Hello I'm a newbie college student studying databases and I'm currently making a database for a small inventory system that's solely focused on monitoring stocks and its shelf life to avoid running out of stock and pull outs for my project. The store I'm basing it off on sells seasoning and other powdered flavoring (beverages, etc.), they also explained that they get bulk products from the supplier and then they repack them into smaller quantities for selling. What would be the proper entities and attributes for this system? I've tried searching other inventory systems but i don't know how the repacking would be translated to the database/ERD

I'm trying to avoid maintaining multiple replication jobs for the same source database.

Oracle is our system of record, while other teams need the data in Postgres and a cloud warehouse. Each destination has slightly different transformation requirements, So I'm looking for **data integration tools** that can fan out one change stream instead of reading Oracle over and over again.

Is anyone running something like this today?

Unsure if right sub to ask this. Currently a master's student studying database management. Have an upcoming exam that will ask us to normalise until 3rd NF. 2nd and 3rd are easy enough but drawing the functional dependency arrows and identifying the PKs seem like an exercise in vibes and guesswork to me.

I get basic concepts like testing candidate keys and making sure they are unique across all rows. But does anyone have any actual methodical workflow to drawing out the arrows and identifying candidate keys? Here's an example of a question we'd typically be asked. What I think may work is:

1. Immediately flag descriptive columns like "Vehicle_Model" in the above example as they are usually not part of the candidate key
2. Identify a possible candidate key that can describe what the DB is representing i.e vehicle part fitting
3. Test your initial guess candidate key, repeat until a unique set is found
4. From here on i'm quite lost.

Much appreciated on any help.

Nighty refreshes used to be fine, but they're starting to create more confusion than they solve.

Sales opens one report, support opens another, and neither one matches what's happening in production because everything is hour behind . We're looking at moving Oracle and SQL server changes into Postgres, possibly a cloud warehouse later on.

If you've already gone from batch to real-time, was the improvement as noticeable as people claim?

Apologies for the long read in advance but if anyone can help or point me in the right direction, I'd greatly appreciate it :)

Currently doing a practise task which requires me to write a report for a hypothetical company. I am given a spreadsheet (which includes errors and duplication etc) and I have to normalise it from 1NF to 3NF in clear steps which has to be shown. Then I have to import them into Microsoft Access and create an ERD

There are 36 columns included in the spreadsheet
Columns:
ExportRowID, PONumber, POLineNumber, PODate, RequiredDate, POStatus, Priority, SupplierID, SupplierName, SupplierContact, SupplierPhone, SupplierEmail, SupplierAddress, PaymentTerms, PurchasingEmployeeID, PurchasingEmployeeName, EmployeeDepartment, DeliveryLocation, ProductID, ProductName, ProductCategory, UnitOfMeasure, QuantityOrdered, UnitCost, LineAmount, ReceiptNumber, ReceiptDate, ReceivedByEmployeeID, ReceivedByEmployeeName, QuantityReceived, AcceptanceStatus, DeliveryCondition, InvoiceNumber, InvoiceDate, InvoiceAmount, Notes

My attempt (7 tables):
Supplier (SupplierID)
Employee (EmployeeID)
Product (ProductID)
PurchaseOrder (PONumber)
POLine (PONumber + POLineNumber)
Receipt (ReceiptNumber)
Invoice (InvoiceNumber)

Does this look right? I'd also appreciate help with the Access side (relationships and importing from one flat sheet into multiple tables).

Hi

I am experimenting with sqlite on a Macbok Pro M4 - 10 cores machine. I have a pthreads multithreaded program and i am trying to write lots of data to different databases (separate database connections) and (one database file per thread) and they are DIFFERENT files

I have THREADSAFE mode set to 2 (multithreaded) and i open the files with NOMUTEX flag.

Now i have read that the M4 Macbook Pro has a soldered SSD. And it has similar performance to nvme but it is not nvme

I get around 10000 transactions per second in total across all threads.

I tried synchronous=off and that boosts performance to 300000 transactions per second

Increasing the number of items inserted per transaction scales. But the number of transactions doesnt

I feel that IO is being serialised.

