DBeaver’s Post

#TipTuesday | Format your SQL code in the push of a button. Use this tip to make your SQL code legible! Step 1: Fetch some code. Step 2: Paste it into SQL Developer. Step 3: Select all (Ctrl+A). Step 4: Format it (Ctrl+F7). Step 5: Change the bits of the formatting you don’t like. Step 6: Copy your code. Step 7: Go to Tools -> Preferences -> Code Editor -> Format -> Advanced Format. Step 8: Select “Auto-Detect Formatter Settings”. Step 9: Paste your code. Your SQL formatting is now set exactly to your liking. Just hit Ctrl+F7 whenever you’re coding, and you’ll never have to worry about your code looking messy again. Pro Tip: Not entirely happy with the results? Select “Preview with current setting” and feel free to manually change the settings to see how it would affect your code. #SQLFormatting #SQL #FormatCode

🚀 𝗦𝗤𝗟 𝗦𝗲𝗿𝘃𝗲𝗿 𝗥𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗘𝗻𝗴𝗶𝗻𝗲: 𝗣𝗵𝗮𝘀𝗲𝘀 & 𝗞𝗲𝘆 𝗖𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁𝘀 💡 PART 1 Ever wondered how SQL Server Relational Engine works? Here's a breakdown of the five phases and key components involved in turning a T-SQL batch into executed results. 𝟭. 𝗣𝗮𝗿𝘀𝗶𝗻𝗴 𝗣𝗵𝗮𝘀𝗲: 𝗧𝗿𝗮𝗻𝘀𝗹𝗮𝘁𝗶𝗻𝗴 𝗧-𝗦𝗤𝗟 𝘁𝗼 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 🎯 𝗚𝗼𝗮𝗹: Convert high-level T-SQL into a machine-readable format. 🛠️ 𝗖𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁: Parser • ✅ Checks for syntax errors (e.g., keywords, spelling). • ✅ Builds a parse tree, a structured representation of the query. 𝟮. 𝗕𝗶𝗻𝗱𝗶𝗻𝗴 (𝗔𝗹𝗴𝗲𝗯𝗿𝗶𝘇𝗲𝗿): 𝗟𝗼𝗮𝗱𝗶𝗻𝗴 𝗠𝗲𝘁𝗮𝗱𝗮𝘁𝗮 🎯 𝗚𝗼𝗮𝗹: Load metadata and ensure references are valid. 🛠️ 𝗖𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁: Algebrizer • ✅ Ensures tables and columns exist. • ✅ Adds necessary conversions to the parse tree. • ✅ Replaces views with their definitions. • ✅ Performs basic optimizations (e.g., convert right joins to left joins, flatten subqueries). 𝟯. 𝗖𝗼𝗺𝗽𝗶𝗹𝗶𝗻𝗴 𝗣𝗵𝗮𝘀𝗲: 𝗛𝗮𝗻𝗱𝗹𝗶𝗻𝗴 𝗡𝗼𝗻-𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝗯𝗹𝗲 𝗦𝘁𝗮𝘁𝗲𝗺𝗲𝗻𝘁𝘀 🎯 𝗚𝗼𝗮𝗹: Compile T-SQL statements that don’t require optimization. 🛠️ 𝗖𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁: T-SQL Compiler • ✅ Compiles simple statements like variable declarations, IF conditions, and WHILE loops. • ✅ Skips optimization for statements that don’t need it. Understanding how SQL Server processes queries helps you troubleshoot and optimize your own queries. Want more details on any phase? Drop a comment or send me a message! #wecommit100xshare #Database #DatabaseAdministration #DatabaseDevelopment #DatabaseOptimization #DatabaseManagement #TechTips #PerformanceTuning #SQL

SQL Syntax? who is the non developer and who is the developer lol I say it depends on your company what syntax to use. However, I can tell if someone is more of a pure SQL user or a developer who knows other languages by how they query or how they name columns using DDL lol 😀 non-developer will use Underscores The non-developer might use names like customer_order_id or branch_manager_of_sales, which can be overly descriptive and cumbersome. The developer way would be to use camel case. For example, customerOrderId instead of customer_order_id and branchManagerOfSales instead of branch_manager_of_sales. Using a combination of underscores and camel case makes you seem more like a developer, and it's faster to use camel case than multiple underscores. Best of the both worlds, try to use combination if you can when you have more than 2 words. customer_salesId, branch_managerOfSales its much more practical than custumer_of_brach_sales lol

The Importance of Databases in Today's Digital Era In the modern world, data drives everything—from businesses to technology solutions. A strong foundation in database management is essential, and one of the most powerful tools to master is SQL (Structured Query Language).If you're stepping into the world of data or looking to enhance your tech skills, mastering SQL will unlock new opportunities. Whether it's querying databases, managing data, or building applications, SQL is indispensable.🚀 SQL – Structured Query Language Here’s a simple SQL Cheat Sheet to help you learn and apply SQL commands easily!SELECT: Retrieve data from a databaseINSERT INTO: Add new dataUPDATE: Modify existing dataDELETE: Remove dataJOIN: Combine rows from multiple tablesWHERE: Filter data based on conditionsGROUP BY: Aggregate dataORDER BY: Sort dataHAVING: Filter aggregated data💡 Why Should You Learn SQL?Versatility: SQL is used in almost every industry, from finance to tech.Demand: Database management skills are in high demand across a variety of fields.Simplicity: SQL has a straightforward syntax, making it easier for beginners to learn.Problem Solving: Whether you work with large datasets or small projects, SQL can help you find insights in data.Career Growth: Mastering SQL can boost your career and salary potential.📊 SQL Is the Future In this data-driven world, mastering SQL is no longer an option, but a necessity. Start learning today and take control of your data!#SQL #DatabaseManagement #TechSkills #Learning #DataAnalytics #CareerGrowth #DataScience #BusinessIntelligence #DigitalTransformation #TechTrendsFeel free to tweak this for your LinkedIn post. This structure balances informative content with keyword optimization for maximum engagement and reach!

𝐒𝐐𝐋 𝐢𝐬 𝐭𝐡𝐚𝐭 𝐭𝐨𝐩𝐢𝐜 𝐢𝐧 𝐃𝐚𝐭𝐚 𝐝𝐨𝐦𝐚𝐢𝐧 𝐭𝐡𝐚𝐭 𝐧𝐨 𝐨𝐧𝐞 𝐜𝐚𝐧 𝐞𝐬𝐜𝐚𝐩𝐞! It's a fundamental skill for any Data Professional. So, to help you prepare better here's a complete plan to learn SQL ! ▲ 𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐭𝐨 𝐒𝐐𝐋 𝐚𝐧𝐝 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞 1. Understanding Databases & Types (Relational vs. Non-Relational databases). 2. SQL Syntax Basics ▲ 𝐁𝐚𝐬𝐢𝐜 𝐒𝐐𝐋 𝐂𝐨𝐦𝐦𝐚𝐧𝐝𝐬 1. SELECT Statements 2. WHERE Clause 3. ORDER BY Clause 4. LIMIT Clause 5. Functions (COUNT, SUM, AVG, MIN, MAX) ▲ 𝐖𝐨𝐫𝐤𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐌𝐮𝐥𝐭𝐢𝐩𝐥𝐞 𝐓𝐚𝐛𝐥𝐞𝐬 1. JOINS (INNER, LEFT, RIGHT, FULL OUTER) 2. UNION vs. UNION ALL ▲ 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐃𝐚𝐭𝐚 𝐌𝐚𝐧𝐢𝐩𝐮𝐥𝐚𝐭𝐢𝐨𝐧 1. GROUP BY and HAVING Clauses 2. Subqueries 3. INSERT, UPDATE, DELETE Statements. ▲ 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞 𝐃𝐞𝐬𝐢𝐠𝐧 𝐚𝐧𝐝 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 1. Normalization 2. Indexes 3. Transactions and Concurrency ▲ 𝐒𝐐𝐋 𝐓𝐨𝐨𝐥𝐬 𝐚𝐧𝐝 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐞𝐬 1. SQL IDEs: Practice using SQL interfaces like SSMS, pgAdmin, or MySQL Workbench. 2. Version Control 3. Writing Efficient SQL Queries ▲ Practice with Real-World Scenarios Sample Projects & Online Challenges Like this post if you found it helpful 👍❤️ Hope this helps! 😊 #sql #sqldeveloper #sqldatabase

