Vivek S.

📊 Data Analytics Job Market 2025: Global Salary & Skills Benchmarking The data analytics field is experiencing unprecedented growth with a projected market value of $178.5 billion by 2025 (26.5% CAGR). Here's what my latest market analysis reveals: 🌍 Salary Benchmarking: USA vs India vs UAE ➡️ Entry Level (0-2 years): 🇺🇸 USA: $68,000 🇦🇪 UAE: $24,000 (AED 88k) 🇮🇳 India: $4,500 (₹5L) ➡️ Senior Level (5+ years): 🇺🇸 USA: $120,000 🇦🇪 UAE: $87,000 (AED 320k) 🇮🇳 India: $18,000 (₹15L) Key Finding: USA offers 3-15x higher compensation, while UAE provides competitive middle-ground salaries with tax advantages. 🎯 Top Skills in Demand (2025) Technical Must-Haves: ❶ SQL - 85% of job postings (15% salary premium) ❷ Python/R - 70% demand (25% salary premium) ❸ Data Visualization (Tableau, Power BI) - 68% demand (20% premium) ❹ Excel/Spreadsheets - 65% demand (10% premium) High-Value Specializations: ❶ Machine Learning - 45% demand but 30% salary premium 🚀 ❷ Data Warehousing - 40% demand (22% premium) Critical Soft Skills: ❶ Communication - 80% demand (bridge the gap between data and business) ❷ Critical Thinking - 75% demand (analytical problem-solving) 📈 Market Outlook 220,000+ new data roles expected in the US alone Skills gap crisis: 178k-234k shortfall in qualified professionals 36% job growth projected for data scientists (2023-2033) 70% of companies consider analytical thinking essential 💡 Career Strategy Tips Start with SQL - Most universal and in-demand skill Add ML specialization for highest salary premiums Don't ignore soft skills - Communication is as crucial as coding Consider geographic arbitrage - Remote opportunities from high-paying markets Bottom Line: The data analytics field offers exceptional career prospects, but success requires continuous upskilling in both technical and soft skills. What's your experience in the data analytics job market? Share your insights below! 👇 #DataAnalytics #CareerGrowth #SalaryBenchmarking #DataScience #TechCareers #JobMarket2025

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Sometimes you may be working with partitioned data in Spark and need to preserve folder structure. In this guide, Arun teaches you how to copy/export deeply nested partitions (year/month/day/hour) using various techniques. He also includes a step-by-step Scala example you can adapt to your own pipelines. https://lnkd.in/ecNp_SwR

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Databricks Performance Optimization: Speed That Saves Money In Databricks, performance isn’t just about faster queries — it’s about using compute wisely. When pipelines aren’t optimized, costs rise quietly in the background. So what actually makes a difference? 🔹 Smarter data layout Well-organized data means systems scan less and respond faster. 🔹 Balanced data distribution Too many splits slow things down. Too few limit growth. Getting the balance right improves both speed and scalability. 🔹 Reuse instead of reprocessing Data that’s accessed often should be reused, not recalculated every time — saving time and cost. 🔹 Right-sized compute Bigger infrastructure doesn’t always mean better performance. The right size delivers the same results at a lower cost. 📊 The business impact Faster data pipelines Lower cloud spend More stable performance as data grows Well-optimized platforms don’t just run better — they pay for themselves. 🚀 Fast data isn’t expensive data — when it’s engineered the right way. #Databricks #DataEngineering #CostOptimization #ScalableData #Lakehouse #CloudEfficiency

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SQL Server to Snowflake isn’t a migration. It’s a reset. When done right: - Queries drop from hours to seconds - Infra shifts to pay-as-you-use - Data becomes elastic and AI-ready Snowflake changes the game — but only with the right architecture and governance. Here’s the modernization playbook 👇 https://bit.ly/4kmSJrO #Kellton #EnterpriseAI #DataEngineering #Snowflake

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Announcing the most awaited Namaste Spark Live Bootcamp on NamasteSQL ⚡️🔥 Spark is no longer optional for data engineers and analytics engineers. But most people learn it in a very fragmented way. This live bootcamp is designed to help you understand Spark from first principles and then use it confidently in real projects. What we will cover 👇 • Big Data fundamentals and why Spark exists • Spark architecture and internals (driver, executors, cluster manager) • Spark UI deep dive – DAG, stages, tasks, shuffles and execution flow • RDD vs DataFrame vs Spark SQL (what to use and why) • PySpark API with hands-on coding • Joins, aggregations, window functions • Performance tuning and common mistakes • Delta lake fundamentals • Real-world ETL Projects And much more . This is not a crash course. This is for people who want clarity, depth, and practical skills. 📅 Live sessions starting Jan 3rd 2026 11am IST 💻 Hands-on + Q&A 🎯 Beginner friendly Prerequisites : Basic SQL and Python If you want to learn Spark the right way, this is for you. Check the curriculum from the link in the comments section . Use code EARLYSPARK for best price.

