u/YeeduPlatform

This tournament was supposed to be the Messi-Haaland-Mbappe show. These people had other plans.

Vozinha (Cape Verde GK, 40) : 7 saves, clean sheet against Spain's 27 shots, Player of the Match on World Cup debut. Went from 50K to 12 million Instagram followers overnight. His post match quote broke the internet harder than the saves did.

Eloy Room (Curaçao GK, 37) : 15 saves against Ecuador, second only to Tim Howard's all time World Cup record of 16. Had 100K followers with 10 minutes left on the clock. Hit 1 million by morning.

Tim Payne (New Zealand, Wellington Phoenix) : Had under 5,000 Instagram followers before an Argentine influencer called him the "least known player at the World Cup." Hit 5.9 million before he had even kicked a ball, then went and actually assisted New Zealand's first goal.

Elijah Just (New Zealand, Motherwell FC) : Plays in the Scottish Championship. Scored twice against Iran on the World Cup stage. Nobody outside New Zealand football had heard this name two weeks ago.

Yasin Ayari (Sweden, Brighton) : Everyone expected Isak or Gyokeres to carry Sweden. Ayari came out and scored two absolute rockets against Tunisia instead.

Johan Manzambi (Switzerland, 20 years old) : Came off the bench against Bosnia with the game at 0-0, scored with his first touch, then scored again. Youngest sub ever to score twice in a World Cup match.

Deniz Undav (Germany, Stuttgart) : 3 goals and 2 assists in this tournament. Every single one as a substitute. Germany have scored 9 goals and this man hasn't started a single game.

Jonathan David (Canada) : Hat-trick in Canada's 6-0 demolition of Qatar, their biggest ever World Cup win. Had a rough season at Juventus. Didn't show on home soil.

Ayyoub Bouaddi (Morocco, 18) : 90 minutes against Brazil, completed 91% of his passes, won 9 of 13 ground duels. Instagram jumped from 135K to 1.1 million in 48 hours.

The Messi-Haaland-Mbappé trio are expected to produce goals. Nobody predicted the 2026 World Cup's biggest stories would be a pair of veteran goalkeepers from island nations and a New Zealand right back from Wellington Phoenix.

Who else do you think had an impact on and off the pitch this FIFA 2026.

This tournament was supposed to be the Messi-Haaland-Mbappe show. These people had other plans.

Vozinha (Cape Verde GK, 40) : 7 saves, clean sheet against Spain's 27 shots, Player of the Match on World Cup debut. Went from 50K to 12 million Instagram followers overnight. His post match quote broke the internet harder than the saves did.

Eloy Room (Curaçao GK, 37) : 15 saves against Ecuador, second only to Tim Howard's all time World Cup record of 16. Had 100K followers with 10 minutes left on the clock. Hit 1 million by morning.

Tim Payne (New Zealand, Wellington Phoenix) : Had under 5,000 Instagram followers before an Argentine influencer called him the "least known player at the World Cup." Hit 5.9 million before he had even kicked a ball, then went and actually assisted New Zealand's first goal.

Elijah Just (New Zealand, Motherwell FC) : Plays in the Scottish Championship. Scored twice against Iran on the World Cup stage. Nobody outside New Zealand football had heard this name two weeks ago.

Yasin Ayari (Sweden, Brighton) : Everyone expected Isak or Gyokeres to carry Sweden. Ayari came out and scored two absolute rockets against Tunisia instead.

Johan Manzambi (Switzerland, 20 years old) : Came off the bench against Bosnia with the game at 0-0, scored with his first touch, then scored again. Youngest sub ever to score twice in a World Cup match.

Deniz Undav (Germany, Stuttgart) : 3 goals and 2 assists in this tournament. Every single one as a substitute. Germany have scored 9 goals and this man hasn't started a single game.

Jonathan David (Canada) : Hat-trick in Canada's 6-0 demolition of Qatar, their biggest ever World Cup win. Had a rough season at Juventus. Didn't show on home soil.

Ayyoub Bouaddi (Morocco, 18) : 90 minutes against Brazil, completed 91% of his passes, won 9 of 13 ground duels. Instagram jumped from 135K to 1.1 million in 48 hours.

The Messi-Haaland-Mbappé trio are expected to produce goals. Nobody predicted the 2026 World Cup's biggest stories would be a pair of veteran goalkeepers from island nations and a New Zealand right back from Wellington Phoenix.

Who else do you think had an impact on and off the pitch this FIFA 2026.

There’s a clear shift happening in the data world: newer engines like DuckDB, Polars, and DataFusion are deliberately avoiding the JVM. This isn’t just an implementation choice—it reflects a deeper change in how performance is defined. Systems like Apache Spark were built around scaling out across clusters. But modern engines are optimizing for something else: how efficiently a single core can execute work. 

That shift leads directly to hardware-aware execution. Native engines (C++/Rust) can fully exploit SIMD, operate on columnar memory, and avoid garbage collection entirely. The JVM, while great for general-purpose systems, introduces overhead exactly where analytical workloads are sensitive—GC pauses, object-heavy memory layouts, and limited vectorization. The result is a hard ceiling on per-core performance that scaling alone can’t fix. 

What’s emerging isn’t “Spark vs new engines,” but a separation of roles: 

• Spark for orchestration, distribution, and fault tolerance  
• Native engines (like Velox or Apache Arrow) for execution

  

The interesting question now isn’t whether the JVM is good or bad—it’s whether execution efficiency is becoming more important than abstraction in modern data systems. 

If you’ve been looking up how to reduce Spark costs, why Databricks bills spike, or how to scale data pipelines without overspending, you’ve probably hit this pattern: everything works, jobs are stable, data is growing… and yet your cloud bill grows faster than your platform. So the real question is: are you actually scaling workloads, or just paying more to run the same patterns at larger volume? 

• Does more data automatically trigger bigger clusters instead of smarter execution?  
• Are your jobs finishing slower than they should for the hardware you’re paying for?  
• Is “keeping clusters ready” quietly turning into always-on cost leakage?  
• Does running pipelines more frequently feel like a budget trade-off?  
• Are you choosing platforms based on convenience instead of cost-to-performance fit?

  

At some point, optimization tweaks stop working because the issue isn’t configuration — it’s how your Spark workloads are structured and executed. Until that shifts, cost growth isn’t accidental… it’s inevitable. 

Most of us run into (yet another) Spark job that looked perfectly fine but kept dragging in production. Spark UI shows a few tasks running forever while everything else finished quickly. Turned out to be data skew, again.  

While digging into it, I ended up writing down what I wish I had checked earlier before running the job. Mostly around how data skew actually shows up during execution, why it’s so easy to miss in dev, and what signals to look for before things go sideways. 

Lol. I wrote a blog about it and published in medium as well.

The DBU rate looks fine on paper until autoscaling kicks in. Databricks' Intelligent Workload Management scales aggressively and you have no real control over it. Add the idle warm pool baked into every DBU (you're paying for readiness even between jobs) and the mandatory Premium tier just to access Serverless — and what looked like ~$1,700/month for a 50 jobs/day workload became $5,000+ in practice. No Spot Instance access either, so that 60–90% EC2 discount AWS offers? Gone.

We ended up benchmarking Yeedu Warm Start as an alternative. Flat license, jobs run in your own VPC, and their Turbo Engine cuts job duration ~5x so you're burning far less compute per run. At low job volumes Databricks still wins on paper, but the crossover hits around 270–300 jobs/day — which for most active data teams is 6–12 months of normal growth away. Sharing in case others are trying to make sense of a surprise bill.