Summary
The Data Engineering hub is focused on bringing together information, experts, organizations, policy makers, and the public to LEARN more about a topic, DISCUSS relevant issues, and COLLABORATE on enhancing research-driven DE knowledge and addressing DE challenges …. …. where onAir members control where and how their content and conversations are shared free from paywalls, algorithmic feeds, or intrusive ads.
The onAir Knowledge Network is a human-curated, AI-assisted network of hub websites where people share and evolve knowledge on topics of their interest.
This About the Data Engineering onAir 2 minute video is a good summary of DE hub mission and user experience.
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Source: Other
Data with Baraa – 13/05/2025 (22:46)
OnAir Post: Data Engineering on Air
News
To grow and thrive in the rapidly evolving AI landscape, organizations must strategically invest in their data engineering capabilities.
In today’s modern digital landscape, businesses are generating heavy data daily which can be processed, analyzed and interpreted for future scalability and growth. This is when AI-driven systems become integral across industries to help create real-time analytics, forecasting and initiating AI-driven automation. Beverly D’Souza, a Data Engineer at Patreon (previously worked at Meta) has played a key role in improving data workflows, processing data at pace and launching machine learning models. Having experience with ETL pipelines, cloud data systems, and AI analytics, she shared, “Building scalable AI-powered data pipelines comes with key challenges and to overcome these obstacles, organizations must implement distributed computing frameworks that can handle large-scale data processing efficiently. Incorporating AI-driven automation helps streamline data processing tasks, making the entire system faster and more efficient.”
About
Overview
What is Data Engineering?
Data engineering involves designing, building, and maintaining systems and architectures that collect, store, and analyze large-scale data. It encompasses the creation of data pipelines to ensure data flows efficiently from source systems to data storage and analytics platforms. Data engineers extract data from various sources, transform it into a usable format, and load it into data storage solutions like data warehouses or data lakes.
Importance of Data Engineering
Data engineering ensures data reliability, accessibility, and quality, which are critical for data-driven decision-making. Robust data engineering practices enable organizations to leverage data for insights, operational efficiency, and competitive advantage.
Future Scope of Data Engineering
The future of data engineering is promising due to the increasing importance of big data, AI, and machine learning. The demand for skilled data engineers will continue to rise with the growth of data volumes. Technologies like cloud computing, real-time data processing, and advanced analytics will further expand opportunities in this field.
Source: Ronit Malhotra
Videos
How I would learn Data Engineering in 2025
May 13, 2025 (22:46)
By: Data with Baraa
00:00 – Intro
02:24 – Phase 1 Roadmap
15:10 – Phase 2 Roadmap
19:03 – Phase 3 Roadmap
