Diana Barros

📌Power BI Alerts Stay Ahead of the Data Did you know you can set up data-driven alerts in Power BI Service to get instant notifications when KPIs cross defined thresholds? ➡️ No more manual monitoring ➡️ Real-time updates via email & Power BI Service ➡️ Seamless integration with Power Automate for automated workflows This means you can ➡️ React faster to business-critical changes ➡️ Save time with automated monitoring ➡️ Enable smarter, proactive decisions Use alerts only on key KPIs to avoid notification fatigue and review them regularly for relevance. Start using Power BI Alerts today and make your dashboards truly proactive! #PowerBI #DataAnalytics #Automation #BusinessIntelligence

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The biggest lie sold to Data Engineers in the last 5 years is that "Storage is cheap, so you don't need to model data anymore." Sure, storing the mess is cheap. But scanning it is bankrupting you. We stopped teaching Dimensional Modeling because it was "hard." We replaced it with raw dumps and "Schema-on-Read." Now, every simple query triggers a full table scan on a petabyte of JSON blobs. Here is the uncomfortable truth about your Cloud Bill: Lazy Architecture: You are burning $500 in Snowflake credits to answer a question that a Star Schema could answer for $0.05. Performance: Without Conformed Dimensions, your joins are exploding, and your dashboards are timing out. 💀 Johnny Winter 💀 isn't just teaching a "course." He is teaching Anti-Fragility. I can teach a Junior Python in a week. I can't teach them how to design a grain that keeps the CFO off my back. If you don't know the difference between a Slowly Changing Dimension and a Snapshot, you aren't a Senior Engineer. You are a liability that is going to burn our entire Q1 compute budget by Valentines day. Go learn this before your next architectural review. #DataModeling #Kimball #DataEngineering #CodeToCash #TechnicalDebt

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Hello Peeps 👋 I'll be conducting a session tomorrow on Hands on Hive session at 11:30AM Create Hive tables, discuss the intricacies of creating tables for different types of tables. How metadata works and all the works I'll share the link in sometime

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"I did a second round interview two weeks ago and its been radio silence ever since" It seems to be a near daily occurrence when speaking to people on the market to hear something like this. This is coming from some top quality Data & Machine Learning Engineers too... 𝗔 𝗣𝗼𝗹𝗶𝘁𝗲 𝗥𝗲𝗺𝗶𝗻𝗱𝗲𝗿: An update to say there are no updates, is an update in itself 💯 Hiring at this time of the year can be VERY hectic, I get it. End of financial year, lot's going on. Don't go to market if you're not ready to hire. Don't tarnish your reputation by going silent during a hiring process. Be TRANSPARENT with timelines! Communicate! Be TRANSPARENT, full stop! If you interview someone, but need time to think, or have other interviews lined up, or know you won't be able to confirm next steps for a few days, or anything at all that prevents things progressing along as they should... Let people know! All it takes is a short text, email, phone call, anything is better than nothing. People talk, and people ARE naming the companies who are acting in this manor. As the old saying goes “A happy customer tells 1 person, an unhappy customer tells 10.” #InterviewTips #HiringTips #DataEngineer #MachineLearning #DataTransformation #DataInterviews

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Smarter Financial Insights with Unsupervised & Interpretable Machine Learning In finance, one of the biggest challenges is uncovering structure in seemingly chaotic market data. As part of ML Project in my MSc Financial Engineering program, our team tackled this using a mix of unsupervised and interpretable machine learning techniques. What we built: Principal Component Analysis (PCA) – Reduced the dimensionality of correlated stock returns, leading to more stable models and better portfolio optimization. K-Means Clustering – Grouped assets into risk-based clusters, paving the way for targeted portfolio strategies. Classification Trees – Generated clear buy/sell signals from SMA and RSI indicators, with minimal preprocessing and high interpretability. Key Takeaways: Interpretability builds trust with decision-makers in finance. Combining PCA with clustering enhances segmentation quality. Simple, transparent models can still deliver valuable alpha. Shoutout to the dream team: A huge thanks to Anuj Chavan and Dheeraj Kumar Yaduwanshi 🇮🇳 for the collaboration, coding expertise, and insightful discussions that made this project possible. 🙌 #MachineLearning #Finance #PCA #KMeans #ClassificationTrees #FinancialEngineering #DataScience

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Today I want to tell you all about the time I worked with VBA just recently. For anyone not in the know VBA is a programming language integrated into Excel (yes, you can program in excel) which allows to either automate some excel processes or build a completely separate application simply “located” so to say inside an excel sheet. The project I recently concluded was a simple Excel employee work sheet blueprint, meaning the Excel sheet where the overseer would punch in all the tasks the employee has done during the shift. Upon filling the sheet and pressing the “Upload” button the report on the employee’s workday would be saved into the company Dropbox reports folder and additionally the info on the employee performance would be properly formatted and added to the sort of “database” Excel file holding all of the previous daily performance reports for every employee, which is also stored on Dropbox. Additionally I added the functionality to easily attach the already formatted Excel report file to an Outlook message by simply clicking the “Send” button. What I found most interesting was how a fairly simple VBA setup can connect multiple tools such as Excel, Dropbox, and Outlook into one smooth workflow. Definitely a good reminder that impactful solutions don’t always require complex tech. #VBA #AutomatingWorkflows #Programming

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An enjoyable afternoon and evening at Data Decoded and Manchester Databricks User Group yesterday! 📊 My highlight was the keynote session by Matthew Scullion on ‘AI needs Data. Data needs AI’. We’re in an era where AI is real, and not just another technology mega trend that will fade away. The constant theme is the need for strong data quality. If garbage goes in, garbage comes out. The responsibility and burden of that falls onto the data engineering team, as per the photo! AI is the digitised Industrial Revolution - it’s an important time we’re in! Data teams that enable AI, may become ‘villains’ as businesses see others around adopting AI on their datasets. There is a machine scale need for data, but teams that can only provide human scale A fantastic demo of Maia from Matillion showed how to overcome the machine scale need for data. A fantastic showcase of how agentic AI can accelerate data engineering 🎉

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💡 Helping More Women Step Into Data & Analytics — Your Input Needed After 15+ years working as a Senior BI Consultant & Data Modeller, I’ve seen how powerful SQL is as a foundation for a career in data — and how valuable it can be to learn it in a supportive, women-focused environment. I’m exploring the idea of running a beginner-friendly, in-person SQL workshop for women in the Netherlands — a safe, practical space to try SQL, ask questions, and understand how it’s used in real-world BI and analytics projects. Before I design it, I want to make sure it meets real needs. Could you spare 2 minutes to complete a short survey? Your feedback will shape whether this workshop happens, and if it does, what it will look like. 📍 Audience: Women in the Netherlands interested in exploring data careers 🎯 Goal: Accessible, hands-on intro to SQL in a women-only setting ⏳ Time: 2 minutes 👉 Survey link is in the first comment below Thank you for helping me make data skills more inclusive and accessible! #WomenInTech #WomenInData #LearnSQL #Netherlands #BI #DataAnalytics #Leadership #Mentorship

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Third module on data warehousing done for the Data Engineering Zoomcamp! This time I've incorporated cloud storage into my workflow to: ✅ Create external tables from Google Cloud Storage bucket data ✅ Build materialised and external tables in BigQuery ✅ Utilised partitions and clusters to optimise queries and performance ✅ Analysed datasets working with over 20M records Notes and repo for module: https://lnkd.in/eeC3CN-x #DataEngineering #DataEngineeringZoomcamp #DataTalksClub #dezoomcamp

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