SQL Skills for Data Roles

Understanding SQL query execution order is fundamental to writing efficient and correct queries. Let me break down this crucial concept that many developers overlook. 𝗛𝗼𝘄 𝗪𝗲 𝗪𝗿𝗶𝘁𝗲 𝗦𝗤𝗟: 1. SELECT - Choose columns 2. FROM - Specify table 3. WHERE - Filter rows 4. GROUP BY - Group data 5. HAVING - Filter groups 6. ORDER BY - Sort results 7. LIMIT - Restrict rows 𝗕𝘂𝘁 𝗛𝗲𝗿𝗲'𝘀 𝗛𝗼𝘄 𝗦𝗤𝗟 𝗔𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗘𝘅𝗲𝗰𝘂𝘁𝗲𝘀: 1. FROM - First identifies the tables 2. WHERE - Filters individual rows 3. GROUP BY - Creates groups 4. HAVING - Filters groups 5. SELECT - Finally processes column selection 6. ORDER BY - Sorts the results 7. LIMIT - Caps the result set 𝗪𝗵𝘆 𝗧𝗵𝗶𝘀 𝗠𝗮𝘁𝘁𝗲𝗿𝘀: • Understanding this order helps debug query issues • Improves query optimization • Explains why some column aliases work in ORDER BY but not in WHERE • Critical for writing efficient subqueries • Essential for complex query planning 𝗣𝗿𝗼 𝗧𝗶𝗽𝘀: 1. Can't use column aliases in WHERE because SELECT executes after WHERE 2. HAVING requires GROUP BY (mostly) as it executes right after 3. Window functions process after SELECT phase 4. ORDER BY can use aliases as it executes after SELECT 𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗜𝗺𝗽𝗮𝗰𝘁: Understanding this execution order is crucial for: - Query Performance Optimization - Debugging Complex Queries - Writing Maintainable Code - Database Design Decisions - Handling Large Datasets ⚠️ Common Pitfalls: ```𝚜𝚚𝚕 𝚂𝙴𝙻𝙴𝙲𝚃 𝚎𝚖𝚙𝚕𝚘𝚢𝚎𝚎_𝚗𝚊𝚖𝚎, 𝙰𝚅𝙶(𝚜𝚊𝚕𝚊𝚛𝚢) 𝚊𝚜 𝚊𝚟𝚐_𝚜𝚊𝚕𝚊𝚛𝚢 𝙵𝚁𝙾𝙼 𝚎𝚖𝚙𝚕𝚘𝚢𝚎𝚎𝚜 𝚆𝙷𝙴𝚁𝙴 𝚊𝚟𝚐_𝚜𝚊𝚕𝚊𝚛𝚢 > 𝟻𝟶𝟶𝟶𝟶  -- 𝚃𝚑𝚒𝚜 𝚠𝚘𝚗'𝚝 𝚠𝚘𝚛𝚔! 𝙶𝚁𝙾𝚄𝙿 𝙱𝚈 𝚎𝚖𝚙𝚕𝚘𝚢𝚎𝚎_𝚗𝚊𝚖𝚎 ``` ✅ Correct Approach: ```𝚜𝚚𝚕 𝚂𝙴𝙻𝙴𝙲𝚃 𝚎𝚖𝚙𝚕𝚘𝚢𝚎𝚎_𝚗𝚊𝚖𝚎, 𝙰𝚅𝙶(𝚜𝚊𝚕𝚊𝚛𝚢) 𝚊𝚜 𝚊𝚟𝚐_𝚜𝚊𝚕𝚊𝚛𝚢 𝙵𝚁𝙾𝙼 𝚎𝚖𝚙𝚕𝚘𝚢𝚎𝚎𝚜 𝙶𝚁𝙾𝚄𝙿 𝙱𝚈 𝚎𝚖𝚙𝚕𝚘𝚢𝚎𝚎_𝚗𝚊𝚖𝚎 𝙷𝙰𝚅𝙸𝙽𝙶 𝙰𝚅𝙶(𝚜𝚊𝚕𝚊𝚛𝚢) > 𝟻𝟶𝟶𝟶𝟶  -- 𝚃𝚑𝚒𝚜 𝚠𝚘𝚛𝚔𝚜! ``` Next Steps: • Review your existing queries • Identify optimization opportunities • Refactor problematic queries • Share this knowledge with your team

