Paresh P.’s Post

🚀 SQL — The Core Engine of Data Engineering No matter how advanced our data stacks get — Databricks, Snowflake, or BigQuery — one language continues to power it all: SQL. Here are 3 essential SQL practices every Data Engineer should master 👇 🔹 Use CTEs (Common Table Expressions) Make transformations modular and easier to debug. They improve readability and maintainability. 🔹 Leverage Window Functions Perfect for ranking, time-series analysis, and deduplication without losing row-level granularity. 🔹 Profile and Optimize Queries Always inspect execution plans before production. Push filters early and select only the columns you need — it saves cost and time. 💡 Efficiency in SQL isn’t about writing shorter queries — it’s about designing smarter logic and reducing scan costs. SQL remains the bridge between data pipelines, performance, and precision — mastering it is what separates a good data engineer from a great one. #DataEngineering #SQL #ETL #BigData #Databricks #Snowflake #QueryOptimization #CTE #WindowFunctions #DataPipelines

🚀 The Most Underrated Performance Booster in Data Modeling: Indexing When we think about data modeling, our focus usually goes toward schema design, fact/dimension tables, or optimizing pipelines. But one thing that silently determines whether your queries run in seconds vs. minutes is indexing. 🔍 What is Indexing? Think of an index as a shortcut. Instead of scanning millions of rows to find what you need, the database uses the index to jump directly to the result - just like using the index page of a book. ✅ Why Indexing Matters Here’s how indexing impacts query performance in data models: ✨ Speeds up search and filtering (WHERE, GROUP BY, ORDER BY) ✨ Improves joins between Fact and Dimension tables ✨ Boosts dashboard performance for BI tools ✨ Reduces compute cost by avoiding unnecessary scans Indexes are especially helpful when your dataset transitions from thousands → millions → billions of rows. 📌 Choose the right index: Clustered Index: Sorting & storing data physically (usually on PKs) Non-Clustered Index: Fast filtering/search on frequently queried columns Columnstore Index: Analytical workloads with aggregations (Synapse, Snowflake, Databricks SQL) Indexing isn’t just a database task; it’s a data modeling strategy. Too few indexes = slow reads Too many indexes = slow writes #DataEngineering #Indexing #DataModeling #SQL #Databases #AzureData #Snowflake #BigData #PerformanceOptimization

𝐒𝐐𝐋 𝐯𝐬 𝐏𝐲𝐒𝐩𝐚𝐫𝐤 — Bridging the Gap Between Two Worlds ⚡ If you’ve ever switched between SQL and PySpark, you know the struggle — “Wait, what’s the PySpark equivalent of a CTE?” “Or how do I write a GROUP BY with multiple aggregations?” 😅 To make that transition seamless, I’ve created a 𝐜𝐨𝐦𝐩𝐫𝐞𝐡𝐞𝐧𝐬𝐢𝐯𝐞 𝐒𝐐𝐋 ↔ 𝐏𝐲𝐒𝐩𝐚𝐫𝐤 𝐄𝐪𝐮𝐢𝐯𝐚𝐥𝐞𝐧𝐜𝐞 𝐆𝐮𝐢𝐝𝐞 📘 It covers everything you need to translate SQL logic into PySpark code: 🔹 Data Types Mapping – INT → IntegerType(), DATE → DateType() 🔹 Database & Table Operations – CREATE, DROP, SHOW, DESCRIBE 🔹 Transformations – Filtering, Aggregations, Joins, Pivots 🔹 Window Functions – RANK, DENSE_RANK, LEAD, LAG 🔹 Conditional Logic – IF, COALESCE, CASE WHEN 🔹 Performance & Partitioning – Bucketing, Caching, and File Writes Each SQL command is paired side-by-side with its PySpark equivalent — making this your go-to resource for hybrid data environments 🚀 📌 Save this post & grab the guide — it’s perfect for anyone moving from SQL to PySpark or working on Databricks and big data pipelines. ⏩ 𝐉𝐨𝐢𝐧 𝐭𝐨 𝐥𝐞𝐚𝐫𝐧 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 & 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬: https://t.me/LK_Data_world 💬 If you found this PDF useful, like, save, and repost it to help others in the community! 🔄 📢 Connect with Lovee Kumar 🔔 for more content on Data Engineering, Analytics, and Big Data. #PySpark #SQL #DataEngineering #BigData #Databricks #ETL #SparkSQL #data #DataScience

