Key SQL Techniques for Data Analysts

Let's talk about 𝐒𝐐𝐋 concepts that not only help in interviews but also make your day-to-day job as a Data Analyst easier. In my experience of facing multiple interviews and working with SQL daily, I've found a few concepts extremely valuable in real-world analytics: 𝐂𝐨𝐦𝐦𝐨𝐧 𝐓𝐚𝐛𝐥𝐞 𝐄𝐱𝐩𝐫𝐞𝐬𝐬𝐢𝐨𝐧𝐬 (𝐂𝐓𝐄𝐬) These help simplify complex queries by breaking them into manageable parts. It makes your query readable and easy to maintain, especially when you're working in teams or on large projects. 𝐖𝐢𝐧𝐝𝐨𝐰 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧𝐬 (𝐑𝐎𝐖_𝐍𝐔𝐌𝐁𝐄𝐑, 𝐑𝐀𝐍𝐊, 𝐃𝐄𝐍𝐒𝐄_𝐑𝐀𝐍𝐊, 𝐋𝐄𝐀𝐃, 𝐋𝐀𝐆) These are game-changers. Instead of writing multiple subqueries, you can easily perform ranking, find running totals, compare rows, and calculate moving averages with one simple statement. 𝐒𝐮𝐛𝐪𝐮𝐞𝐫𝐢𝐞𝐬 (𝐍𝐞𝐬𝐭𝐞𝐝 𝐐𝐮𝐞𝐫𝐢𝐞𝐬) Subqueries allow you to perform complex operations step-by-step. They are great for scenarios where you need results from multiple queries combined into one. 𝐈𝐧𝐝𝐞𝐱𝐞𝐬 & 𝐐𝐮𝐞𝐫𝐲 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 Understanding indexing helps your queries run faster. For instance, creating an index on columns frequently used in JOINs, WHERE, or GROUP BY clauses drastically improves performance, especially in large tables. 𝐉𝐨𝐢𝐧𝐬 𝐯𝐬. 𝐒𝐮𝐛𝐪𝐮𝐞𝐫𝐢𝐞𝐬 (𝐖𝐡𝐞𝐧 𝐭𝐨 𝐔𝐬𝐞 𝐖𝐡𝐚𝐭) Many of us get confused about using joins or subqueries. Typically, JOINs are more efficient for large datasets, while subqueries can be simpler to write for smaller or one-time analyses. 𝐂𝐀𝐒𝐄 𝐒𝐭𝐚𝐭𝐞𝐦𝐞𝐧𝐭𝐬 𝐟𝐨𝐫 𝐂𝐨𝐧𝐝𝐢𝐭𝐢𝐨𝐧𝐚𝐥 𝐋𝐨𝐠𝐢𝐜 These are useful for categorizing your data without using multiple queries. A single CASE statement can simplify your logic and save processing time. 𝐀𝐠𝐠𝐫𝐞𝐠𝐚𝐭𝐢𝐨𝐧𝐬 & 𝐆𝐫𝐨𝐮𝐩𝐢𝐧𝐠𝐬 You should know how to effectively use GROUP BY along with aggregate functions like COUNT, SUM, AVG, MAX, MIN. Grouping data properly is fundamental to answering most analytical questions. 𝐃𝐚𝐭𝐞 & 𝐓𝐢𝐦𝐞 𝐌𝐚𝐧𝐢𝐩𝐮𝐥𝐚𝐭𝐢𝐨𝐧𝐬 Real analytics problems often involve time series data. Learn functions like DATE_TRUNC, DATE_PART, DATE_DIFF, DATE_ADD, and DATE_FORMAT to handle date-time data effectively. 𝐒𝐞𝐥𝐟-𝐉𝐨𝐢𝐧𝐬 & 𝐑𝐞𝐜𝐮𝐫𝐬𝐢𝐯𝐞 𝐐𝐮𝐞𝐫𝐢𝐞𝐬 Not all data lives neatly in one table. Self-joins help you analyze hierarchical data like employee-manager relationships or user referral systems. 𝐇𝐚𝐧𝐝𝐥𝐢𝐧𝐠 𝐃𝐮𝐩𝐥𝐢𝐜𝐚𝐭𝐞𝐬 𝐚𝐧𝐝 𝐃𝐚𝐭𝐚 𝐈𝐧𝐭𝐞𝐠𝐫𝐢𝐭𝐲 Knowing how to identify and remove duplicate records using ROW_NUMBER() or DISTINCT ensures accurate and reliable analysis. SQL isn't just about writing queries; it's about efficiency, readability, and solving real business problems. The above topics cover essential areas that have personally helped me improve my productivity and provided great value during interviews. Did I miss any important topic? Drop your suggestions below. Follow Shakra Shamim for more such posts.!