🚀 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐒𝐐𝐋 𝐂𝐨𝐧𝐜𝐞𝐩𝐭𝐬: 𝐂𝐓𝐄𝐬 𝐚𝐧𝐝 𝐓𝐞𝐦𝐩 𝐓𝐚𝐛𝐥𝐞𝐬 🚀 As part of my journey in mastering advanced SQL, I’ve recently delved into Common Table Expressions (CTEs) and Temporary Tables. These powerful tools can significantly enhance the efficiency and readability of your SQL queries. Here’s a brief introduction to both, along with their use cases and when to use which. 🔍 𝐂𝐨𝐦𝐦𝐨𝐧 𝐓𝐚𝐛𝐥𝐞 𝐄𝐱𝐩𝐫𝐞𝐬𝐬𝐢𝐨𝐧𝐬 (𝐂𝐓𝐄𝐬): CTEs are temporary result sets that you can reference within a SELECT, INSERT, UPDATE, or DELETE statement. They are defined using the WITH clause and are particularly useful for breaking down complex queries, improving readability and maintenance. 𝐔𝐬𝐞 𝐂𝐚𝐬𝐞𝐬: > Simplifying complex queries by breaking them into simpler parts. > Recursive queries, such as hierarchical data retrieval. > Improving code readability and maintainability. 🔍 𝐓𝐞𝐦𝐩𝐨𝐫𝐚𝐫𝐲 𝐓𝐚𝐛𝐥𝐞𝐬: Temporary tables are similar to regular tables but are stored in the tempdb database and are deleted when the session that created them ends. They are great for storing intermediate results or when you need to perform multiple operations on a result set. 𝐔𝐬𝐞 𝐂𝐚𝐬𝐞𝐬: > Storing intermediate results for complex calculations. > When you need to manipulate the data multiple times. > When you need to index the temporary data to improve performance. 𝐖𝐡𝐞𝐧 𝐭𝐨 𝐔𝐬𝐞 𝐖𝐡𝐢𝐜𝐡: 𝐂𝐓𝐄𝐬: Best for improving query readability and for recursive queries. Use them when you need a temporary result set that is used only once or twice within a single query. 𝐓𝐞𝐦𝐩𝐨𝐫𝐚𝐫𝐲 𝐓𝐚𝐛𝐥𝐞𝐬: Ideal for storing intermediate results that require multiple operations, indexing, or when you need to reference the data multiple times throughout a session. Both CTEs and Temp Tables are valuable tools in a SQL developer's toolkit. Understanding when and how to use them can lead to more efficient and maintainable code. Happy querying! 🌟 #SQL #DataEngineering #CTE #TempTables #LearningJourney #DataConsultant

🚀 Data Contract CLI v0.10.12 is here! 🚀 We're thrilled to announce the latest version of Data Contract CLI, packed with enhancements and fixes to make your data modeling experience smoother. Again, a big THANK YOU to our amazing community for your contributions and feedback! Your support drives continuous improvements! 👏 Here's what's new in v0.10.12: 🌟 Added: - DBML Model Import: Seamlessly import DBML models (kudos to Joachim Praetorius) - Export to SQLAlchemy ORM: "datacontract export --format sqlalchemy" now available (kudos to Marco Vela) - Glue Import: Support for varchar max length (kudos to Andrii Roiko) - Environment Variable Support: "datacontract publish" accepts DATACONTRACT_MANAGER_API_KEY as an env variable - Avro Schema Export: Required fields are now supported (kudos to jsamson1) - Spark Import/Export: Support for data type map (kudos to Pierre M.) - Avro Enhancements: Support for enums and enum titles on export and import 🔄 Changed: - Deltalake: Now powered by DuckDB's native support (Changed by myself ;) - YAML Dumping: Alias name used instead of pythonic name (kudos to Michael J. Schultz) 🐞 Fixed: - Fix an issue where the datacontract cli fails if installed without any extras (kudos to Taras Slipets) - Glue database location error (kudos to Andrii Roiko) - Bigint to long mapping fix (kudos to Andrii Roiko) - BigQuery import parameter name correction (kudos to Joachim Praetorius) - S3 server connection issue resolved (#kudos to mstysk) - Model specification bug fix (kudos to Michael J. Schultz) - Spark array type management issue fixed (kudos to Pierre M.) Update now and take advantage of these powerful new features and improvements: https://lnkd.in/eRhZgJx2 #DataContracts #DataEngineering #DBML #SQLAlchemy #Spark #Avro #DeltaLake #Glue #OpenSource

Consider two SQL queries for pagination: OFFSET-BASED (Scans and skips the first 10,000 rows, then retrieves 100): SELECT * FROM users ORDER BY id LIMIT 100 OFFSET 10000; (API: GET /users?page=100&limit=100) KEY-BASED (Directly retrieves the next 100 rows after id > 10000, leveraging logarithmic index lookup): SELECT * FROM users WHERE id > 10000 ORDER BY id LIMIT 100; (API: GET /users?after=10000&limit=100) Pagination is critical when your API is to serve a large dataset. If the lookup is on an indexed column, then you might be better off using KEY-BASED pagination with LIMIT instead of the traditional OFFSET with LIMIT. Of-course, key based works well only in case of ordered sequential indexed column and also requires the client to keep track of the last seen key, which can be returned in the API response #api #database #design #sql #backend #techtalks

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1. Just finished up a major project using SQL - love how powerful and versatile this language is for managing and analyzing data! #softwareengineer #sql" 2. "Feeling like a coding rockstar after successfully optimizing our database with some slick SQL queries. Who else is a SQL ninja like me? 🙌 #softwareengineer #sql" 3. "SQL continues to amaze me with its capability to streamline and enhance our software projects. Proud to be a part of the SQL community! 💻 #softwareengineer #sql"