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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

Derniers posts

Given enough time all warehouses will look the same 2.4K views · 23 likes · 0 reposts · 2 replies Open on X →
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 →
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 →
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 →
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_djo
@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_djo
@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_djo
@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_djo
#duckdb parquet writer is embarrassingly fast. https://t.co/zvQ2hUoMvo
33.6K views · 124 likes · 4 reposts · 8 replies Open on X →

Face aux comptes de taille comparable

5 posts des 90 derniers jours, à côté de la tranche de 10K–100K abonnés. diffusé largement, mais peu de ces spectateurs réagissent.

Vues médianes1 530ce compte924médiane pour 10K–100K
Portée, %12.25%ce compte3.62%médiane pour 10K–100K
Engagement, %1.03%ce compte1.52%médiane pour 10K–100K
IndicateurCe compteMédiane pour 10K–100KRapport
Vues médianes par post1 5309241.66×
Portée (vues ÷ abonnés)12.25%3.62%3.38×
Taux d'engagement1.03%1.52%0.68×

Autres comptes de cette tranche →   Comparer avec un autre compte →   Comment ces repères sont établis →

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.

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