If you have an already up-and-running Typer app, you might want to extend it to some sort of GUI (Graphical User Interface).
While building a full GUI can be time-consuming, there is a really fast utility which can be adopted to get somewhere in between.
There are several recommended options to perform a cross-account full copy of a DynamoDB table.
It seems reasonable to compare these options based on several different properties: setup time (low is better), approach (serverless is better), involved resources (few is better), costs estimate (low is better) and new code possibly required (none is better).
Over the past two years I had the chance to read a bunch of data-related books on the O'Reilly learning platform: what follows is a collection of quotes from "Fluent Python, 2nd Edition" by Luciano Ramalho that captured my attention or hit hard because they were highly relatable.
Over the past two years I had the chance to read a bunch of data-related books on the O'Reilly learning platform: what follows is a collection of quotes from "Robust Python" by Patrick Viafore that captured my attention or hit hard because they were highly relatable.
Over the past two years I had the chance to read a bunch of data-related books on the O'Reilly learning platform: what follows is a collection of quotes from "Tidy First?" by Kent Beck that captured my attention or hit hard because they were highly relatable.
Se si usano window functions (es. LAG) per calcolare delta tra timestamp Unix, il risultato "ignora" eventuali switch di ora legale/solare (correttamente, dato che un timestamp Unix conta esclusivamente il tempo che scorre).
For all the Python developers working on AWS: have you ever wanted to easily read a DynamoDB table directly into a pandas DataFrame?
Check out the latest release of AWS SDK for pandas (formerly AWS Data Wrangler), where I contributed with a brand new read method for DynamoDB module!
Nell'utilizzo avanzato di AWS CDK, si realizza prima o poi l'esigenza di centralizzare alcune logiche e risorse in maniera da poterle riutilizzare in maniera rapida, riducendo il codice boilerplate. Anche se una prima analisi potrebbe suggerire che la soluzione sia l'implementazione di una "casalinga" factory - pythonica sì, ma non conforme alle best practices di CDK - la risposta probabilmente più corretta potrebbe riguardare l'implementazione di un costrutto L3, descritto come:
designed to help you complete common tasks in AWS, often involving multiple kinds of resources.
Main databases types: