Abdul Rauf on LinkedIn: I finally completed my final project for CS50SQL! Initially, I thought of… (2025)

Day 23 of #100DaysofNoCode 💯Today I learned about Notion databases and how to build a blog using @NotionHQ & @feather_blogsHere's my blog 👉 https://lnkd.in/gkkGPhen

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Me, trying to deploy a simple data pipeline: Everything's ready, just need to push the button...Dependency gremlins: "Hold on there, buddy. Seems like version X.Y.Z of library A requires version W.V.U of library B, which conflicts with version M.N.O you have installed for library C..."Me: (╯°□°)╯︵ ┻━┻ 🤦♂️ #dataengineering #dependencies #softwaredevelopment #humor #relatable #LinkedInLaughs 🤖💾🔗

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I've been using Prisma for one of our clients for the past year, not necessarily understanding what's happening "behind the scenes". As we wanted to improve performance, we started using Knex -SQL query builder, and Bun. The fun began when I needed to convert the "easily written" Prisma queries to Knex. It then became quite important to know SQL because, after all, it's the foundation. When you want to test if the code you wrote is correct and you're fetching the right things, the best way is to test your generated SQL code. So, I took and completed an SQL course from Codecademy. Cheers! 🙌

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[New Free #SQLServer Video Alert]Parsing SP_Server_Diagnostics Output (by Amit R S Bansal)Watch the full video on SQLMaestros https://bit.ly/sqlmaestrosPls Share.

🕸️New Docs: Querying Graph DBs🕸️Graph DB's are great for capturing many real world relationships but can be cumbersome to interact with. The ability of LLM's to extract relationships and write complex structured queries is making it practical and valuable to use graph DB's in a wide range of applications.Our favorite graphista Tomaz Bratanic has contributed in-depth docs on using LangChain to build graph DB querying systems. These include:- A quickstart- How to handle high cardinality properties- How to build safer and more robust systems with narrowly-scoped tools- General prompting strategies- Specific graph DB integrationsGive it a read: https://lnkd.in/gEG55ezA

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💡 DSA Day 8: Understanding Time and Space Complexities in 1D Arrays! 💡Time and space complexity analysis are crucial for assessing the efficiency and memory usage of algorithms and data structures. Let's delve into the time and space complexities of various operations in 1D arrays:1️⃣ 𝗧𝗶𝗺𝗲 𝗮𝗻𝗱 𝗦𝗽𝗮𝗰𝗲 𝗖𝗼𝗺𝗽𝗹𝗲𝘅𝗶𝘁𝗶𝗲𝘀 𝗶𝗻 𝟭𝗗 𝗔𝗿𝗿𝗮𝘆𝘀:𝗖𝗿𝗲𝗮𝘁𝗶𝗻𝗴:Time Complexity: O(1) - Constant time complexity. Creating a 1D array involves allocating memory for a fixed number of elements, which is a direct operation.Space Complexity: O(n) - Linear space complexity. The space required for a 1D array is proportional to the number of elements.𝗔𝗰𝗰𝗲𝘀𝘀𝗶𝗻𝗴 𝗘𝗹𝗲𝗺𝗲𝗻𝘁𝘀:Time Complexity: O(1) - Constant time complexity. Accessing elements in a 1D array by index is a direct operation.Space Complexity: O(1) - Constant space complexity. Accessing elements does not require additional space.𝗜𝗻𝘀𝗲𝗿𝘁𝗶𝗼𝗻 𝗮𝘁 𝗘𝗻𝗱:Time Complexity: O(1) - Constant time complexity. Inserting an element at the end of a 1D array is a direct operation.Space Complexity: O(1) - Constant space complexity. Insertion at the end does not require additional space.𝗜𝗻𝘀𝗲𝗿𝘁𝗶𝗼𝗻 𝗮𝘁 𝗕𝗲𝗴𝗶𝗻𝗻𝗶𝗻𝗴 𝗼𝗿 𝗠𝗶𝗱𝗱𝗹𝗲:Time Complexity: O(n) - Linear time complexity. Inserting an element at the beginning or middle of a 1D array requires shifting elements, leading to linear time complexity.Space Complexity: O(n) - Linear space complexity. Insertion may require additional space for shifting elements.𝗧𝗿𝗮𝘃𝗲𝗿𝘀𝗶𝗻𝗴:Time Complexity: O(n) - Linear time complexity. Traversing all elements of a 1D array involves visiting each element once.Space Complexity: O(1) - Constant space complexity. Traversing does not require additional space.𝗟𝗶𝗻𝗲𝗮𝗿 𝗦𝗲𝗮𝗿𝗰𝗵𝗶𝗻𝗴:Time Complexity: O(n) - Linear time complexity. Searching for an element in an unsorted 1D array requires checking each element sequentially.Space Complexity: O(1) - Constant space complexity. Searching does not require additional space.𝗗𝗲𝗹𝗲𝘁𝗶𝗻𝗴:Time Complexity: O(n) - Linear time complexity. Deleting an element from a 1D array requires shifting elements, leading to linear time complexity. However, if the element is deleted from the end, the time complexity is O(1).Space Complexity: O(1) - Constant space complexity. Deletion does not require additional space, especially when deleting an element at the end (O(1) time complexity).Understanding both time and space complexities associated with different operations in 1D arrays is crucial for designing efficient algorithms and optimizing performance.Check out my GitHub repository for more Data Structures and Algorithms implementations and practice problems:https://lnkd.in/g5ZbAkrSStay tuned for DSA Day 9, where we'll dive into multi-dimensional (2D) arrays and their complexities! 🚀💻 #DataStructures #Arrays #TimeComplexity #SpaceComplexity #TechLearning #DSA #DSADay8 #Java #DS

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https://lnkd.in/dFzuUY3x<< ...Ingestr is a command-line application that allows you to ingest data from any source into any destination using simple command-line flags, no code necessary... >>

Yesterday, Andrew Chan, IFRI Certified talked about the proof of concept for an actuarial Excel Lambda library. The current library which I created https://lnkd.in/gDYvAdMt just wraps a call to get mortality tables from https://mort.soa.org. But the other side of the spectrum is wrap the Fed's API https://lnkd.in/gtJDrBCK in similar Lambda functions so you can get market data. This may be overkill because the FRED Excel plugin is quite good, which you can find at https://lnkd.in/g9skdFcT. I get my hopes up that we can now build reusable Excel version controlled libraries that allow actuaries to quickly build calculations that are easy to read and understandable to external parties, especially IT developers. Furthermore, Lambda calculus leads to purity. Purity is having a function in your program working like a function in math. A particular input will always give me the same result, which is not true in imperative paradigms. Purity makes programs very easy to massively parallelize calculations, which is why Hadoop used the same concept. Now Excel can be fun and interesting! What if we could imbed Haskell, https://www.haskell.org/, in Excel formulas? That would be the day!

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https://lnkd.in/dAXn8cA7<< ...Reladiff is a high-performance tool and library designed for diffing large datasets across databases. By executing the diff calculation within the database itself, Reladiff minimizes data transfer and achieves optimal performance... >>

[#SQLServer Video Archives] Removing the SORT Operator (by Amit R S Bansal SQLMaestros)Watch the full video: https://lnkd.in/gNgbghkBMore tutorials here: https://lnkd.in/eGV-TvyHShare.#SQLServerWithAmitBansal