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Data Engineering, a pre requisite of AI

What is Data Engineering ?

Data Engineering is the set of operations which consists in structuring data

What is Data Engineering ?

Data Engineering is the set of operations which consists in structuring data

The data is the new oil of the 21st century. But if you want to use it, you have to refine it. This is the job of the data engineer. The mission of the data engineer is to collect all the data existing in the IT services of a company, in order to make them usable for the machine learning models of data scientists. To do this, he must do the following: - collect and centralize data in datalakes - clean and homogenize data by reconciling for example data sources and formats - ensure the reliability of the data stored in the data lake - implement data protection measures as set out by the GDPR

Data Engineering is operated with its specific technologies

Data engineering handles large amounts and diversity of data

Data Engineering is operated with its specific technologies

Data engineering handles large amounts and diversity of data

Data engineering requires both Cloud architecture and database skills. Indeed, a data engineer deals with processing very large databases or with various data. The data engineer must be able to manage all the stages of the data pipeline operations: - Ingestion - Data collection - Processing - Data processing to normalize them - Storage - Data storage for rapid recovery by an external service

Why Data Engineering is important ?

90% of the data stored in the world is unstructured.

Why Data Engineering is important ?

90% of the data stored in the world is unstructured.

90% of the data stored in the world is unstructured. However, 99% of machine learning models need structured data to be operational. In addition, when doing an artificial intelligence project, it is the improvement of the data pipeline that is more likely to improve the performance of an algorithm, rather than the work on the algorithm itself.

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