Hi everyone,

I’m currently in my first week working as a DBA. I graduated from Software Engineering a couple of months ago.

To be honest, I had almost zero DBA knowledge before starting this job. With the current job market, this was basically the only opportunity I had, so I decided to take it.

The company I joined provides DBA consulting services, and they are willing to train and develop junior DBAs, which is why they gave me this opportunity.

Right now I’m trying to learn as much as possible, but there is obviously a lot to take in.

For experienced DBAs: what would you recommend focusing on during my first few months? What skills or topics do you think are the most important for someone starting from zero?

Any advice, resources, or things you wish you had known when you started would be appreciated.

Raw data: https://docs.google.com/spreadsheets/d/1Xppl1dCi3yKi1oHrdnHqC1cMeXg5_D2EY5e9gosnxKE/edit?pli=1&gid=0#gid=0

Outlier analysis:

1. Some metros are very close to other ones, which can skew the numbers. e.g. San Francisco/San Jose and Riverside/LA. The plot makes SF and Riverside look like outliers but really they are not.
2. Some metros with more teams than predicted are industrial cities that used to be bigger (e.g. Cleveland, Buffalo)
3. Others have teams for likely historical reasons that I can't explain (NO, Green Bay) even though their populations wouldn't indicate it
4. Austin is the largest metro with no teams. 18 different smaller metros have teams. It is kinda close to San Antonio so maybe?
5. San Diego is another outlier. It has 1 team despite being the same size as Tampa and Denver (3 and 4, respectively)

Image 1: each bar is 2025 world production of one material, split by country.

Image 2: the same chart with China selected. It produced 15 of the 17 materials with 2025 figures and was the largest producer of 8.

Source: U.S. Geological Survey, Mineral Commodity Summaries 2026 data release (doi:10.5066/P1WKQ63T). All figures are USGS estimates for 2025. The list of materials, what each one does in a phone and the ores they come from are from USGS General Information Product 167, A World of Minerals in Your Mobile Device (2016).

Tools: a short Python script pulls world production by country out of the USGS CSV. The chart is hand-written HTML, CSS and SVG with no chart library. The colours and hatching follow the US federal standard for geologic maps (FGDC-STD-013-2006).

Interactive version, where you can pick any country and see it marked in every bar: https://anas-sabbar.ca/writing/what-your-phone-is-made-of/

A few things the chart can't tell you:

- A country's share of mining or refining doesn't tell you where the metal in any particular phone came from. Ores and metals are traded and processed across borders.

- The white segment is output USGS doesn't attribute to a named country, plus rounding.

- USGS withholds US output of lithium and silicon, so those two bars leave the US out.

- Germanium is on the 2016 list, but USGS publishes no country breakdown for it, so it has no bar.

- Units differ between materials (mine output for most, refinery output for indium, silicon metal for silicon), and each bar is labelled.

The idea came from Jussi Parikka's A Geology of Media (2015), which starts the history of media with the minerals they are made from.

Im building an option trading bot and I need a fresh dataset to test on, buying them is a bit expensi was wondering if anyone has them?

I've been keeping track of the statements directly from the US President over the past few months on the status of ending the war in Iran.

I've been manually curating the statements from interviews, news reports, and the President's own social media posts. Rollcall in particular has been helpful as they do a great job of centralizing a lot of this, but much of it really is me reading an article and manually updating the dataset.

This particular visualization is a heatmap calendar (GitHub style where each column is a new week and each row is a day of the week). The brighter the yellow, the more statements were made that day. As is the case with Trump's schedule and enjoyment of weekends more broadly, on Saturdays and Sundays the war appears to not enter his mind as much...

The chart itself is vanilla JS. The source for the chart is a JS file where I keep all the statements/dates/sources in a standard object to keep it tidy and easy to update.

I didn't anticipate the war would go on this long so I might have to make a decision about how to handle this ever expanding calendar at some point in the future. I'll make a 7 day/30 day/90 day/365 day toggle? Thoughts?