SQL sits at the heart of every data pipeline - from ingestion and transformation to analytics and reporting. This guide brings together 25 practical SQL patterns that show up again and again in real-world data engineering work: filtering recent data, handling nulls, joining tables, deduplicating records, ranking results, building rolling metrics, detecting gaps, and implementing incremental loads and upserts. You’ll find patterns like: • Time-based filters (Last N days, date ranges, current month/week) • Core joins (inner, left, anti, semi) • Aggregations with GROUP BY and HAVING • Window functions for ranking, running totals, rolling averages, LAG/LEAD • Deduplication and duplicate detection • Top-N per group queries • Pivot-style aggregations • Incremental load filters • Upsert / MERGE patterns for warehouse pipelines These are the exact building blocks used in ETL/ELT pipelines, analytics models, and production warehouses - whether you’re working with Snowflake, BigQuery, Redshift, Postgres, or Spark SQL. If you’re building data pipelines, maintaining warehouses, or writing transformations daily, mastering these patterns saves hours of debugging and makes your SQL far more production-ready. Save this for reference, it’s a solid checklist of SQL techniques every data engineer ends up using in practice

Essential SQL Topics for Data Analysts: A Statistical Perspective  1. Aggregations and Statistical Functions    Basic Aggregation Functions: COUNT(), SUM(), AVG(), MAX(), MIN()    Advanced Statistical Functions: Functions like STDEV(), VARIANCE(), and MEDIAN() (though MEDIAN() is not natively supported in some SQL databases).      Examples:        Mean (Average): SELECT AVG(column_name) FROM table_name;        Standard Deviation: SELECT STDEV(column_name) FROM table_name;        Variance: SELECT VARIANCE(column_name) FROM table_name;     2. Percentiles and Quartiles    Calculating percentiles and quartiles to understand the distribution of data is important for outlier detection and other insights.    In SQL, this is often done using PERCENTILE_CONT() or NTILE() functions.      Example:       SELECT PERCENTILE_CONT(0.25) WITHIN GROUP (ORDER BY column_name) AS first_quartile       FROM table_name;        3. Window Functions    Window Functions like ROW_NUMBER(), RANK(), DENSE_RANK(), and NTILE() are essential for performing statistical analysis over specific windows of data.    They allow you to calculate running totals, cumulative averages, and perform ranking within partitions.      Example: Calculating a running total or moving average.       SELECT column_name,              SUM(column_name) OVER (ORDER BY column_name) AS running_total       FROM table_name;        4. Group By and Having Clauses    Using GROUP BY for grouping data into different categories and then applying aggregate functions to each group is crucial for statistical summary.    The HAVING clause helps in filtering data based on aggregate conditions.      Example:       SELECT department, AVG(salary)       FROM employees       GROUP BY department       HAVING AVG(salary) > 50000;        5. Correlation and Covariance    To identify relationships between variables, understanding correlation and covariance is important. SQL supports this via the functions CORR() and COVAR_POP().      Example: Checking the correlation between two columns:       SELECT CORR(column1, column2)       FROM table_name;        6. Hypothesis Testing    Although more complex hypothesis testing is usually done outside SQL (e.g., in Python or R), basic tests like chisquare or ztest can be approximated using SQL logic and aggregation functions.    