As data processing becomes increasingly complex, traditional solutions must evolve too. New tools such as Mage are reshaping the data engineering landscape.
The Mage tool
Mage is a data orchestration tool powered by machine learning artificial intelligence. It can be used to create open source pipelines and aims to streamline and optimise data engineering processes. Mage can run its pipelines to move and transform data. This means that data can be stored anywhere and used to train models, as in Sagemaker, a training platform for AI.
Other alternatives to this tool already exist, including Airbnb’s Apache Airflow.
Reshaping the engineering landscape?
Mage provides a pipeline for complete data engineering, including data ingestion, transformation, preview and deployment. This end-to-end platform ensures rapid data processing, efficient delivery and seamless connectivity.
Mage data engineers will have the ability to apply various operations during the transformation phase, ensuring that data is cleansed, enriched and prepared for further processing.
The preview stage enables the quality of the processed data to be validated and assessed, guaranteeing its accuracy and reliability.
Throughout the pipeline creation process, Mage gives top priority to efficiency and scalability. To improve performance, it uses optimisation techniques such as parallel processing, data sharing and caching.
At launch, Mage enables processed data to be transferred effortlessly to downstream or production systems. It has tools for automation, version management, error resolution and performance optimisation, enabling data to be transferred reliably and quickly.
In the same way that data science is developing, the tools used to exploit and create it will also push back the pre-established limits. We can already see this with artificial intelligence and the massive use of data in businesses. Data science is becoming an essential part of every business, and this sector is currently experiencing a phenomenal increase in recruitment and profile searches. That’s why, if you’ve enjoyed this article and are considering a career in data science, don’t hesitate to check out our articles and training offers on DataScientest.
Source : mage.ai
