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Data Engineering – A Journal with Pragmatic Blueprint

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction to Data Engineering In recent days the consignment of data produced from innumerable sources is drastically increasing day-to-day. So, processing and storing of these data has also become highly strenuous.

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The Complete Collection of Data Science Cheat Sheets – Part 2

KDnuggets

A collection of cheat sheets that will help you prepare for a technical interview on Data Structures & Algorithms, Machine learning, Deep Learning, Natural Language Processing, Data Engineering, Web Frameworks.

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Big data engineering simplified: Exploring roles of distributed systems

Data Science Dojo

They allow data processing tasks to be distributed across multiple machines, enabling parallel processing and scalability. It involves various technologies and techniques that enable efficient data processing and retrieval. Stay tuned for an insightful exploration into the world of Big Data Engineering with Distributed Systems!

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Introducing Agent Bricks: Auto-Optimized Agents Using Your Data

databricks

ignore all data before May 1990). Second, based on this natural language guidance, our algorithms intelligently translate the guidance into technical optimizations – refining the retrieval algorithm, enhancing prompts, filtering the vector database, or even modifying the agentic pattern.

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Big data engineer

Dataconomy

Big data engineers are essential in today’s data-driven landscape, transforming vast amounts of information into valuable insights. As businesses increasingly depend on big data to tailor their strategies and enhance decision-making, the role of these engineers becomes more crucial.

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Top Posts July 11-17: Machine Learning Algorithms Explained in Less Than 1 Minute Each

KDnuggets

Also: Linear Algebra for Data Science; 10 Modern Data Engineering Tools; Parallel Processing Large File in Python; How Does Logistic Regression Work?

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Data Engineering for IoT Applications: Unleashing the Power of the Internet of Things

Data Science Connect

A recent article on Analytics Insight explores the critical aspect of data engineering for IoT applications. Understanding the intricacies of data engineering empowers data scientists to design robust IoT solutions, harness data effectively, and drive innovation in the ever-expanding landscape of connected devices.