For instance, contingency tables can be built using GROUP BY and COUNT().  7. Outlier Detection    Detecting outliers using statistical measures such as standard deviations from the mean or interquartile range (IQR) can be done using SQL queries.      Example: Detecting outliers based on mean and standard deviation.             SELECT *       FROM table_name       WHERE column_name > (SELECT AVG(column_name) + 3 * STDEV(column_name) FROM table_name);       Follow more for Priyanka SG #DataAnalyst #SQLServer #Excel #PowerBI #Python #DataVisualization

Key SQL Skills to Revise a Day Before Your Data Analyst Interview: 1. SELECT Statements: Retrieve data from one or more tables using basic SELECT queries. 2. WHERE Clause: Filter data based on specific conditions to refine query results. 3. JOINs (INNER, LEFT, RIGHT, FULL): Combine data from multiple tables using various types of joins. 4. GROUP BY: Group rows of data based on a specific column for aggregation. 5. HAVING Clause: Filter aggregated data after using GROUP BY. 6. ORDER BY: Sort results by one or more columns, in ascending or descending order. 7. DISTINCT: Retrieve unique values from a column to eliminate duplicates. 8. COUNT, SUM, AVG, MIN, MAX: Use aggregate functions to calculate metrics such as count, sum, average, etc. 9. CASE Statements: Perform conditional logic directly within queries to return specific values based on criteria. 10. Subqueries: Use nested queries within SELECT, WHERE, or FROM clauses for more complex data retrieval. 11. UNION and UNION ALL: Combine results from multiple queries into a single result set (with or without duplicates). 12. Aliases (AS): Rename columns or tables temporarily for easier readability. 13. DISTINCT vs GROUP BY: Understand the difference and when to use each to eliminate duplicates or aggregate data. 14. Indexes: Understand how indexing can optimize query performance by reducing lookup times. 15. Normalization & Denormalization: Grasp database design concepts for optimizing structure and queries. 16. Transactions (BEGIN, COMMIT, ROLLBACK): Manage data consistency and handle errors in database operations. 17. Constraints (PRIMARY KEY, FOREIGN KEY, UNIQUE): Enforce rules to maintain data integrity. 18. LIKE, IN Use these operators to filter data with flexible conditions. 19. ALTER, CREATE, DROP: Modify the database structure by adding, changing, or deleting tables and columns. 20. Triggers and Stored Procedures: Automate tasks and create reusable SQL scripts for frequent operations. 21. Normalization Levels (1NF, 2NF, 3NF): Understand database normalization to reduce redundancy and improve data integrity. 22. Window Functions (ROW_NUMBER, RANK, PARTITION BY): Perform advanced calculations across rows in a query result. 23. Date Functions (NOW, DATEADD, DATEDIFF, YEAR, MONTH, etc.): Manipulate and calculate date and time values in queries. #SQL #dataanalyst

𝗧𝗵𝗶𝗻𝗸 𝗬𝗼𝘂’𝘃𝗲 𝗠𝗮𝘀𝘁𝗲𝗿𝗲𝗱 𝗦𝗤𝗟? 𝗠𝗼𝘀𝘁 𝗣𝗲𝗼𝗽𝗹𝗲 𝗦𝘁𝗼𝗽 𝗧𝗼𝗼 𝗦𝗼𝗼𝗻. Sure, you know `SELECT`, `JOIN`, and `WHERE`—but that’s just scratching the surface. What sets you apart in interviews, dashboards, and real-world analysis? 