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Redefining Data Engineering: GenAI for Data Modernization and Innovation – RandomTrees

RandomTrees

Data engineering, the practice of collecting, transforming, and organizing data for analysis, is poised for a significant transformation with the advent of Generative Artificial Intelligence (Gen AI). Generative AI with ETL Pipelines: Generative AI can be used to automate the creation of ETL pipelines.

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The Symbiotic Relationship Between AI and Data Engineering

Ascend.io

The rise of generative AI is changing more than just technology; it’s reshaping our professional landscapes — and yes, data engineering is directly experiencing the impact. How does AI recalibrate the workload and priorities of data teams? How can data engineers harness the power of AI?

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Data Pipeline Observability: A Model For Data Engineers

Databand.ai

Data Pipeline Observability: A Model For Data Engineers Eitan Chazbani June 29, 2023 Data pipeline observability is your ability to monitor and understand the state of a data pipeline at any time. We believe the world’s data pipelines need better data observability. To measure, but not track.

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15+ Must Have Data Engineer Skills in 2023

Knowledge Hut

The contemporary world experiences a huge growth in cloud implementations, consequently leading to a rise in demand for data engineers and IT professionals who are well-equipped with a wide range of application and process expertise. Data Engineer certification will aid in scaling up you knowledge and learning of data engineering.

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A Data Mesh Implementation: Expediting Value Extraction from ERP/CRM Systems

Towards Data Science

The disconnection between the operational teams immersed in the day-to-day functions and those extracting business value from data generated in the operational processes still remains a significant friction point. Searching for data Imagine being a data engineer/analyst tasked with identifying the top-selling products within your company.

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DataOps Tools: Key Capabilities & 5 Tools You Must Know About

Databand.ai

Here are some of the reasons why DataOps tools are important: Improved Collaboration DataOps tools enable better collaboration between data teams, including data engineers, data scientists, and data analysts. This enables data teams to quickly and easily find the data they need for their analytics projects.

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Data Lake Explained: A Comprehensive Guide to Its Architecture and Use Cases

AltexSoft

In 2010, a transformative concept took root in the realm of data storage and analytics — a data lake. The term was coined by James Dixon , Back-End Java, Data, and Business Intelligence Engineer, and it started a new era in how organizations could store, manage, and analyze their data.