Most engineers I know (myself included) like to create "stuff" efficiently, often even more than they like to create the "stuff". We are trained to build in a way that would produce positive outcomes for whoever will use our creation thus efficiency is a big part of it: - How long will it be until someone can use it? - Will it last once we deliver it? - How much will it cost us? Join Hannan Kravitz, Guy Biecher, and myself as we dive into what the above means for the efficient data engineer. https://hubs.li/Q02-s9D90
Ariel Pohoryles’ Post
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I like the Data Engineering Central articles. It has a grumpy way to talk about data engineering and technology, but the content is interesting. In my case, I am from the Group 1 that sometimes explores the skillset from Group 2. Which group would you belong? https://lnkd.in/d2FSmd9u
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Level-2: These are all about from datasets-restaurants data analysis. Tasks-1 Table Booking and Online Delivery Tasks-2 Price Range Analysis Tasks-3 Task: Feature Engineering Excited about the foundational knowledge gained and eager to apply it to real-world challenges. Onward to the next level! #cognifyz #cognifyztech #cognifyztechnologies #dataanalysis #datascience #pythonprogramming
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100 Days of Discussing Data Analytics Engineering Challenges Day #1: 💡 Critical Database Transactions: How do you choose data consistency over performance? >> My take as a Data Analytics Engineer: When prioritizing data consistency in critical transactions: 1. ACID-compliant databases ⚙️ 2. Locks or isolation levels 🔒 3. Distributed transactions for cross-system integrity 🔄 4. Retries for transactional success ⏳ By adhering to ACID principles, and using mechanisms like row-level locks, we ensure transaction integrity even at the expense of speed. What’s your approach? Drop your thoughts in the comments! 👇✨
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code documentation and profilling is paramount. all scripts are assets dedicated to solve a particular scenario In a report out. it needs to be knit and well documented.
Data engineers should have clear responsibility and ownership around the code they maintain. #dataengineering
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In Data Engineer Academy's latest YouTube video, we share exactly how went from making $60,000 to $500,000 in just a few years as a #DataEngineer. We'll walk you through the key steps, strategies, and mindset shifts that helped me scale up quickly. If you're a #dataanalyst, aspiring data engineer, or someone wanting to break into tech with real momentum, these tips can give you the boost you need. ✔ Watch the full video on YouTube and explore more topics on our YouTube channel: ttps://https://lnkd.in/eiRfvT5F
How I QUICKLY went from $60,000 to $500,000 as a Data Engineer (Without a tech degree)
https://www.youtube.com/
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📚 CAP theorem — What Every Data Engineer Should Know 💡 Article by: Santosh Joshi ➡ Link to Article: https://lnkd.in/eFUnsR5U - - - - - - - - - - - - - - - - - - - - - - - - - - - 💡 Follow for more great content! ✅ ✍️ Want to contribute? Connect with us. 🔗 Let's create together! ✅ - - - - - - - - - - - - - - - - - - - - - - - - - - - #dataengineering #dataarchitecture #data #systemdesign
CAP theorem — What Every Data Engineer Should Know
medium.com
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Another one for my collection. Small steps towards becoming a data engineer.
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I am excited to share our latest blog post, "How to Flatten Nested JSON Arrays (2024)." Dive into this comprehensive guide that simplifies the process of working with complex JSON structures. In this post, we break down the techniques and best practices to efficiently flatten nested arrays, providing insights that can enhance your data manipulation skills. Whether you're a developer, data analyst, or just starting your journey in data handling, this article offers valuable strategies to streamline your work. Explore the full post here: https://ift.tt/bmlN1ke.
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Different type of data structures
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Head of Product Marketing at Rivery.io
2wjoin us tomorrow: https://hubs.li/Q02-s9D90