I am one of the developers on a very small team and have just found the following query

I would love to hear your ideas for what you think was being attempted here!

SELECT ... FROM client WHERE CAST(ABS(SIN(clientId)) AS BIT) = 0

I found this SQL style guide which looks good. I'm not a pro at SQL but it looks good to me. I mean, you use the style guide your boss wants you to use, or what all others use, but what if you could choose?

TL;DR?

PRAGMA journal_mode = WAL;
PRAGMA busy_timeout = 5000;
PRAGMA synchronous = NORMAL;
PRAGMA cache_size = 1000000000;
PRAGMA foreign_keys = true;
PRAGMA temp_store = memory ;

Hello! Let me first clarify, this is for a personal project, based on an idea I always use to learn all kinds of things: personal finance tracking.

The DB model I typically use looks something like this:

Initially, I made the decision to separate incomes, expenses and transfers into separate tables, which makes sense to me, according to the way I learned DB normalization.

But I was wondering if there is any benefit in somehow mixing the expense and income tables (since they are almost identical, and any code around these is always almost identical), or even all 3 (expense, income and transfer). Maybe it is more convenient to have the data modeled like this this for an API, but for BI or analytics, a different format would be more convenient? How would such format look like? Or maybe this would be better for BI and analytics, but for an API it's m

Is there a programming language specifically designed for interacting with SQL databases that avoids the need for Object-Relational Mappers (ORMs) to solve impedance mismatch from the start?

If such a language exists, would it be a viable alternative to PHP or Go for a web backend project?

cross-posted from: https://programming.dev/post/10749238

cross-posted from: https://programming.dev/post/10707322

cross-posted from: https://programming.dev/post/10707319

In this article, we want to share our experience with fellow developers and offer insights using real-life examples on how to identify and optimize slow SQL queries, especially when working with relational database management systems like PostgreSQL, MySQL, MSSQL, Oracle, etc.

intended audience

• Rachel has a master’s degree in cell biology and now works in a research hospital doing cell assays.
• She learned a bit of R in an undergrad biostatistics course and has been through the Carpentries lesson on the Unix shell.
• Rachel is thinking about becoming a data scientist and would like to understand how data is stored and managed.
• Her work schedule is unpredictable and highly variable, so she needs to be able to learn a bit at a time.

prerequisites

• basic Unix command line: cd, ls, * wildcard
• basic tabular data analysis: filtering rows, aggregating within groups

learning outcomes

• Explain the difference between a database and a database manager.
• Write SQL to select, filter, sort, group, and aggregate data.
• Define tables and insert, update, and delete records.
• Describe different types of join and write queries that use them to combine data.
• Use windowing functions to operate on adjacent rows.
• Explain what transactions are and write quer

I’ve started to take an intro SQL class but I want to find more places to practice. Do any of you have recommendations for sites that I can use to practice creating queries based on pieces of information someone is looking for?

Any other advice to give to a brand new SQL learner?

Thanks in advance for any guidance.

Edit: Thanks a ton for the responses, I really appreciate it. I’ve bookmarked these pages and started to go through these sites.

Looks like @[email protected] is looking for reviews on their latest optimizations to the Lemmy backend. Figured folks here might be interested in taking a look.

A relatively simple but common application of time series done with PG.

cross-posted from: https://programming.dev/post/700753

I tried looking the original Joint USSS/FBI Advisory shown in the end of the video, and found these:

• https://web.archive.org/web/20150910021244/https://usa.visa.com/download/merchants/20090212-usss_fbi_advisory.pdf
• https://www.researchgate.net/publication/346975125_Heartland_Data_Breach_Analysis