u/Economy-Concert-641

BR

Fala, pessoal! Uma dúvida técnica sobre licenciamento de Power BI: alguém consegue resumir como funciona a estrutura de compartilhamento?

Hoje meu cenário é: tenho licença Pro, publico no meu Workspace e uso um Gateway (on-premises) para atualizar dados locais. Sei que, para meu colega ver o dashboard, ele também precisa da licença Pro.

Minha dúvida é sobre escala: se eu precisar compartilhar com uma equipe inteira, a Capacidade Premium é o caminho para que os usuários finais não precisem de licença individual? Como funciona o acesso ao Workspace nesse caso?

Pensando como consultor: se eu for implantar o Power BI do zero em uma empresa, qual é o 'step-by-step' para estruturar os Workspaces e garantir que todos visualizem os dashboards de forma correta e segura? Valeu!

EN

Hey everyone! I have a technical question about Power BI licensing: can anyone summarize how the sharing structure works?

My current scenario is: I have a Pro license, I publish to my Workspace, and I use an on-premises Gateway to update local data. I know that for my colleague to see the dashboard, they also need a Pro license.

My question is about scalability: if I need to share with an entire team, is Premium Capacity the way to ensure end users don't need individual licenses? How does Workspace access work in this case?

Thinking as a consultant: if I were to implement Power BI from scratch in a company, what are the step-by-step instructions for structuring Workspaces and ensuring everyone can view the dashboards correctly and securely? Thanks!

Been using .pipe() in pandas lately and it's been a game changer — anyone else?

I was writing some data transformation code the other day and stumbled across .pipe(). Honestly didn't expect much, but it completely changed how I structure my pipelines.

Instead of this mess:

df_final = sort_by_total(calculate_total(filter_by_price(df)))

You just write it top to bottom like a recipe:

df_final = (

df

.pipe(filter_by_price)

.pipe(calculate_total)

.pipe(sort_by_total)

)

Same result, way more readable. Each function takes a DataFrame and returns a DataFrame — that's the only rule.

Full example if you want to try it:

import pandas as pd

df = pd.DataFrame({

"product": ["Product A", "Product B", "Product C", "Product D"],

"price": [20, 150, 230, 100],

"quantity": [10, 5, 3, 8]

})

def filter_by_price(df):

return df[df["price"] > 100]

def calculate_total(df):

return df.assign(total_value=df["price"] * df["quantity"])

def sort_by_total(df):

return df.sort_values("total_value", ascending=False)

df_final = (

df

.pipe(filter_by_price)

.pipe(calculate_total)

.pipe(sort_by_total)

)

Been using it a lot for ETL and data cleaning workflows. Makes debugging way easier too — just comment out one .pipe() step and you see exactly where things go wrong.

Anyone else using this regularly? Any patterns you've found useful with it?