Mim

@mim_djo · Brisbane, Queensland · joined 09 Nov 2016

#MicrosofFabric user advocate, interests in Small Data & Self Service #Microsoftemployee since Dec 2023 , but my tweets are my own

12 493Followers

3 114Following

37 957Posts total

43.2KViews on collected posts

Últimas publicaciones

Mim@mim_djo12 Sep

Given enough time all warehouses will look the same 2.4K views · 23 likes · 0 reposts · 2 replies Open on X →

Mim@mim_djo9 Sep

Writing Databricks Parquet That VertiPaq Likes. One disclaimer: I am not a Spark person. I know delta-rs much better, so if I got something wrong in the Spark configuration, please be nice 😁 #onelake #powerbi #databricks https://t.co/nXXXB0tirq 1.3K views · 20 likes · 0 reposts · 0 replies Open on X →

Mim@mim_djo8 Sep

I took the executive decision to reduce the default row group size in my vibe-coded apps that have at least two users (me and a German dude) from 6M to 4M. Thanks to your attention :) https://t.co/gQUPNEzoMt 851 views · 8 likes · 0 reposts · 0 replies Open on X →

Mim@mim_djo6 Sep

i am very sad to report that even fable 5.1 can't fully understand the nuance of spark configuration https://t.co/X3HJeab0qO

GIF

2.2K views · 37 likes · 0 reposts · 9 replies Open on X →

Mim@mim_djo23 Jul

querying #onelake Iceberg catalog from your browser #apacheiceberg #wasm #lakehouse #MicrosoftFabric #duckdb https://t.co/9kWpOxerh9 1.5K views · 5 likes · 0 reposts · 1 replies Open on X →

Mim@mim_djo6 Oct 2024

@mim_djo This is very cool research. Are the compression settings the same? This might impact throughput quite a bit 126 views · 2 likes · 0 reposts · 0 replies Open on X →

Mim@mim_djo3 Oct 2024

@mim_djo how do you transform files into a delta table with duckdb? 631 views · 1 likes · 0 reposts · 1 replies Open on X →

Mim@mim_djo3 Oct 2024

@mim_djo Pandas chokes and sputters out at a large number of files? 🐢 573 views · 2 likes · 0 reposts · 0 replies Open on X →

Mim@mim_djo2 Oct 2024

#duckdb parquet writer is embarrassingly fast. https://t.co/zvQ2hUoMvo

33.6K views · 124 likes · 4 reposts · 8 replies Open on X →

Frente a cuentas del mismo tamaño

5 publicaciones de los últimos 90 días, junto al rango de 10K–100K seguidores. llega a mucha gente, pero pocos de esos espectadores reaccionan.

Visualizaciones medianas1 530esta cuenta924mediana de 10K–100K

Alcance, %12.25%esta cuenta3.62%mediana de 10K–100K

Interacción, %1.03%esta cuenta1.52%mediana de 10K–100K

Métrica	Esta cuenta	Mediana de 10K–100K	Proporción
Visualizaciones medianas por publicación	1 530	924	1.66×
Alcance (visualizaciones ÷ seguidores)	12.25%	3.62%	3.38×
Tasa de interacción	1.03%	1.52%	0.68×

Otras cuentas de este rango → Comparar con otra cuenta → Cómo se construyen estas referencias →

Growth & engagement

How the posts we collected actually performed: views and reaction rate post by post, what the audience did with them, and where the follower count goes.

Views per post

33.6K2 Oct

5733 Oct

631

1266 Oct

1.5K23 Jul

2.2K6 Sep

8518 Sep

1.3K9 Sep

2.4K12 Sep

Last 9 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.

Engagement rate per post

0.41%2 Oct

0.35%3 Oct

0.32%

1.59%6 Oct

0.39%23 Jul

2.07%6 Sep

0.94%8 Sep

1.57%9 Sep

1.03%12 Sep

Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 1.52%.

What the audience does

Likes75.5%222 in total

Reposts1.4%4 in total

Replies7.1%21 in total

Bookmarks16.0%47 in total

Share of every reaction we collected for this account. Replies mean argument, reposts mean endorsement, bookmarks mean the post was worth keeping.

The follower curve appears once this account has two daily snapshots — we take one a day, and this one is on its first.