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In today’s digital systems, vast volumes of information are produced daily. However, simply collecting large amounts of data ...
Knowledge graphs and ontologies have emerged as pivotal tools in process engineering, enabling the integration, simulation, and optimisation of complex industrial processes.
Mukul Garg is the Head of Support Engineering at PubNub, which powers apps for virtual work, play, learning and health. In my journey through data engineering, one of the most remarkable shifts I ...
Jerod Johnson, senior technology evangelist, CData Software, and Phillip Miller, senior product marketing manager, AI, Progress, joined DBTA's webinar, Powering AI/ML & Analytics with Smarter Data ...
AI-powered data engineering has the potential to completely transform business analytics, as demonstrated by Hari's career. For those who can fully utilize this technology, the future is bright.
While data science and AI get the spotlight, data engineering is the unsung hero behind successful AI systems. Let’s explore why data engineering is the backbone of AI: 1.
Data engineering is the next big thing, right? With all of the hype surrounding AI and data, our industry should have this squared away, but we don’t. In fact, data engineering is the confused team.
You need to build and maintain integration pipelines for each source — a massive engineering burden for data teams juggling disparate ETL tools to centralize what’s needed to power AI workloads.