💡 Using Aliases in Data Pipelines: Bridging Technical Logic and Business Meaning While building data pipelines in Databricks, there are times when it becomes necessary to assign temporary names (aliases) to certain columns. This isn’t always due to technical errors; rather, it ensures that the data structure aligns with business logic and stakeholder understanding. ✅ For example, a column in production data might be named “uuid”, but renaming it temporarily to a more descriptive name can make reports and transformations much clearer from a business perspective. ✅ Aliases allow us to assign meaningful names to columns without altering the original schema, helping both engineers and analysts interpret data consistently. In my latest write-up, I demonstrate how to implement aliases in SQL, PySpark, and Spark SQL, showing how knowledge can seamlessly transfer across different codebases and technologies. Keep learning, keep improving, and remember to always read documentation. Knowledge transfer from one code base to the other shouldn't be difficult. #DataEngineering #Databricks #PySpark #SQL #BigData #DataAnalytics #DataPipelines #ETL #AnalyticsInstitute #DataTransformation #BusinessIntelligence #TechEducation

SQL vs PySpark — Bridging the Gap Between Two Data Worlds ⚡ If you’ve ever bounced between SQL and PySpark, you know the feeling: 👉 “What’s the PySpark version of a CTE?” 👉 “How do I do multi-column aggregation again?” 😅 To make that shift effortless, I’ve created a complete SQL ⇆ PySpark translation guide — helping you move between syntax and thinking patterns with ease. Here’s what’s inside 👇 ✅ Data Type Mapping — e.g., INT → IntegerType(), DATE → DateType() ✅ Database & Table Commands — CREATE, DROP, SHOW, DESCRIBE ✅ Core Transformations — filters, joins, group-bys, aggregates, pivots ✅ Window Functions — RANK, DENSE_RANK, LAG, LEAD ✅ Conditional Logic — CASE WHEN, IF, COALESCE ✅ Performance Essentials — caching, bucketing, partitioning, file writes Every SQL snippet is paired side-by-side with the PySpark equivalent — a perfect companion for Big Data projects, Databricks workflows, or anyone transitioning from SQL to PySpark 🚀 📌 Save this post and grab the guide — it’s your new go-to reference for hybrid data environments. If this cheat sheet helps you, like, save, and share to support the data community 🔄✨ — 📢 Connect with Leonard Solace for more insights on Data Engineering, Big Data, and Analytics! #PySpark #SQL #DataEngineering #BigData #LeonardSolace #Databricks #ETL #SparkSQL #DataScience #Data

I wanted to explore how dbt macros make analytics workflows modular and reusable — and how this fits perfectly with Snowflake’s scalable data warehouse. 🧩 How It Works 1️⃣ Raw data (users + events) is stored in Snowflake. 2️⃣ dbt connects to Snowflake and runs SQL models that clean, join, and transform this data. 3️⃣ I created a macro in dbt that defines what an “active user” means — for example, users who logged in within the last 30 days: {% macro active_users(days=30) %} SELECT * FROM {{ ref('users') }} WHERE DATEDIFF('day', last_login_date, CURRENT_DATE()) <= {{ days }} {% endmacro %} 4️⃣ I can now reuse this macro across multiple models — for 7-day, 30-day, or 90-day activity — with just one line of code: {{ active_users(30) }} 5️⃣ The results are stored back into Snowflake as tables or views, which feed directly into my dashboard. ⚙️ Why This Matters - 💡 Macros keep your SQL logic consistent and reusable — one change updates every dependent model. 💡 Snowflake handles all the heavy lifting for storage and computation, so dbt focuses purely on transformation logic. 💡 Together, they create a clean, production-style data stack — perfect for analytics engineering projects. 🧠 Tools Used: dbt, Snowflake, SQL, Jinja 📊 Key Learning: dbt macros + Snowflake = reusable, scalable, and maintainable data transformations. #dbt #snowflake #dataengineering #sql #analyticsengineering #learning #dataproject #career