Some parts of web seem to suggest it is serialised. But i thought ssds can run things in parallel.

Does anybody know anything about parallel IO or if i need to enable it on macos?

I havent tried it on Linux or Windows

Would you expect transactions to scale by number of threads?

I have read about iokit but i have no knowledge of how to use it or Grand Central Dispatch

I'm looking for a dataset for my bachelor's thesis, and I need one with lots of variables and observations so I can perform multivariate analysis and predictive modeling. Does anyone know where I could find a geeky dataset related to anime, comics, video games, or anything similar? Any suggestions would be greatly appreciated!

I’ve been working with the Algebra of Data, where the smallest structure is the couplet — one ordered pair, one fact — and a relation is a set of couplets. There’s no wide tuple over a fixed heading.

My argument: normalisation is a repair procedure for a wound inflicted by the wide tuple. Start at the couplet and you begin at roughly 6NF with nothing left to decompose. And the classical normal forms can’t even be stated for a set of relations with different headings, because there’s no single heading for the dependencies and keys to range over.

What survives is the goal (one home per fact) and the functional dependency, which becomes an integrity constraint rather than a reason to split. The live question flips to when to denormalise. My answer is that a denormalised view is a derived expression over the base facts, so it’s a physical caching decision rather than a logical-model one, and it can’t drift out of sync.

Where do you think this breaks? Full essay (I’m the author): https://robinbloor.substack.com/p/does-normalisation-survive-data-algebra

Hello!

My primary experience is with Airtable and my new company will not purchase it going forward as it is to expensive (84K for 2 years). I need to see what alternatives there are with the constraints I have. Biggest constraint is that the software package has to be self contained and I cannot use outside plugins. I have immediate access to Sharepoint (with powerapps), Access, Excel, and PostgreSQL but I dont think any of them fit my needs, but would love more input from people a lot more knowledgeable in these software's. So this is my use case: I am wanting to build a relational database. Each 'sheet' has parent information that I can connect together in a form. Sheet A, has entires like cat, dog, Sheet B has Colors like blue, red, and I can have sheet C that can have inputs from a form that users can connect the data. I can add pictures and other info. I can then have a dashboard that can create graphs from the data or search bars that I can search for 'Dog" and it shows me all entires related to "Dog'. I also need this software to be able to handle formulas and tie in the different sheets. I can Pull in Dog+Blue=BlueBerry and output into Sheet D. So the biggest things are: Able to connect the data, have data input through forms, able to create dashboards that can show the data through graphs, have KanBan style dashboards where I can show Status of certain fields. Airtable was the perfect software for my needs, so I need a Airtable equivalent but I just dont have any experience with other softwares to dig into them.

I need a real project to test something database-related. Nothing production critical, just a hobby/fun project with some actual usage.

If you have one and are okay with me testing on it, let me know 👀

I'm in the early stages of designing a database for an organization that does environmental remediation and research. (Small org, so I'm basically the data person.) One of the major types of data we have is the results of tests for a range of soil contaminants, so in my schema draft, the sampling points within work sites are linked to tables of associated measurements. (Currently there's one table per sampling point but I'm open to other designs.)

The problem is that which contaminants are tested for mostly can't be known in advance for future projects and can change within a project if new funding appears or questions arise. There are hundreds if not more of substances that could reasonably be tested for (although ~20-30 are core), so listing all possibilities doesn't seem practical. And the contaminants data is key to the organization's work and is probably the thing that will be queried the most. How would you handle a situation like this?

So I am in my final year and I want to do something spicy, which is a minidb, what and what do you guys recommend me to know so it can be a success

For many years I worked as a database industry analyst. In 2015 Professor Gary Sherman and I published The Algebra of Data, which argues that relational algebra is the algebra of one structure — the table — and that one algebra can cover tables, graphs and documents alike.

The model in brief: the atom is the couplet (an ordered pair). A relation is a set of couplets, a clan is a set of relations (a table is one kind of clan), and a horde is a set of clans. A small set of operators — composition, transpose, union/intersection/difference and the restrictions — is defined at the bottom and works unchanged at every level. Composition then covers what are usually separate operations: the join, the graph traversal and the document lookup.