SQL feels confusing when you try to learn everything at once. But most queries are built from the same few commands. SELECT. WHERE. ORDER BY. GROUP BY. Aggregate functions. JOIN. That’s it. These 6 commands carry most of the early work. 𝗦𝗘𝗟𝗘𝗖𝗧 helps you choose the columns you actually need. 𝗪𝗛𝗘𝗥𝗘 helps you filter out the rows that do not matter. 𝗢𝗥𝗗𝗘𝗥 𝗕𝗬 helps you sort the result so the important records are easier to see. 𝗚𝗥𝗢𝗨𝗣 𝗕𝗬 helps you turn rows into summaries. 𝗔𝗴𝗴𝗿𝗲𝗴𝗮𝘁𝗲 𝗳𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 help you calculate totals, averages, counts, minimums, and maximums. 𝗝𝗢𝗜𝗡𝘀 help you connect data from different tables. Nothing fancy at first. Just the basics. And honestly, that is where most people should spend more time. Because once these are clear, the logic of SQL starts to sink in. Then it feel less like code and more like asking structured questions. → What do I want to see? → Which rows matter? → How should the result be sorted? → What should be grouped? → What needs to be calculated? → Which tables need to be connected? That is the mindset. Not memorizing syntax for the sake of it. But learning how to pull the right answer from the right data. Pick one command. Write a small query. Break it. Fix it. Then move to the next one. That is how SQL starts to click. 💾 Save for later ♻️ Repost for the homies

Most people learn SQL like this: SELECT FROM WHERE GROUP BY ORDER BY But databases don’t execute queries in that order. And this small misunderstanding… is where a lot of confusion starts. What actually happens behind the scenes: FROM → data is picked JOIN → tables are combined WHERE → rows are filtered GROUP BY → data is grouped HAVING → groups are filtered SELECT → columns are selected ORDER BY → final sorting Why does this matter? Because once you understand execution order: • You stop writing inefficient queries • You understand why some filters don’t work • You debug faster • You avoid wrong aggregations For example: If you try to filter aggregated data using WHERE… it won’t work the way you expect. That’s where HAVING comes in. Not a syntax problem. A thinking problem. SQL is not just about writing queries. It’s about understanding how the database thinks If you’re learning SQL right now, don’t just memorize commands. Spend time understanding execution flow. That’s what actually changes your level. If you want more structured guidance or clarity in SQL and data concepts: https://lnkd.in/gWSkyyiv #SQL #DataAnalytics #DataScience

As data engineers, we often talk about scalability, performance, and automation — but there’s one thing that silently determines the success or failure of every pipeline: Data Quality. No matter how advanced your stack, if your data is inconsistent, incomplete, or inaccurate, your downstream dashboards, ML models, and decisions will all be compromised. Here’s a detailed list of 25 critical checks that every modern data engineer should implement 👇 🔹 1. Null or Missing Value Checks Ensure no essential field (like customer_id, transaction_id) contains missing data 🔹 2. Primary Key Uniqueness Validation Verify that key columns (like IDs) remain unique to prevent duplicate business entities or revenue double counting. 🔹 3. Duplicate Record Detection Detect duplicates across ingestion stages 🔹 4. Referential Integrity Validation Confirm that all foreign key relationships hold true 🔹 5. Data Type Validation Ensure incoming data matches schema definitions — no strings in numeric fields, no invalid dates. 🔹 6. Numeric Range Validation Catch impossible values (e.g., negative ages, >100% percentages, invalid ratings). 🔹 7. String Length & Pattern Checks Enforce length constraints and validate formats (emails, phone numbers, IDs) with regex rules. 🔹 8. Allowed Value / Domain Validation Ensure categorical columns only contain valid entries — e.g., gender ∈ {‘M’, ‘F’, ‘Other’}. 🔹 9. Business Rule Consistency Check rules like order_amount = item_price * quantity or revenue = sum(product_sales). 🔹 10. Cross-Column Consistency Validate logical dependencies — e.g., delivery_date ≥ order_date. 🔹 11. Timeliness / Freshness Checks Detect data delays and SLA breaches — especially important for near real-time systems. 🔹 12. Completeness Check Verify all partitions, expected files, or dates are present — no missing data slices. 🔹 13. Volume Check Against Historical Data Compare record counts or data sizes vs previous runs to detect anomalies in ingestion. 🔹 14. Statistical Distribution Checks Validate stability of metrics like mean, median, and standard deviation to catch silent drifts. 🔹 15. Outlier Detection Identify records that deviate significantly from normal ranges 🔹 16. Schema Drift Detection Automatically detect added, removed, or renamed columns — common in dynamic source systems. 🔹 17. Duplicate File Ingestion Check Prevent reprocessing of already-loaded files or data across multiple sources. 🔹 18. Negative / Invalid Value Checks Block impossible values like negative prices or zero quantities where not allowed. 🔹 19. Percentage / Total Consistency Check Ensure calculated percentages correctly sum to 100% or totals match constituent values. 🔹 20. Hierarchy Validation Validate hierarchical consistency. 🔹 21. Audit Column Consistency Confirm audit columns like created_by, updated_at, and load_date are properly populated. #DataEngineering #DataQuality #Databricks #ETL #DataPipelines #DataGovernance