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💼 Job Hunt Learning Series (Day 2) Day 2 was all about bringing time awareness and business intelligence into PySpark workflows ⏱️ This time, I took up a subscription analytics use case — figuring out how to track active users, identify lapsed ones, and calculate engagement trends from time-series data. Here’s what I worked on 👇 🔹 Lag & Lead Functions — used `lag()` and `lead()` to compare each user’s current activity with their previous or next subscription period. Perfect for tracking churn or reactivation patterns. 🔹 Date-Based Insights — calculated gaps between consecutive events using `datediff()` and handled those tricky `"ongoing"` subscriptions gracefully by replacing them with `current_date()`. 🔹 Dynamic Flags for Active Users — used conditional logic with `when()` to tag users who were *still active* in a given time window. 🔹 Clean ETL Pipeline — joined `user_behavior` and `subscription` data, filtered for active subscribers, and aggregated total watch time — a real example of how media platforms measure engagement. 🔹 Bonus: Learned to combine `groupBy()` and `orderBy()` correctly for ranked summaries, and cast all date columns properly to avoid silent errors. Feels like I’ve added a solid dose of time-series logic to my PySpark toolkit today 🚀 What’s your take — what other time-based or analytical transformations should I explore next? Drop your thoughts below 👇 #PySpark #DataEngineering #ETL #BigData #ApacheSpark #LearningJourney #JobHunt #CodeWeekend

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Data is the backbone of many businesses today. The rise of data analytics is helping companies make smarter decisions. Businesses are using data to understand their customers better than ever. One key trend is the use of AI in data analysis. AI helps find patterns and insights faster than ever before. This means companies can act quickly and stay ahead of the competition. In fact, businesses using AI tools have seen a 20% increase in efficiency. This exciting trend shows how important AI is for growth. It’s vital for professionals to keep learning about these tools to stay relevant. Join a workshop or take an online course to sharpen your skills! What trends are you noticing in your industry?

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"Aadi, I am heading a D2C data team. How should I set up analytics to get the most output and impact from the small data team?" A friend asked me this as he joined a growing D2C brand last year. Here's the complete list of things I told him to do: 𝗚𝗲𝘁 𝘆𝗼𝘂𝗿 𝗱𝗮𝘁𝗮 𝗶𝗻 𝗼𝗻𝗲 𝗽𝗹𝗮𝗰𝗲.  • Google Tag Manager - With GTM, analysts can trigger events for the smallest user actions and changes happening on the site. It's easy. You can also get events from third-party services like Gokwik or the search widget. A good D2C setup can have 100-200 events.  • Data pipelines: Use tools like Fivetran or Airbyte to get data from your Shopify tables, ads reports, and any other source into your database. Must have all your data in one place.  • Data Warehouse (BigQuery): This is where the actual work happens - you store data, automate transformation, and run queries. BigQuery is just the easiest, cheapest and most obvious choice. Once you have the data from all your sources in one place, it's time to convert raw data into useful data models - or - 𝗗𝗮𝘁𝗮 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 The transformation layer includes everything between raw data and final outputs. This is where you get the ROI for your data investment. In D2C, you will be dealing with 3 types of data:  1. Events from GA4, webhooks, 3rd party services connecting  2. Fact tables that come as snapshots - Shopify orders  3. Aggregated data that updates over a few days: this is from Google and Meta Ads reports You have to clean this up, combine it, and create automated tables for the outputs you need. This is a ongoing process for any data team. I will share more details in a post this week. You can setup this layer using tools like dataform / dbt for managing the pipelines. Dataform is easier and enough for most shopify stores. Don't forget to create data sanity dashboards once you have data models. They build trust and give you peace of mind. 𝗙𝗶𝗻𝗮𝗹𝗹𝘆, 𝗺𝗮𝗸𝗲 𝗱𝗮𝘁𝗮 𝗮𝘃𝗮𝗶𝗹𝗮𝗯𝗹𝗲 𝘁𝗼 𝗽𝗲𝗼𝗽𝗹𝗲 𝘄𝗵𝗲𝗿𝗲 𝘁𝗵𝗲𝘆 𝗻𝗲𝗲𝗱 𝗶𝘁.  • Send daily slack and gmail reports.   • Create automated weekly reviews in spreadsheets  • Create Dashboards with filters that stakeholders need That's all! Do this and you have a best in class data setup. I will get into more detail for each of these steps in my posts this week and share resources to make this less painful. 🔔 Turn on post notifications if you don't want to miss them.

