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 more convenient to have something different?

A while ago at a previous job, an experienced software architect once suggested, for a transactional system, to separate the transactional DB from a historical DB, and continuously migrate the data differences through ETL's. I have always thought that idea is pretty interesting, so I wonder if it makes sense to try in my little personal project.

If it was you, how would you model personal finance tracking? Is there something you think I may be missing, or that I should look into for DB modeling?

(Note: I intentionally do not track loans / investments, or at least I have not tried to integrate it for the sake of simplicity, and I have no interest in trying YET.)

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 queries that roll back when constraints are violated.
• Explain what triggers are and write SQL to create them.
• Manipulate JSON data using SQL.
• Interact with a database using Python directly, from a Jupyter notebook, and via an ORM.

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 @phiresky@lemmy.world 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

cross-posted from: https://programming.dev/post/252619

> Works with Postgresql, MySql, and SQLite natively. > > Extends confirmed support for DuckDB, ClickHouse, BigQuery, and Snowflake via the vdsql plugin.

> Can you find out whodunnit?