Data Engineering’s Post

Data Modernization Services In the words of great American writer Daniel Keys Moran, "You can have data without information but you can't have information without data." First the question arises what is data modernization? In simple words it is the process of using the company's existing database

“Nobody ever got fired for buying IBM.” This classic phrase highlights the importance of making trusted decisions—especially when it comes to building your finance tech stack. But times have changed. Today, there are countless tools for every part of the finance workflow. How do finance leaders decide what works best? That’s why I’m launching the Finance Tech Stack Survey: a quick, 3-minute survey (100% multiple choice) designed to uncover the tools finance teams are actually using and loving. Take the survey 👇 https://lnkd.in/eK6PXtmv Let’s build a playbook for the modern finance team together.

There are a lot of myths and misinformation about extracting information from an XBRL-based report. I created a little prototype XBRL-based report repository to help those that are interested to understand the moving pieces of that puzzle. I even provided a working application for doing such extractions using Excel. https://lnkd.in/gpDF_RJ9

I completed the fifth course during this month: The fifth course is: #Building_A_3_Statement_Model_In_Excel A very excellent course, I recommend it to anyone working in finance or financial analysis. The course covers preparing #financial_modeling tables for the three financial statements: - Income Statement. - Balance Sheet. - Cash Flow Statement. - #Financial_forecasts for a number of years based on three scenarios, and in a very wonderful way.

I'm excited to share our latest blog post that delves into the intricacies of Lisp Query Notation (LQN). This comprehensive article explores how LQN can enhance data manipulation and retrieval processes, offering valuable insights for developers and data analysts alike. As organizations increasingly seek efficient ways to manage and query data, understanding LQN becomes essential. Discover how this notation can streamline your workflows and improve your data operations by reading the full post here: [Lisp Query Notation (LQN)](https://ift.tt/IEqjsUN).

MISMO has published a new white paper, "Ability to Repay Decision Model and Notation", which demonstrates how decision modeling and notation language can be used with the MISMO Logical Data Model (LDM). Check it out! https://lnkd.in/ekyyS223

Friday 1. Pivot Index: Balancing Act The first problem to tackle for the day was Find Pivot Index. It is a fantastic problem that is well presented and leads to the solving of the very typical problem of balancing portions of an array. Now, the definition of a pivot index is the one in which the sum of the numbers on the left equals the sum of the numbers on the right. Simple, isn't it? But catch: you need to find this without brute-forcing your way through the array lest you end up with some pretty inefficient solutions. On a first pass, it seemed that the best way would be to check the sum of elements on both sides of each index. But that would have result in an  O(n2) solution. Therefore, I needed to be smarter. The trick here is realizing you don't need to recalculate the sum for every index once you know the total sum of the array. Instead, you track the sum of elements by moving through the array and adjust the "left sum.". By using a running sum, I reduced the time complexity to 𝑂(𝑛) O(n), iterating over the array just once while maintaining constant-time operations for each index. It was satisfying to find an elegant and efficient solution that balanced both time and space complexity. https://lnkd.in/dzPK_qJY 2. Remembering the Pivot Index (Again): Why Memorization is Important  Second time around on the Pivot Index problem felt almost like a continuation, but with new perspective. Why? Simply because, to master an algorithm, sometimes the repetition is as important as an actual solution. Even if you solved it once, reviewing it later may help you find new optimizations, alternative methods, or more elegant ways of looking at the problem. While redoing the same exercise may sound redundant, it is actually a really good way to re-imprint some of the principles. Every time you go back to a problem, you will notice different nuances. Perhaps edge cases that you hadn't accounted for the first time around, or how an algorithm could be simplified. The revisit today really brought out a deeper appreciation of the efficiency and elegance that otherwise may only result from what at times seem like simple problems. Key Takeaways: Efficiency Is Key: The Pivot Index problem is an excellent example of how small optimizations—like using a running sum—can drastically improve efficiency. What initially seems like a straightforward task can quickly become a computational bottleneck if approached naively. This problem reinforced the value of always asking yourself, "Can this be done in fewer operations?" Repetition Deepens Understanding: Going through the problem a second time reinforced how important repetition is in the learning process. It helped me spot potential improvements and reminded me that each problem-solving experience is an opportunity for growth. https://lnkd.in/dzPK_qJY

Why use one type of slowly changing dimension (SCD) over another? ➡️ Let’s discuss the strengths and weaknesses of each. ⚒️ Type 1 Slowly Changing Dimensions Pro: You always have the most recent value.  Con: You have no record of previous values. ⚒️ Type 2 Slowly Changing Dimensions Pro: You retain records with old and new values. Con: Your tables have no true primary key. ⚒️ Type 3 Slowly Changing Dimensions Pro: There’s a true primary key of each record.   Con: They only track one change in record values. ⚒️ Type 4 Slowly Changing Dimensions Pro: You retain records for each change in value.  Con: You need to piece together historical values using two different tables. Which do you prefer to deal with in your databases? ⬇️ Comment down below!

Have you heard about Microsoft’s recent pricing update? Don’t miss out on this essential information - head to our blog post now to make informed decisions with our in-depth analysis.

A taxonomy is the backbone of the hierarchical data structure. It’s one of the key pillars of a successful records management strategy 👍 Just like organizing a large library of books, classifying content into logical filing structures makes it easier to access what’s needed. Getting a #taxonomy right pays off for years to come, it is the foundation of what comes later. Read more: https://lnkd.in/eKwNfkBB Book a Demo: https://lnkd.in/e52QRFcJ #metadata #recordsmanagement #informationmanagement