🚀 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝗩𝗶𝗲𝘄𝘀 𝗶𝗻 𝗗𝗮𝘁𝗮𝗯𝗿𝗶𝗰𝗸𝘀 𝗠𝗮𝗱𝗲 𝗦𝗶𝗺𝗽𝗹𝗲! In Databricks, Views are like virtual tables — they don’t store data but show live results from your SQL queries every time you run them. 💡 Let’s break it down 👇 🧩 1️⃣ 𝗧𝗲𝗺𝗽𝗼𝗿𝗮𝗿𝘆 𝗩𝗶𝗲𝘄 ✅ Created for your 𝘤𝘶𝘳𝘳𝘦𝘯𝘵 𝘯𝘰𝘵𝘦𝘣𝘰𝘰𝘬 or 𝘚𝘱𝘢𝘳𝘬 𝘴𝘦𝘴𝘴𝘪𝘰𝘯 only. 🕒 𝙇𝙞𝙛𝙚𝙩𝙞𝙢𝙚: Ends when the notebook or cluster session ends. 📍 𝙐𝙨𝙚 𝙒𝙝𝙚𝙣: You just need quick, temporary transformations during exploration or ETL testing. 𝘊𝘙𝘌𝘈𝘛𝘌 𝘖𝘙 𝘙𝘌𝘗𝘓𝘈𝘊𝘌 𝘛𝘌𝘔𝘗 𝘝𝘐𝘌𝘞 𝘵𝘦𝘮𝘱_𝘴𝘢𝘭𝘦𝘴 𝘈𝘚 𝘚𝘌𝘓𝘌𝘊𝘛 * 𝘍𝘙𝘖𝘔 𝘴𝘢𝘭𝘦𝘴 𝘞𝘏𝘌𝘙𝘌 𝘢𝘮𝘰𝘶𝘯𝘵 > 1000; 🌐 2️⃣ 𝗚𝗹𝗼𝗯𝗮𝗹 𝗧𝗲𝗺𝗽𝗼𝗿𝗮𝗿𝘆 𝗩𝗶𝗲𝘄 🌍 Shared across all notebooks and sessions within the same cluster. 🕒 𝙇𝙞𝙛𝙚𝙩𝙞𝙢𝙚: Ends when the cluster stops. 📍 𝙐𝙨𝙚 𝙒𝙝𝙚𝙣: You want multiple notebooks/jobs to use the same temporary logic. 𝘊𝘙𝘌𝘈𝘛𝘌 𝘖𝘙 𝘙𝘌𝘗𝘓𝘈𝘊𝘌 𝘎𝘓𝘖𝘉𝘈𝘓 𝘛𝘌𝘔𝘗 𝘝𝘐𝘌𝘞 𝘨𝘭𝘰𝘣𝘢𝘭_𝘤𝘶𝘴𝘵𝘰𝘮𝘦𝘳𝘴 𝘈𝘚 𝘚𝘌𝘓𝘌𝘊𝘛 * 𝘍𝘙𝘖𝘔 𝘤𝘶𝘴𝘵𝘰𝘮𝘦𝘳𝘴 𝘞𝘏𝘌𝘙𝘌 𝘴𝘵𝘢𝘵𝘶𝘴 = '𝘢𝘤𝘵𝘪𝘷𝘦'; 🏛️ 3️⃣ 𝗣𝗲𝗿𝘀𝗶𝘀𝘁𝗲𝗻𝘁 (𝗣𝗲𝗿𝗺𝗮𝗻𝗲𝗻𝘁) 𝗩𝗶𝗲𝘄 💾 Stored permanently in the Unity Catalog or Hive Metastore. 🕒 𝙇𝙞𝙛𝙚𝙩𝙞𝙢𝙚: Until it’s manually dropped. 📍 𝙐𝙨𝙚 𝙒𝙝𝙚𝙣: You need reusable, production-level logic across dashboards, notebooks, and pipelines. 𝘊𝘙𝘌𝘈𝘛𝘌 𝘖𝘙 𝘙𝘌𝘗𝘓𝘈𝘊𝘌 𝘝𝘐𝘌𝘞 𝘧𝘪𝘯𝘢𝘯𝘤𝘦_𝘥𝘣.𝘮𝘰𝘯𝘵𝘩𝘭𝘺_𝘴𝘶𝘮𝘮𝘢𝘳𝘺 𝘈𝘚 𝘚𝘌𝘓𝘌𝘊𝘛 𝘮𝘰𝘯𝘵𝘩, 𝘚𝘜𝘔(𝘳𝘦𝘷𝘦𝘯𝘶𝘦) 𝘍𝘙𝘖𝘔 𝘧𝘪𝘯𝘢𝘯𝘤𝘦_𝘥𝘣.𝘵𝘳𝘢𝘯𝘴𝘢𝘤𝘵𝘪𝘰𝘯𝘴 𝘎𝘙𝘖𝘜𝘗 𝘉𝘠 𝘮𝘰𝘯𝘵𝘩; 💡 𝗧𝗶𝗽:   • Use 𝗧𝗲𝗺𝗽 𝗩𝗶𝗲𝘄𝘀 for short-lived transformations.   • Use 𝗚𝗹𝗼𝗯𝗮𝗹 𝗧𝗲𝗺𝗽 𝗩𝗶𝗲𝘄𝘀 for shared, temporary logic.   • Use 𝗣𝗲𝗿𝘀𝗶𝘀𝘁𝗲𝗻𝘁 𝗩𝗶𝗲𝘄𝘀 for stable, reusable, governed logic in production. #Databricks #DataEngineering #SparkSQL #DataAnalytics #BigData #ETL #SQL #CloudData #DataPipeline #DataGovernance #Views #Temp #Global #Persistent #Query #UnityCatalog