Data quality isn't a single check, it's a lifecycle. 🔄 Most data pipelines struggle to guarantee quality because they lack end-to-end control. dlt bridges this gap by owning the entire runtime, from ingestion to staging to production. dlt ensures quality across 5 core dimensions: 1️⃣ Structural Integrity Does the data fit? dlt automatically normalizes column names and types to prevent SQL errors. For stricter control, use Schema Contracts to reject undocumented fields. 2️⃣ Semantic Validity Does it make business sense? Attach Pydantic models to your resources to enforce logic like "age > 0" or email validation in-stream. 3️⃣ Uniqueness & Relations Is the dataset consistent? Handle deduplication automatically using primary keys and merge dispositions. 4️⃣ Privacy & Governance Is the data safe? Hash PII or drop sensitive columns in-stream before they ever touch the disk. 5️⃣ Operational Health Is the pipeline reliable? Monitor volume metrics and set up alerts to catch schema drift the moment it happens. It’s time to move beyond simple "null checks" and treat data quality as a comprehensive lifecycle. Here are the docs to help you implement some of this: 📌 Alerting on Schema Changes: https://lnkd.in/d8dGX-2b 📌 Data Normalization & Type Management: https://lnkd.in/dsSr3CPf 🚀 Commercial Early Access: dltHub Data Quality Checks https://lnkd.in/dCjcug_F #DataEngineering #DataQuality #Python #dlt #DataGovernance #ETL #SchemaEvolution

Most data engineers focus on scalability, performance, and automation. But the real foundation of every reliable pipeline? Data Quality. You can build the most advanced data stack — but if your data is inconsistent or incomplete, everything on top of it breaks: → Dashboards become misleading → ML models lose accuracy → Business decisions go wrong So instead of only optimizing pipelines… start validating them. Here are some essential data quality checks every data engineer should implement: 🔹 Check for missing or null values in critical columns 🔹 Ensure primary keys remain unique 🔹 Identify duplicate records early in ingestion 🔹 Validate relationships between tables (foreign keys) 🔹 Enforce correct data types and formats 🔹 Catch out-of-range values (like negative prices or invalid percentages) 🔹 Apply business rules (e.g., revenue = price × quantity) 🔹 Validate dependencies between columns 🔹 Monitor data freshness and delays 🔹 Ensure completeness of partitions/files 🔹 Compare with historical data to detect anomalies 🔹 Track distribution changes (mean, median, etc.) 🔹 Detect outliers and unusual patterns 🔹 Handle schema changes proactively 🔹 Prevent duplicate file ingestion 🔹 Validate totals and percentages 🔹 Ensure audit columns are correctly populated 💡 Simple checks like these can prevent major downstream failures. In real-world data engineering, data quality is not a step — it’s a system. If your data isn’t trustworthy, nothing built on top of it will be. If you’re building pipelines, don’t just move data — make sure it’s reliable. Found this helpful? Repost it! 🔁 Follow Akash AB for Practical Data Engineering #dataengineering #dataquality #bigdata #etl #analytics #datascience

Just checking for NULL is not enough: If you want to validate your data and make sure it's good quality for downstream user's you need a more comprehensive approach. Validate these and more: ↳ Data ranges that make business sense ↳ Referential integrity between related tables ↳ Date formats and impossible date combinations ↳ String patterns and data type consistency ↳ Record counts and expected data volumes As someone who cares about the functioning of the business you should be guarding against downstream data that is dirty. Why comprehensive validation matters: ✅ Catches data quality issues before they reach dashboards ✅ Creates alerting opportunities for pipeline monitoring ✅ Prevents incorrect business decisions from bad data ✅ Makes your pipelines more resilient to source system changes Your source data isn't going to be clean. Build validation checks into all your SQL transformations. Start with these validation patterns and expand based on what you discover in your own data. What are some cool data validation checks you've built into your SQL models? 🔔 Follow me for more SQL and data engineering tips. ♻️ Repost if you think your network will benefit. #sql #dataengineering #dataanalytics

Most people start learning SQL by memorizing queries. I did the same. SELECT this   JOIN that   GROUP BY something  Run the query.   Get the output.   Move on. It feels like progress. But after a while, you notice something: You can write queries… but you struggle to explain them. And that’s exactly where interviews get difficult. Because SQL interviews are not about syntax. They are about clarity. What interviewers actually check: • Can you understand the data before writing the query?   • Can you decide when to use JOIN vs subquery?   • Can you handle duplicates, NULLs, and edge cases?   • Can you explain why your query works?  That’s the real gap. This is where structured notes like this help. Instead of jumping between random queries… you build understanding step by step. Starting from: • What is a database & DBMS   • How tables are created and structured   • How data is inserted, updated, deleted   • How SELECT actually works   • Filtering with WHERE   • Aggregations & GROUP BY   • Joins (inner, left, right, full, cross)   • Subqueries & views   • Transactions & indexes  Everything is explained in a very simple, handwritten way — which makes it easier to understand and revise quickly before interviews. You also get clarity on common mistakes people make: • Wrong number of values while inserting   • Confusion between WHERE and GROUP BY   • Not handling NULL values properly   • Misunderstanding joins   • Writing queries without understanding data  A simple way to use this: 1. Pick one concept   2. Write queries on your own   3. Explain each step   4. Fix where you get stuck  That’s how SQL improves. Not by memorizing more queries. But by understanding better. Save this so you can revisit it before interviews. Follow Sahil Hans for more!