👉 you can 𝘁𝗵𝗶𝗻𝗸 𝗶𝗻 𝗦𝗤𝗟, solve 𝗰𝗼𝗺𝗽𝗹𝗲𝘅 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗽𝗿𝗼𝗯𝗹𝗲𝗺𝘀, and write 𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁, 𝗿𝗲𝗮𝗱𝗮𝗯𝗹𝗲 𝗾𝘂𝗲𝗿𝗶𝗲𝘀. Here’s what advanced SQL professionals focus on (and hiring managers love to ask about): 🔹 𝗪𝗶𝗻𝗱𝗼𝘄 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 Calculate running totals, ranks, and compare rows using `ROW_NUMBER`, `LEAD()`, `LAG()` 🔹 𝗥𝗲𝗰𝘂𝗿𝘀𝗶𝘃𝗲 𝗖𝗧𝗘𝘀 Handle hierarchies and sequences—no loops needed 🔹 𝗖𝗼𝗿𝗿𝗲𝗹𝗮𝘁𝗲𝗱 𝗦𝘂𝗯𝗾𝘂𝗲𝗿𝗶𝗲𝘀 Apply row-level filtering logic across related tables 🔹 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝗮𝗹 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 Use `FIRST_VALUE`, `NTH_VALUE`, `PERCENT_RANK` for deep insights 🔹 𝗗𝗮𝘁𝗲 & 𝗧𝗶𝗺𝗲 𝗟𝗼𝗴𝗶𝗰 Write accurate reports, cohort analysis, and trend summaries 🔹 𝗦𝗲𝘁 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀 Merge and compare data across sources with `UNION`, `INTERSECT`, and `EXCEPT` 🔹 𝗥𝗲𝗮𝗹 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗦𝗰𝗲𝗻𝗮𝗿𝗶𝗼𝘀 Top-K filtering, performance tuning, and nested aggregations If you’re aiming for roles in 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴, 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀, 𝗼𝗿 𝗕𝗜, these are the SQL skills that make you stand out, not just pass. 📌 Save this post 🔁 Share with someone preparing for data roles 📥 Follow Aishwarya Pani for more practical SQL & Data Engineering insights 📌 Ready to grow in your data journey?  𝗟𝗲𝘁’𝘀 𝗰𝗼𝗻𝗻𝗲𝗰𝘁 → https://lnkd.in/gFKyas-g #SQL #AdvancedSQL #DataEngineering #Analytics #DataSkills #TechCareers #InterviewPrep

Don't ignore the power of SQL. 💪 Focus on its usefulness, NOT complexity. It's about these practical techniques that give clear results. Here are 16 ways to utilize SQL effectively for Data Analysts, Data Scientists, or Engineers 👇 1/ Start with SELECT * but never end there ↳ Begin exploring, then refine to only the columns you need 𝗔𝗧𝗧𝗘𝗡𝗧𝗜𝗢𝗡: 𝗗𝗼𝗻'𝘁 𝗱𝗼 𝘁𝗵𝗶𝘀 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗹𝗶𝗺𝗶𝘁𝗶𝗻𝗴 𝘁𝗵𝗲 𝗿𝗼𝘄𝘀. 𝗕𝗲𝘀𝘁 𝘁𝗼 𝘂𝘀𝗲 𝗮 𝗾𝘂𝗲𝗿𝘆 𝗹𝗶𝗸𝗲 𝘁𝗵𝗶𝘀: SELECT TOP 1* FROM table_name WHERE 1 = 0 2/ Use WHERE clauses before complex JOINs ↳ Filter early to reduce processing and improve speed 3/ Master the difference between INNER and LEFT joins ↳ Know which records you're keeping vs. excluding 4/ Write subqueries to break complex problems down ↳ Solve one piece at a time instead of one massive query 5/ Use CTEs instead of nested subqueries ↳ WITH clauses make your code readable and maintainable 6/ Learn window functions for running calculations ↳ Use OVER() to avoid complex self-joins and grouping 7/ Write date functions once and reuse them ↳ Consistent date type to prevent subtle data issues 8/ Avoid SELECT DISTINCT when possible ↳ Fix the root cause of duplicates instead 9/ Use table aliases to improve readability ↳ Short, meaningful aliases make complex queries clearer 10/ Comment on your complex SQL logic ↳ Help your future self understand your thinking 11/ Format queries consistently with line breaks ↳ Align SELECTs, JOINs, and WHEREs for quick scanning 12/ Master GROUP BY with HAVING clauses ↳ Filter aggregated results without nested queries 13/ Use CASE statements to create conditional logic ↳ Transform data directly in your SELECT statement 14/ Learn one advanced function each week ↳ COALESCE, NULLIF, and LIST_AGG solve common problems 15/ Test with small data sets before scaling up ↳ Use LIMIT to validate logic on samples before processing millions of rows 16/ Benchmark queries before and after optimization ↳ Measure improvements to know what techniques work 𝗣𝘂𝘁 𝘁𝗵𝗲𝘀𝗲 𝘁𝗶𝗽𝘀 𝘁𝗼 𝘄𝗼𝗿𝗸: 1. DataCamp: https://lnkd.in/gqM6_xvv 2. Analyst Builder: https://lnkd.in/gbpa-9ak 3. LeetCode: https://leetcode.com/ Working on real-world problems will challenge you to improve and prepare you for interviews and actual job situations. Which tip will you implement in your next SQL query? ♻️ Repost to help your network improve their SQL skills 🔔 Follow Don Collins for weekly data tips that turn complexity into clarity

