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What is the difference between INNER JOIN and OUTER JOIN? ------------------------------------------------------------- INNER JOIN: Returns: Only rows with matching values in both tables. Use When: You need data that exists in both tables. Example: SELECT * FROM A INNER JOIN B ON A.id = B.id; OUTER JOIN: ------------ Types: ------------- LEFT JOIN: Returns all rows from the left table, and matched rows from the right table. Unmatched rows from the right table result in NULL values. RIGHT JOIN: Returns all rows from the right table, and matched rows from the left table. Unmatched rows from the left table result in NULL values. FULL JOIN: Returns all rows when there's a match in either table. Unmatched rows from either side will have NULL values. Use When: You need all data from one or both tables, regardless of matches. Example: SELECT * FROM A LEFT JOIN B ON A.id = B.id; #SQL #SQLInterviewQuestions

There are various types of join statements you can use depending on the use case you have. Specifically, there are four types of joins as follows: Inner Join: This method returns datasets that have the same values in both tables. Full Join: This is also referred to as a full outer join. It combines all rows from the left table and the right table to create the result set. It means that a query would return records from both tables, even if they had NULL values. If there are no matching rows, the result-set (joined table) will display NULL values. Right Join: This is also referred to as the Right outer join. All records from the right table will be returned, as well as any records that match from the left table. Left Join: This is also referred to as Left outer join. All records from the left table will be returned, as well as any records that match from the right table.

Users tell us that they love how little space ClickHouse takes to store data. And guess what? In the 24.6 release, we’ve made it even better! Read our latest blog post to learn more about the optimal table-sorting feature that makes this happen. This release also sees a beta release of chDB, our in-process SQL engine, and functions for Hilbert Curves. https://lnkd.in/ejP3xB-3

🚀 𝐒𝐐𝐋 𝐐𝐮𝐞𝐫𝐲 𝐢𝐧 𝐀𝐜𝐭𝐢𝐨𝐧: 𝐅𝐢𝐥𝐭𝐞𝐫𝐢𝐧𝐠 𝐛𝐲 𝐒𝐞𝐥𝐥𝐞𝐫 𝐑𝐚𝐭𝐢𝐧𝐠! 🚀 Just wrapped up a cool SQL query for a web shop scenario! I had two tables: Sellers and Items, where each item is linked to a seller. The goal was to select only those items where the seller had a rating greater than 4 ⭐. Here's how I approached it(see the image below) 👉This query returned the item name and the seller name for items belonging to sellers with a top-notch rating! 👉It’s fascinating how SQL can help connect and filter data effortlessly, allowing businesses to keep track of their high-quality sellers and the products they offer. Thanks to Pragya mam #SQL #DataAnalysis #SQLQueries #DataInsights #WebShop #SellersAndItems #TechSkills #LearningByDoing

We're back with this week's Wrap of what's new (and what's better) in Equals. New We’re listening to your feedback and fine-tuning the querying experience: • Headers are now restyled to more clearly identify queried output (purple), calculated columns (green), and data notes (yellow) • We’ve improved datasource joins to make each step more intuitive • We've streamlined the process of adding and removing filters on connected sheets Improved • You'll find links to relevant datasources from the schema overview in the table browser Fixed • You can now add an OR criteria after several ANDs in query builder filters And that's your Wrap! See you next week—same time, same place.

Reading: Query Exercise: Finding Sister Locations to Help Each Other #sqlserver

Time to kick off your Monday with your Weekly Wrap of what's new in Equals! With some big projects still in the works, this week's small but mighty updates are focused on amping up the speed and reliability of the query experience: • Queries that modify a pivot table's data range now refresh consistently • Query filters always recognize your database's default schema • Saved queries that reference tables that no longer exist can still be opened and edited •You can now directly paste values into calculated column headers That's a wrap. Until next week — happy building! For the latest on what's new, improved, and fixed -> updates.equals.com 

I've been diving deeper into SQL and discovered something new and interesting to me: the difference between RANK() and DENSE_RANK() window functions. I was only aware of RANK(), but skipping ranking after ties bothered me. Here's a concise example of employees and their revenue contributions to the company: | employee_id | employee_name | revenue | | 1 | Alice | 5000 | | 2 | Bob | 7000 | | 3 | Charlie | 7000 | | 4 | David | 6000 | | 5 | Eva | 5000 | ------------------------------------------------------ Consider this query: SELECT employee_id, employee_name, revenue, RANK() OVER (ORDER BY revenue DESC) AS rank, DENSE_RANK() OVER (ORDER BY revenue DESC) AS dense_rank FROM employees; It provides us the result set of: | id | name | revenue | rank | dense_rank | 2 | Bob | 7000 | 1 | 1 | 3 | Charlie | 7000 | 1 | 1 | 4 | David | 6000 | 3 | 2 | 1 | Alice | 5000 | 4 | 3 | 5 | Eva | 5000 | 4 | 3 Do you see the difference? In this example, RANK() skips ranks after ties, whereas DENSE_RANK() does not. Understanding these differences can be crucial.

Val gift to you. Want a Val gift? Say no more. Here’s a gift of knowledge, in this package is contained steps on how to arrange your Val gifts: Put the first gift down first, add subsequent gifts to it, oh, that’s you stacking the gift items, right? Yeah! You’re right. Stacking the gifts is just you arranging it in an ordering form, as a programmers, we arrange data too, so it can be easily accessed and manipulated. Stack is a data structure, and in this article, I’ll walk you through implementing a stack with linked list. https://lnkd.in/dR8AXqsv

▶ Tables Tables are used to show data in Grid format ie( in rows and columns) They are particularly useful when you need to organize the data. ▪ Structure Tags that are used to create a table: ▫ <table>: The <table> tag is used to construct a table. All other tags used in creating the table are placed between the <table> tags. ▫ <tr>: The <tr> tag is used to create a row in a table. ▫ <th>: The <th> tag is used to represent data as a heading. It appears in bold in the browser. ▫ <td>: The <td> tag is used to represent data in the table. ▪ Example <html> <head> <title> Table</title> </head> <body> <table> <tr> <th>Days</th> <th>Weather</th> </tr> <tr> <td>Monday</td> <td>Sunny</td> </tr> <tr> <td>Tuesday</td> <td>Rainy</td> </tr> </table> </body> </html> Mehtab Alam #saylanimassittraining