyec/2021/08/17/how-data-engineers-can-achieve-competitive-advantage/?sh=6293a84a7db5