SQL starts simple. But for data engineers, it quickly becomes a core system-building skill. You are not only writing queries. You are extracting data, joining systems, transforming records, designing tables, and optimizing pipelines at scale. Here are the 5 levels of SQL every data engineer should master: → 𝗟𝗲𝘃𝗲𝗹 𝟭: 𝗦𝗤𝗟 𝗕𝗮𝘀𝗶𝗰𝘀 This is where you learn to read, filter, and summarize data. Core commands: SELECT, FROM, WHERE, GROUP BY, HAVING, ORDER BY, LIMIT. Used for basic extraction, quick checks, reporting queries, and pipeline validation. → 𝗟𝗲𝘃𝗲𝗹 𝟮: 𝗝𝗼𝗶𝗻𝘀 This is where you connect data from multiple tables. Core joins: INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL OUTER JOIN, CROSS JOIN. Used to combine source systems and build warehouse-ready datasets. → 𝗟𝗲𝘃𝗲𝗹 𝟯: 𝗪𝗶𝗻𝗱𝗼𝘄 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 This is where SQL becomes powerful for analysis and transformations. Core concepts: PARTITION BY, ORDER BY, RANK, ROW_NUMBER, DENSE_RANK, LAG, LEAD. Used for deduplication, running totals, trend analysis, and clean transformation logic. → 𝗟𝗲𝘃𝗲𝗹 𝟰: 𝗦𝗤𝗟 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 This is where you learn how databases and tables are designed. Core commands: CREATE, ALTER, DROP, INSERT, UPDATE, DELETE, COMMIT, ROLLBACK. Used for staging tables, warehouse models, data marts, and reliable database structures. → 𝗟𝗲𝘃𝗲𝗹 𝟱: 𝗦𝗤𝗟 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 This is where you make SQL faster, cheaper, and more scalable. Core concepts: indexes, partitions, execution plans, table scans, query tuning, materialized views. Used to improve pipeline speed, reduce cloud cost, and keep large-scale data systems reliable. SQL is not just an interview topic. It is the language behind reliable data pipelines, analytics systems, and warehouse design. Save this if you are preparing for data engineering interviews. Follow Sumit Gupta for more such insights!!

It took me 10 years to learn about the different types of data quality checks; I'll teach it to you in 5 minutes: 1. Check table constraints The goal is to ensure your table's structure is what you expect: * Uniqueness * Not null * Enum check * Referential integrity Ensuring the table's constraints is an excellent way to cover your data quality base. 2. Check business criteria Work with the subject matter expert to understand what data users check for: * Min/Max permitted value * Order of events check * Data format check, e.g., check for the presence of the '$' symbol Business criteria catch data quality issues specific to your data/business. 3. Table schema checks Schema checks are to ensure that no inadvertent schema changes happened * Using incorrect transformation function leading to different data type * Upstream schema changes 4. Anomaly detection Metrics change over time; ensure it's not due to a bug. * Check percentage change of metrics over time * Use simple percentage change across runs * Use standard deviation checks to ensure values are within the "normal" range Detecting value deviations over time is critical for business metrics (revenue, etc.) 5. Data distribution checks Ensure your data size remains similar over time. * Ensure the row counts remain similar across days * Ensure critical segments of data remain similar in size over time Distribution checks ensure you get all the correct dates due to faulty joins/filters. 6. Reconciliation checks Check that your output has the same number of entities as your input. * Check that your output didn't lose data due to buggy code 7. Audit logs Log the number of rows input and output for each "transformation step" in your pipeline. * Having a log of the number of rows going in & coming out is crucial for debugging * Audit logs can also help you answer business questions Debugging data questions? Look at the audit log to see where data duplication/dropping happens. DQ warning levels: Make sure that your data quality checks are tagged with appropriate warning levels (e.g., INFO, DEBUG, WARN, ERROR, etc.). Based on the criticality of the check, you can block the pipeline. Get started with the business and constraint checks, adding more only as needed. Before you know it, your data quality will skyrocket! Good Luck! - Like this thread? Read about they types of data quality checks in detail here 👇 https://lnkd.in/eBdmNbKE Please let me know what you think in the comments below. Also, follow me for more actionable data content. #data #dataengineering #dataquality