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How to Crack Amazon Data Engineer Interview in 2025?

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Become a Job-Ready Data Engineer with Complete Project-Based Data Engineering Course ! What are some commonly used file types in data engineering? A feedback mechanism is also established to communicate data quality issues to relevant stakeholders, fostering collaboration for effective problem resolution.

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7 Best Data Warehousing Tools for Efficient Data Storage Needs

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These tools are crucial in modern business intelligence and data-driven decision-making processes. They provide a centralized repository for data, known as a data warehouse, where information from disparate sources like databases, spreadsheets, and external systems can be integrated.

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How to Learn AWS for Data Engineering?

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Amazon Redshift makes it simple, quick, and more secure for data engineers to gain insights about cloud data warehousing. Using Amazon Redshift, data engineers can analyze all of the data in operational databases, data lakes, data warehouses, and third-party data.

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Microsoft Fabric - All-in-one AI-Powered Analytics Solution

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With Microsoft Fabric, you can integrate data from various sources, including point-of-sale systems, inventory databases, customer relationship management (CRM) tools, and external sources like weather forecasts and social media trends. This cutting-edge feature enhances efficiency and streamlines the data preparation process.

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30+ Data Engineering Projects for Beginners in 2025

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And, out of these professions, we will focus on the data engineering job role in this blog and list out a comprehensive list of projects to help you prepare for the same. Cloud computing skills, especially in Microsoft Azure, SQL , Python , and expertise in big data technologies like Apache Spark and Hadoop, are highly sought after.

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How to Build an AI Agent with Pydantic AI: A Beginner's Guide

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Pydantic AI agents offer a robust, Python-centric framework designed to streamline the development of AI-driven applications. In contrast, PydanticAI is a Python-centric framework focused on type safety, structured response validation, and integration with multiple LLMs. Source: www.linkedin.com/ Why Use Pydantic AI Agent Framework?

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Machine Learning Engineer vs Data Scientist - The Differences

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If you look at the machine learning project lifecycle , the initial data preparation is done by a Data Scientist and becomes the input for machine learning engineers. Later in the lifecycle of a machine learning project, it may come back to the Data Scientist to troubleshoot or suggest some improvements if needed.