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 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
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.
Discover our digest about Data Engineering
3 Steps to Improve the Data Quality of a Data lake
From Customising Logs in the Code to Monitoring in Kibana
Automate AWS Tasks Thanks to Airflow Hooks
This article is a step-by-step tutorial that will show you how to upload a file to an S3 bucket thanks to an Airflow ETL (Extract Transform Load) pipeline
How to Get Certified in Spark by Databricks?
This article aims to prepare you for the Databricks Spark Developer Certification: register, train and succeed, based on my recent experience.
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