The SQL skills that separate analysts from true data professionals… Most people know basic SQL. But the ones who grow fastest in data careers master advanced SQL, the kind that helps you analyze faster, optimize queries, and solve business problems with precision. SQL isn’t just about querying data. It’s about understanding how data thinks, moves, and behaves. If you can master these advanced concepts, you stop writing queries… and start building analytics systems that scale. Here are the 9 SQL concepts every data analyst must know- 1. Window Functions Ranking, running totals, moving averages, and value lookups - essential for analytics and trend analysis. 2. Subqueries & Correlated Subqueries Use nested queries for advanced filtering and dynamic conditions that depend on outer queries. 3. Common Table Expressions (CTEs) Simplify complex logic, make queries modular, and handle hierarchical data easily. 4. Pivot & Unpivot Queries Convert rows ↔ columns for dashboards, reporting, and cross-tab analysis. 5. Recursive Queries Handle parent-child structures, org charts, category trees, and anything hierarchical. 6. Analytic Functions Identify first/last values, percentile distributions, trends, and cohorts. 7. Query Optimization Use indexes, execution plans, and partitioning to make queries 10x faster. 8. Set Operations Combine or compare datasets using UNION, INTERSECT, EXCEPT — great for deduping and validation. 9. JSON & Semi-Structured Data Query APIs, logs, and nested data using JSON_VALUE, JSON_EXTRACT, and JSON_TABLE. If you want to grow as a data analyst, stop memorizing SQL syntax. Start mastering query design, performance thinking, and structured problem-solving. These concepts aren’t just “advanced SQL”… They’re the difference between writing queries and thinking like a data engineer. Follow Sumit Gupta for more insights!!

𝐇𝐨𝐰 𝐜𝐚𝐧 𝐒𝐐𝐋 𝐭𝐫𝐢𝐜𝐤𝐬 𝐢𝐦𝐩𝐫𝐨𝐯𝐞 𝐝𝐚𝐭𝐚 𝐚𝐧𝐚𝐥𝐲𝐬𝐢𝐬 𝐞𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲? SQL tricks can significantly enhance data analysis efficiency by streamlining processes, reducing manual effort, and optimizing performance. Here are some ways they can make a difference: 1. Data Transformation and Cleaning - Use *common table expressions (CTEs)* to break down complex queries into manageable steps. - Apply *window functions* for tasks like ranking, calculating running totals, or identifying duplicates—all without writing multiple queries. 2. Faster Query Execution - Indexing tips: Ensure proper indexing to improve search speed. Tricks like *covering indexes* can eliminate the need to read the main table. - Query optimization: Avoid subqueries by rewriting them as joins or utilizing *EXPLAIN plans* to understand and improve query performance. 3. Dynamic Data Analysis - Use pivoting techniques (`CASE` statements or `PIVOT` operators) to reformat data dynamically for better insights. - Recursive queries, like the one in the previous example, allow generating sequences or performing hierarchical data analysis. 4. Aggregations and Summarizations - Tricks like *GROUPING SETS* or *ROLLUP/CUBE* can provide multi-dimensional aggregations in a single query, saving significant effort for reporting. 5. Automation and Time-Saving - Leverage stored procedures or parameterized queries to automate repetitive tasks. - Dynamic SQL can adapt queries to different scenarios without writing multiple scripts. 6. Efficient Data Extraction - Use *windowed aggregates* instead of joining extra tables for summary data. - Apply query hints and table partitioning to handle large datasets efficiently. By mastering these techniques, analysts can handle large, complex datasets with agility, making insights available faster and more accurately. #datascience #sql #sqltricks

