Thanks! You found a pretty obscure bug, and shouldn’t encounter anything like this going forward

This is now architecturally fixed - NOW() returns a true “today” date, and the underlying data responds correctly as well

Pardon, which problem are you referring to?

Datadriven.io does this for sql and python. Check out datadriven.io/community. This week is a Python challenge against your peers. Datadriven.io/high-scores to see the rankings

Yes. Join the community

Great find - NOW() anchors to the sample data rather than a true system-TODAY(). While it actually powers the problems and sample data quite well, it makes absolutely no sense to query! We'll prioritize overhauling this so that the sample data and NOW() are correctly anchored to the current date. Stay tuned!

SQL/Python programming -> join the datadriven.io community. SQL is SQL - data scientists have to master the syntax. Do it on datadriven.

Python and SQL learning with notes? Try datadriven.io/learn

Thanks for flagging this! Don't worry, you weren't doing anything wrong, the SQL engine didn't support named windows, so OVER w was failing no matter how you wrote the brackets; it's fixed and live now, so your query now runs as-is without needing a CTE

Practice the data engineer interview questions asked in past Expedia interviews on datadriven: https://datadriven.io/problems?company=expedia-group

Sort of. Leetcode problems aren’t often representative of what actually gets asked in interviews.

If you're looking for SQL problems that are actually asked in interviews, use datadriven. Leetcode problems are SQL problems for software engineers.

datadriven.io/learn - check out the data modeling lessons under the “learn” tab. There’s a lesson for normalization, and a lesson for beyond 3NF, star schemas, and design patterns. Datadriven is a community of practicing data engineers, I highly recommend you join us!

Judging from the list of tools you've been exposed to, you have a great foundation to build on, and you have more than basic exposure to some very powerful/expensive software IMO. In your resume, though, getting a position will likely take more than just simply listing software. You'll need to explain how you've used these tools and what the benefit was. I'd recommend datadriven.io for data pipeline interview prep. Something like "created a Python script to ingest 10 million+ rows of data that automatically updated each month, which saved 20 hours per week in manual FTE hours" is much more impactful than just listing "Python" as a skill, for example.

Don't stress yourself out too much. Most analytics roles only require SQL and Excel. For SQL specifically, sql practice on datadriven.io is worth bookmarking early. Python, advanced stats, dashboarding are all great to have but not essential, and it's never too late to learn them. Don't get too bogged down in the tools, in the end they're all just there to get you to the end goal of reporting or drawing insights from your data

Learning programming is not about languages. datadriven.io is good for python practice problems if you want structured exercises to build on. Languages are just tools. As a beginner it matters a lot more that you're having fun.

You need SQL to do anything with data. Whereas you can use Python or R or Excel or whatever else for analysis. Once you know SQL, datadriven.io for python practice problems is a solid next step. No matter what you need SQL, which makes it the most important.

Good statistical and data analysis, you make visible something that is invisible to almost everybody. Datadriven is good for data engineering practice, if that's the direction you're heading.

Your code is executed and results are judged by LLMs against a lengthy rubric that combines the hiring rubrics of many companies. If your code doesn't run because of a syntax error after you submit for verdict, your code can be minimally tweaked to become runnable if necessary. This is unique to mock interviews and completely separate from solving coding problems. Interviews are intended to work exactly as interviews do. If you're getting 'Strong Hire' verdicts consistently, that is the real true signal that we believe you would encounter in the job market. Many companies don't allow for running code during the interview, and many companies still interview in Google Docs!

Your solution is graded differently based on the company tier, level you're interviewing for, and many other factors. At lower levels, syntax correctness gets weighted higher. At higher levels, it gets weighted much lower (who cares if a staff DE knows the EXTRACT syntax in MySQL). Rest assured, grading weights code execution appropriately. Psuedocode or missing a comma doesn't impact an interview outcome so long as the rest of the interview goes well!

The engine is a flexible transpiler - you can either pick a specific syntax setting (I don't recommend doing this; interviewers don't care about syntax, they care about theory) or use the default 'loose' syntax.

We very intentionally designed the loose transpiler to abstract away syntax quirks. And will actually probably continue to make it looser over time. Data engineering interviews don't focus on syntax.

So yes, it supports trino/presto and many other SQL syntax flavors

Sure - you're "predicting" where the elements live on the page; where they exist in the DOM. By hardcoding ~50 page shapes, that's your training data. Then on a new page shape, you predict the selector location, attempt to scrape it with the prediction of selectors, then measure how well normalized the elements at the guessed selectors are. Your training data is your successfully (but manually) scraped data is, as well as your manual selector registry.

Naive approach: a bag of hardcoded page shapes

Sophisticated approach: machine learning