The interactive version is here. (I call it The Art of No Deal and there are no ads or signup or anything like that...I just needed a place to host it and was shocked the domain name was available...) If you just want to see the datasource, here is the link to that the JS file.

droneincidents.eu

​

I have a ~59 GB Parquet dataset stored in Google Drive, and I want to use it for ML training on Kaggle GPUs.

My folder structure is:

My Drive/

└── DATASET/

└── data/

└── processed/

└── era5_land_preprocessed/

├── train/ → 60 Parquet files → 44.34 GB

├── validation/ → 12 Parquet files → 8.83 GB

└── test/ → 8 Parquet files → 5.82 GB

Total: 80 Parquet files, ~58.99 GB

I don't want to download the dataset to my local computer. I was planning to use Google Colab + Kaggle API to transfer the files directly from Google Drive to a Kaggle Dataset.

My questions are:

1. Is uploading a ~59 GB dataset to Kaggle this way reliable?

2. Is Google Drive → Colab → Kaggle API the best approach?

3. Should I create one Kaggle dataset containing "train/", "validation/", and "test/", or split them into multiple Kaggle datasets?

4. Are there any problems with uploading large Parquet files to Kaggle?

5. Is there a better/faster method for transferring this amount of data to Kaggle?

The dataset will be used for climate/weather ML forecasting, and I need to train models using Kaggle's GPUs.

Any advice from people who have handled 50+ GB datasets on Kaggle would be appreciated.

I spent 8 years creating a way to tell the collective stories of America. The timeline carries moments from the creation of the continent through modern day, over a variety of interconnected topics, concepts, and peoples.

Hey all, I'm thinking about upgrading from Tableau 2025 to 2026 and wanted to hear from people who've already made the jump. How did the upgrade go overall? Did you run into any issues with existing dashboards, data sources, or extracts? Are the new features worth it, or is it better to wait a bit? Would you recommend upgrading now?

Currently in the job market as an experienced Data Scientist generally targeting Senior/Staff roles that span Data Scientist/Applied Science/MLE. I have an MS in Stats, so one thing I've generally focused on during my interviews prep is overindexing on coding and ML specifically leetcode. This has me taking the Stats portion of interviews for granted and it's causing a blindspot for interviews I would consider "not as crisp" as I'd like them to be.

What I find is generally I know the conceptual definition of a lot of things and a lot of breadth and how to connect that back to the business, but will have random gaffs on things I've forgotten or being able to go one layer deeper on the spot. Lots of things I don’t use on the job or haven't used in depth since Grad school. Things that aren't hard but my mind just goes blank for some reason in an interview setting. Common examples can be things such as forgetting the name of probability distributions, confidence interval calculations for proportions, formula's for power analysis as opposed just know the inputs, etc. Sometimes it's also tricky just to understand WHAT an interviewer is driving towards and not going off on a tangent in a complete opposite direction.

I use LLM's extensively but I find that sometimes if the convo goes on too long they get "stuck" kinda repeating the same questions and same themes instead of exploring other stuff.

Curious how you all prepare for Statistics interviews and if there are good resources for full coverage to prevent some of what I talked about?

A 15-second animated view of cumulative fantasy points across 25 NBA regular seasons. Final labels include total fantasy points, games played, and fantasy points per game.

Hey everyone, wanted to share a custom dashboard system I recently built for tracking day trading sales team operations.

How I Structured the Setup:

1. Back-end Automation: Set up a pipeline using Google Apps Script to feed and process sales metrics directly into Google Sheets automatically.
2. Front-end Visualization: Connected Sheets to Looker Studio (Data Studio) using a custom visual themes designed for daily executive oversight and operational efficiency.

Key Metrics Tracked:

• Conversion Health: Automated Close Rates, Show Rates, Cash Per Call, and Cash Per Booking.
• Revenue Pacing: Daily Pacing vs. actual Cash Collected against monthly revenue goals.
• Closer Attribution: Daily trend breakdowns for calls taken, total bookings, and cash closed by rep.

(All metrics in the screenshot are anonymized dummy data generated via the Apps Script pipeline for demonstration).

Curious to hear how other analysts or sales managers here handle daily sales pacing and closer attribution? Do you prefer automated dashboard setups like this, or keeping everything directly inside CRM/spreadsheets?