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AI won't replace analysts. But analysts who use AI as a thinking partner will replace those who don't. Three of the sharpest data people I know just launched a course to help you become that analyst. Sravya (Senior Manager, Data Science @ Grammarly), Hai Guan (Head of Data @ Ontra), and Shane Butler (Principal Data Scientist @ Ontra) created AI Analytics for Builders on Maven. 𝐖𝐡𝐚𝐭 𝐭𝐡𝐞 𝐜𝐨𝐮𝐫𝐬𝐞 𝐭𝐞𝐚𝐜𝐡𝐞𝐬: → Translate vague product questions into clear, testable hypotheses → Define primary, secondary, and guardrail metrics with AI → Break down KPI movements through segmentation and trend analysis → Pressure-test your assumptions and edge cases → Turn analysis into clear, actionable recommendations 𝐓𝐡𝐞 𝐝𝐞𝐚𝐥: 🔥 5 early bird spots at $180 (normally $1,200) 🎟️ Everyone else who applies gets 40% off ⏰ Applications close in 24 hours I've worked with these folks. They're sharp, practical, and actually care about teaching skills that stick. If you're in data, product, or analytics, this is worth a look. 👉 𝐀𝐩𝐩𝐥𝐲 𝐡𝐞𝐫𝐞: https://lnkd.in/g7AHwSYY ♻️ Repost to help someone in your network level up their analytics game

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Attention Aspiring #Data_Analysts! Meesho Data Analyst Interview Experience (0–2 Years): 1. Write a SQL query to find the top 5 products with the highest number of orders in the last 30 days. 2. Identify users who placed orders from more than 3 different PIN codes. 3. Find products that never got a return request. 4. Calculate the average basket size (items per order) for each user in the last 2 months. 5. Retrieve users who placed more than 1 order on the same day at least 3 times. 6. Write a query to get the most ordered product for each category. 7. Find sellers whose cancellation rate exceeds 10%. 8. Write a query to get the first and last order date for each user. 9. Identify users who placed orders in all 4 weeks of the last month. 10. Find products with a return rate lower than the average return rate in their category. ⸻ Answers Solution 1: SELECT product_id, COUNT(*) AS total_orders FROM orders WHERE order_date >= CURRENT_DATE - INTERVAL ‘30 days’ GROUP BY product_id ORDER BY total_orders DESC LIMIT 5; Solution 2: SELECT user_id FROM orders GROUP BY user_id HAVING COUNT(DISTINCT pin_code) > 3; Solution 3: SELECT product_id FROM products WHERE product_id NOT IN ( SELECT DISTINCT product_id FROM returns ); Solution 4: SELECT user_id, AVG(item_count) AS avg_basket_size FROM orders WHERE order_date >= CURRENT_DATE - INTERVAL ‘2 months’ GROUP BY user_id; Solution 5: SELECT user_id FROM ( SELECT user_id, order_date, COUNT() AS orders_in_day FROM orders GROUP BY user_id, order_date HAVING COUNT() > 1 ) t GROUP BY user_id HAVING COUNT(*) >= 3; Solution 6: SELECT category, product_id FROM ( SELECT category, product_id, COUNT() AS order_count, RANK() OVER (PARTITION BY category ORDER BY COUNT() DESC) AS rnk FROM orders o JOIN products p ON o.product_id = p.product_id GROUP BY category, product_id ) t WHERE rnk = 1; Solution 7: SELECT seller_id FROM orders GROUP BY seller_id HAVING SUM(CASE WHEN status = ‘Cancelled’ THEN 1 ELSE 0 END) * 1.0 / COUNT(*) > 0.1; Solution 8: SELECT user_id, MIN(order_date) AS first_order, MAX(order_date) AS latest_order FROM orders GROUP BY user_id; Solution 9: SELECT user_id FROM orders WHERE order_date >= DATE_TRUNC(‘month’, CURRENT_DATE) GROUP BY user_id, WEEK(order_date) HAVING COUNT(*) >= 1 GROUP BY user_id HAVING COUNT(DISTINCT WEEK(order_date)) = 4; Solution 10: SELECT product_id FROM products p JOIN ( SELECT category, AVG(return_rate) AS avg_cat_return FROM products GROUP BY category ) c ON p.category = c.category WHERE p.return_rate < c.avg_cat_return; Follow Mohit Nath for more..

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