Reza Rad’s Post

(Part 5 of 5) 🛒GCP Retail Validation & Trend Analysis For the final project of this series, I focused on validating and analyzing 543K retail transactions across multiple countries to uncover revenue trends, customer activity patterns, and top-selling products. Using Google Colab with SQL and Python, I explored the data, orchestrated workflows in Airflow, applied dbt and Soda for validation and modeling, stored results in BigQuery, and built interactive Power BI dashboards. The analysis revealed $8.9M revenue, 5.2M items sold, 18.5K invoices, and 4.3K customers, along with monthly revenue shifts, country-level disparities, and product mix insights. Compared to the earlier projects - Olympics (historical trends), YouTube (regional behavior), and Real Estate (anomaly detection) - this one emphasized data quality, validation, and reliable insights for business growth. With this post, the series comes to a close - showcasing how I applied data analytics across different domains, each with its own focus: trends, correlations, anomalies, and validation. Together, they highlight the versatility of data in uncovering meaningful insights and driving better decisions. github: https://lnkd.in/ebi5Zd6G #DataAnalytics #BigQuery #dbt #Soda #PowerBI #RetailAnalytics #BusinessIntelligence #DataProcessing #DataPipelines here is the breakdown 👇

🌀 MS FABRIC : DP-700 🌀 💭 Imagine this: 🛒 A retail platform processes 50M+ transactions/day ✈️ An airline collects engine sensor data every second 🏥 A hospital monitors thousands of patients in real time Now ask yourself: 👉 Without the right aggregations, how do you even begin to see what matters? ⚡ This is where Kusto Query Language (KQL) quietly shines. It’s not just about querying data — it’s about distilling noise into signals. 🔎 With a few lines of summarize magic, you can transform chaos into clarity: 📈 Spot spikes in system errors before outages occur 👩⚕️ Detect anomalies in patient vitals 💰 Find revenue leaks in billion-row sales tables 📊 In my latest write-up, I cover: ✅ How KQL aggregations simplify massive datasets ✅ Real-world industry use cases ✅ Best practices for high-impact dashboards 💡 Because in big data, insight ≠ data. It’s the ability to summarize ➝ interpret ➝ act. 🔗 Grab the PDF & see how KQL Aggregations unlock this power! #KQL #MicrosoftFabric #DataEngineering #Analytics #BigData

In 2025, real-time analytics has become the new baseline. Whether it's finance catching fraud in seconds, retail adjusting stock on the fly, or logistics rerouting deliveries in real-time,speed is now a business necessity. If your data tools still work on yesterday’s timeline, you’re already behind. Now’s the time to level up. Learn platforms like BigQuery, Power BI, or Azure Synapse. They’re not just tools, they’re your edge in a fast-paced, data-first world. Stay sharp. Stay relevant. Stay real-time. #RealTimeAnalytics #TechTrends2025 #DataDriven #BusinessIntelligence #PowerBI #BigQuery #AzureSynapse #DigitalSkills #NigerianProfessionals

🔹 Simple View "One table. One view. Clean and simple. Your shortcut to focused data." 🔹 Complex View "When tables talk to each other—joins, subqueries, aggregations—Complex Views weave them into one story." 🔹 Materialized View (MV) "Why repeat the same heavy query? Materialized Views save the result, so you query speed instead of storage." 🔹 Partitioned View "Big data in slices, stitched together seamlessly. Partitioned Views let you query across partitions like a pro." 🔹 Indexed View "Views on steroids—Indexed Views bring indexing power for blazing fast reads on pre-joined, pre-aggregated data." 🔹 Temp View "Temporary by design, powerful in the moment. Temp Views live only for your session."

Good day, Everyone! 🚀 Fabric Series: Monitoring & Optimization in Fabric We’ve talked about ingestion, transformation, storage, visualization, and governance… But what comes next after building all of this? 👉 Optimization & Monitoring – the backbone of a reliable Fabric environment. Here are some best practices to keep in mind: 🔹 Performance Tuning – Optimize queries (SQL/DAX/KQL) to reduce refresh times – Use partitioning & incremental refresh for large datasets – Monitor refresh failures proactively 🔹 Resource Optimization – Right-size capacity (don’t overpay, don’t underpower) – Track usage to identify high-cost workloads – Leverage Fabric’s workload management tools 🔹 Data Refresh Strategy – Incremental refresh vs. full refresh – Schedule refreshes smartly (avoid peak business hours) 🔹 Health Monitoring – Use built-in Fabric monitoring & Power BI service metrics – Track refresh history, performance, and bottlenecks 🔹 Alerting & Automation – Set up alerts for failures, slow queries, or unusual activity – Automate responses where possible (notifications, retries) ✨ Remember: Fabric is not just about building insights. It’s about making sure those insights are reliable, fast, and cost-efficient. #MicrosoftFabric #DataEngineering #PowerBI #Optimization #DataStrategy #Analytics #ETL #SQL

💥 Dashboards breaking after every backend update? 🎯 Data teams drowning in last-minute fixes? It’s time to introduce #DataContracts, the modern way to align producers and consumers around clear expectations. In our latest article, we explain: ✅ What data contracts are and what they’re not ✅ How do they prevent chaos in fast-paced environments ✅ Who benefits and when to use them 📖 Please, read the full article on our blog: https://lnkd.in/d25qxsM2 #DataEngineering #ModernDataStack #DataQuality #Analytics

One of the things I enjoy most about working in data is how a small query tweak can completely change the insights you get. Recently, I’ve been practicing with advanced SQL functions they’re game changers for understanding customer behaviour, spotting trends, and simplifying retention analysis. It’s amazing how the right function at the right time can turn complex data into a clear story 📊✨ Curious—what’s your favourite SQL function that you can’t live without? #SQL #DataAnalytics #BusinessInsight #Snowflake

🌊 MICROSOFT FABRIC: WHY IT'S A GAME-CHANGER 🧠 Imagine: No more copying data from one tool to another. ✅ Everything is connected: Data Lake, Data Factory, Data Science, Power BI. ⚡ Context is preserved from end to end. No more information loss. ❓ In your opinion, what's the biggest win with a unified platform? #FutureTechThursday #MicrosoftFabric #DataPlatform #DataEngineering

💡Snowflake has its own Data Marketplace — where businesses can access live, ready-to-query datasets without copying, moving, or downloading anything. Think of it like an app store for data: 📊 Financial data from leading providers 🌦️ Weather and climate datasets 🛒 Retail and demographic insights 💉 Even healthcare and public data (like COVID-19 stats) No messy ETL. No stale exports. Just instant, governed access to live data streams you can plug straight into your analytics. For SMEs, that means you don’t need a giant budget or complex pipelines to enrich your business decisions — you just pick from the shelves and start querying. 👉 If you could pick one external dataset today to improve your business decisions, what would it be?

What Is a Data Cube? When data is spread across systems, comparing trends across time, regions, or products becomes a real challenge. That’s where data cubes come in, they provide a structured way to analyze large datasets from multiple perspectives. In this article on DataCamp, I explain data cubes, how they work, and why they’re still relevant in modern analytics. Article link: https://lnkd.in/dCjD4DCf #datacube