💡 Implementing UUIDs in Data Warehouse Design with Databricks When designing a data warehouse architecture following the Star Schema model, it’s essential to distinguish between the database primary key and the record identifier. ✅ To achieve this, it’s best practice to create a Universally Unique Identifier (UUID) for each record. ✅ A UUID is a 128-bit value that uniquely identifies information across systems. In SQL, it’s typically represented as a 36-character alphanumeric string, formatted into five groups separated by hyphens. ✅ Using UUIDs provides a clean separation of data logic and enhances the flexibility and scalability of your warehouse design, especially in Star or Snowflake Schemas. In this post, I demonstrate how to implement UUIDs in Standard SQL, PySpark, and Spark SQL within Databricks. Keep learning, keep improving, and remember to always read documentation. Kindly share which is your preferred approach in the comment section. #Databricks #PySpark #SparkSQL #DataEngineering #BigData #Analytics #SQL #DataProducts #DataAnalytics #ETL #ELT

Databricks asked these SQL questions in a Data Engineering interview ( 2+ YOE ) 1.⁠ ⁠Write a SQL query using Delta tables to compute cumulative revenue per day 2.⁠ ⁠Retrieve the first and last login per user from a huge event log table 3.⁠ ⁠Find the top 3 customers by spend in each region (use window functions) 4.⁠ ⁠Detect duplicate records in a transactional table and delete extras safely 5.⁠ ⁠Get users who made purchases in 3 consecutive months 6.⁠ ⁠Identify skewed joins in SQL and propose fixes using broadcast hints 7.⁠ ⁠Compute a 7-day moving average of product sales on Delta Lake 8.⁠ ⁠Pivot daily sales into month-wise columns 9.⁠ ⁠Find customers who bought products every month in a year 10.⁠ ⁠Rank products by sales per year, resetting rank each year 11.⁠ ⁠Find employees earning more than their department average 12.⁠ ⁠Find the median transaction amount (no built-in median) 13.⁠ ⁠Get all users who placed their first order in the last 30 days 14.⁠ ⁠Compare price change between two dates for each product 15.⁠ ⁠Identify customers whose first and last transaction is on the same day 16.⁠ ⁠Calculate the percentage of returning users for each month 17.⁠ ⁠Retrieve products that have never been sold 18.⁠ ⁠Detect schema drift in historical Delta data snapshots 19.⁠ ⁠Find departments where at least 2 employees have identical salaries 20.⁠ ⁠Group users by login streaks of 3+ consecutive days #dataengineeringirl #databricks SQL Questions with video solution :- https://lnkd.in/efC6pTr4

I’ve published all 20 questions (with detailed explanations) on Medium to help others prepare effectively. 💻 Check it out here 👉 https://lnkd.in/g-UVKVsq