90% of SQL interviews are built on these patterns. (If you know them, you're already ahead.) SQL interviews aren’t about syntax. They’re about problem-solving and spotting patterns. If you master these 5 patterns, you won’t just answer questions, you’ll impress with clarity and confidence. 1. 𝐉𝐨𝐢𝐧𝐬 & 𝐃𝐚𝐭𝐚 𝐂𝐨𝐦𝐛𝐢𝐧𝐚𝐭𝐢𝐨𝐧 ↳ Know how to connect multiple tables. ↳ Understand inner, outer, and self joins. ↳ Learn how filtering affects results post-join. 2. 𝐀𝐠𝐠𝐫𝐞𝐠𝐚𝐭𝐢𝐨𝐧𝐬 & 𝐆𝐫𝐨𝐮𝐩 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 ↳ Use GROUP BY to uncover trends. ↳ Add HAVING to filter aggregated results. ↳ Go deeper with nested aggregations. 3. 𝐖𝐢𝐧𝐝𝐨𝐰 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧𝐬 ↳ Rank rows with ROW_NUMBER, RANK, DENSE_RANK. ↳ Compare values using LAG, LEAD. ↳ Partition data for running totals and comparisons. 4. 𝐒𝐮𝐛𝐪𝐮𝐞𝐫𝐢𝐞𝐬 & 𝐂𝐓𝐄𝐬 ↳ Use subqueries to isolate logic. ↳ Break down complexity with CTEs. ↳ Write recursive queries for hierarchy problems. 5. 𝐐𝐮𝐞𝐫𝐲 𝐋𝐨𝐠𝐢𝐜 & 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 ↳ Control flow with CASE, COALESCE, NULLIF. ↳ Filter efficiently using WHERE, IN, EXISTS. ↳ Optimize performance with indexes and EXPLAIN. You don’t need to memorize everything. Just understand these patterns deeply. That’s how top candidates stand out. Check out the full breakdown on "𝐇𝐨𝐰 𝐭𝐨 𝐀𝐜𝐞 𝐒𝐐𝐋 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰𝐬": https://lnkd.in/dVfhtz3V Remember, practice is the key!! I’ve attached a cheat sheet of the most common SQL functions to help you prep faster. ♻️ Save it for later or share it with someone who might find it helpful! 𝐏.𝐒. I share job search tips and insights on data analytics & data science in my free newsletter. Join 13,000+ readers here → https://lnkd.in/dUfe4Ac6

I’ve faced around 10 interviews for Data Analyst positions, and in every single one, SQL was a compulsory topic. While other technical skills can be optional, SQL is an absolute necessity for anyone aspiring to be a Data Analyst. If you're just starting out, or even revising your basics, here’s a simple SQL syllabus you can follow (divided by levels): 𝗘𝗮𝘀𝘆 𝗟𝗲𝘃𝗲𝗹 - CREATE, INSERT, UPDATE, ALTER, DELETE, DROP, TRUNCATE & DATA TYPES in SQL  - SELECT, DISTINCT, WHERE, LIKE, ORDER BY, LIMIT, TOP, AND, OR, NOT, IN, BETWEEN 𝗠𝗲𝗱𝗶𝘂𝗺 𝗟𝗲𝘃𝗲𝗹 - SUM, MAX, MIN, COUNT, AVG , GROUP BY, HAVING  - JOINS - INNER JOIN, RIGHT JOIN, LEFT JOIN, OUTER JOIN & SELF JOIN 𝗠𝗲𝗱𝗶𝘂𝗺 𝘁𝗼 𝗛𝗮𝗿𝗱 𝗟𝗲𝘃𝗲𝗹 - EXISTS, UNION, UNION ALL, CTE - SUBQUERIES, DATE & TIME functions - CASE WHEN, WINDOW FUNCTIONS: ROW_NUMBER, RANK, DENSE_RANK, LEAD, LAG, NTILE, FIRST_VALUE, LAST_VALUE - Using AGGREGATE FUNCTIONS as WINDOW FUNCTIONS Why SQL Matters: SQL is pivotal for data analytics because it enables efficient data extraction and manipulation, empowering businesses to uncover insights and make informed decisions. Mastering SQL can significantly enhance your ability to analyze data and contribute to your organization's success. 🎥 Learn SQL in English: https://lnkd.in/duPnsSCw 🎥 Learn SQL in Hindi: https://lnkd.in/dC4hXxGK Follow Pratik Sonawane for more! #Dataanalytics #SQL #Datascience #BigData #BusinessIntelligence