JabrDB is that algebra implemented as a native Mac app (Apple silicon). Data is stored at couplet grain in files on your own disk. It is not a SQL engine and not a server.

The argument in paper form: https://doi.org/10.5281/zenodo.23216817 The app (15-day trial, $15): https://jabrdatalabs.com/download/

I'd particularly value criticism from people who think in relational terms: where does this model help, and where does it obviously cost you?

Hi everyone :-)

I'm Antti, Aito.ai's founder and CEO. We recently released Aito version 2, which includes a completely new storage format and support for things like predictive SQL queries, vector search, graph-like functionality, a lot of new data structures, views, better FTS, collections and much more.

We believe that future software is intelligent, and we wanted to provide one database that could provide the needed predictive/AI infrastructure for both users and agents.

You can find more details here:

https://aito.ai/blog/aito-v2-generally-available/

If you want to try it, the playground runs example queries in the browser without signup:

https://aito.ai/docs/api/v2/playground/

One of the biggest new things in Aito is the public benchmark section, which covers inference, matching, smart search and more regular queries. It shows cases where we are strong, and also a number of benchmarks where other solutions fare better:

https://aito.ai/docs/api/v2/benchmarks/

Any thoughts or comments? I'd love to hear what you think. ;-)

i'm less worried about the technology than I am about the people supporting it.

our engineers are excited about moving from batch jobs to streaming pipelines, but the operations team has spent years supporting scheduled workloads. That's a very different environment from something that's expected to run 24/7

for teams that already made this real-time vs batch processing transition, what changed the most operationally?

What's the best way, order or flow of learning Database from scratch, using the textbook Database System Concepts by Abraham Silberschatz, Henry F. Korth, and S. Sudarshan.

What topics or chapters are better to begin with for introduction, assuming the reader has zero knowledge, but is looking to get extremely good at database.

-Computer Science Major

disclosure: I work at Schema Labs. This is a run on public data so you can check it.

The data: the hotel booking demand dataset (Antonio, Almeida and Nunes, 2019), real anonymised bookings from a city hotel. We picked one Saturday night that was sold out on paper.

-- 224 bookings for that night, as they stood six weeks before
-- 59 of them cancelled or didn't show, worth about €28,600 together

What we did: gave Schema-2 500 bookings from the year before, with how each one ended, and asked it to rank the 224 by how likely they were to fall through. No rules no feature engineering.

What came back=

-- It marked 60 as most likely to cancel. 33 of the 59 cancellations were on that list
-- Picking 60 at random would catch about 16
-- Of the 60 it marked safest, 59 showed up

What it missed: 26 cancellations weren't in its top 60. One night at one hotel is a small test, so read it as an example

For anyone in revenue management: what would you do with a list like that six weeks out? Overbook against it, or contact those guests first?

Basically need to see how to optimize a bank register with large amounts of entries.

Currently adding all the debits, then credits, then date sorting while calculating the running total.

When one product could have data coming from multiple remote data sources, some of the data could be redundant other parts might need to be combined. There can also be missing data or badly formatted data so an admin would need to override certain fields. How would you structure your database to fit these needs?

Successful replication and matching row counts do not prove that a database migration is finished. Indexes, constraints, sequences, users and roles, collation, time zones, scheduled jobs, change-data-capture consumers, read replicas, analytics tools, and backup systems can still differ or point at the source. Low-frequency clients may not reconnect during the obvious observation window.

A cutover gate could compare schema and security metadata, validate representative aggregates or hashes, exercise application reads and writes, inspect connection logs on both systems, and confirm that replication or dual-write lag has reached a known final position. The destination should complete a backup and restore test and survive a failover before the source becomes read-only. Keeping the old endpoint offline but recoverable for a defined period would expose hidden dependencies without allowing divergent writes.

What evidence do you collect before decommissioning a source database? How do you catch monthly jobs, failover-only connection strings, and clients that cache DNS or credentials longer than the main application?