Big Data Data Engineering Engineering Management 
O'Reilly Media - Data
APRIL 11, 2018
It’s important to understand the differences between a data engineer and a data scientist. Misunderstanding or not knowing these differences are making teams fail or underperform with big data. I think some of these misconceptions come from the diagrams that are used to describe data scientists and data engineers.
AWS Machine Learning - AI
SEPTEMBER 3, 2024
Harnessing the power of big data has become increasingly critical for businesses looking to gain a competitive edge. However, managing the complex infrastructure required for big data workloads has traditionally been a significant challenge, often requiring specialized expertise.
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AWS Machine Learning - AI
AUGUST 8, 2024
About the Authors Apurva Gawad is a Senior Data Engineer at Twilio specializing in building scalable systems for data ingestion and empowering business teams to derive valuable insights from data. She has a keen interest in AI exploration, blending technical expertise with a passion for innovation.
Apiumhub
APRIL 5, 2022
M2- Data Engineering Stage: Technical track focusing on agile approaches to designing, implementing and maintaining a distributed data architecture to support a wide range of tools and frameworks in production. Presentations by some of the leading experts, researchers and practitioners in the area.
The Citus Data
MARCH 31, 2023
(on-demand talk, Citus open source user) 6 Citus engineering talks Citus & Patroni: The Key to Scalable and Fault-Tolerant PostgreSQL , by Alexander Kukushkin who is a principal engineer at Microsoft and lead engineer for Patroni.
Altexsoft
APRIL 26, 2022
With Snowflake, multiple data workloads can scale independently from one another, serving well for data warehousing, data lakes , data science, data sharing, and data engineering. BTW, we have an engaging video explaining how data engineering works.
CIO
FEBRUARY 20, 2025
Stone called outdated apps a multi-trillion-dollar problem, even after organizations have spent the past decade focused on modernizing their infrastructure to deal with big data. AI models can then access the data they need without direct reliance on outdated apps. We are in mid-transition, Stone says.
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