Edem

18 months

@UTDIMole Toni Kronos

Guess the player Very hard.

we’re doing a premiere for the biggest video i’ve ever made. and you’re invited*

We have this flood convo every year but nothing seems to change

Don’t mourn a life that you can still live. “Man is born to live and not to prepare to live.” — Boris Pasternak

The point of a routine is to make progress less dependent on negotiation.

Consistency

@James_adjet True. Got to know most of them because of his BAR tapes

E.L is fond of introducing us to such rap k*llers . The likes of PK,Recognise Ali, Kev&Grenade,Nova Blaq,PHYL4LYPH &A-Clipse,Kobi Onyame & Tradey just to name a few. These guys all be rap k*llers ooo

Can’t choose

He doesn’t force the flow….it just FLOWS!

@Chairman_barima @Shakerthis @1RealJoeyB A tell you. See the one he cook give @PAPPYKOJO recently

@_edem19 @Shakerthis @1RealJoeyB Shaker de3 king hooks oooo

@1RealJoeyB and @Shakerthis have a unique way of making catchy and humorous hooks and I love it!

You want to compete globally with a download speed of 500kb/s? 😂😂

@Skill1ssueFT @_nanawan @Eddyszn09 It’s crazy 😂😂

@_edem19 @_nanawan @Eddyszn09 Logical argument don't exist on this app. Somebody also mentioned Zuckerberg being a billionaire at 23 hence they can afford. Bruh? How does that make sense? How many billionaires are in the world saf.

Ebi oman fon nti you Dey see am strange nu 😂😂😂

Sarcasm😂

@Skill1ssueFT @_nanawan @Eddyszn09 It’s crazy how they are using outliers to support their argument 😂

@_nanawan @Eddyszn09 If the country you have in mind is the US then you must be delusional The average 22 year old American is soaked in student loan I don't know who lied to you guys. Most Americans are living on credit but I'm supposed to believe the average 22 year old can buy a Corvette

@AndyNosretep @tosinolaseinde Oh yeah. This just goes to show that ideas are not fully formed in the beginning or at the outset. The most important thing is to start and not obsess over every detail.

@tosinolaseinde Nvidia CEO, Jensen Huang, said the same. That, if he knew how difficult the Nvidia journey would be, he would not have done it.

“Luckily for me, we didn’t know what we were building, because if I’d known, we’d have chickened out.” Dangote

@gaagana_ You are doing great and it’s inspiring

Progress🥹🥲📊 But this looks so simple for something I spent an entire day doing like…😂😭 So I built a MLR model using an insurance dataset from Kaggle, and I hosted it on streamlit(trust me I didn’t even know it existed until today) there’s so much to learn, and it’s so humbling😌. Try it out: glicoinsurancecost.streamlit.app

Data Nerds! I ranked every data engineering tool by how often it shows up in 4M+ job postings. 📊 But here's the catch 😳. Some critical skills show up way less than they should because they're often assumed to be foundational skills for jobs. (e.g., Skills like Bash/Terminal for running pipelines) Anyway, here's the breakdown of the tiers 👇 (Note: % = how often each tool appears in DE job postings) 🔴 S TIER — Non-Negotiable The core skills needed for any DE job. Don't apply without these: 📊 SQL (~68%) — every warehouse runs on it. Query, transform, and model data. 🐍 Python (~67%) — the pipeline language. Ingestion, automation, APIs, glue between systems. ⌨️ Terminal/Bash (~11%) — every tool you'll use runs from here. This is highly undervalued in postings. 📁 Git (~11%) — version control. Every team uses it. Same posting-% caveat as Bash. ☁️ One cloud platform + warehouse (~26-46%) — AWS + Redshift, GCP + BigQuery, or Azure + Synapse. Combined cloud presence is in nearly every posting. Start with SQL, then Python. Everything else you absorb alongside them. 🟠 A TIER — Job-Ready Foundation The tool that closes the gap from "learning DE" to "hireable for modern stacks": 🪛 dbt (~10%) — only 10% of all DE postings, but 36% in Analytics Engineer (AE) roles. That's not a niche, it's a leading indicator. AE is the new hybrid role modern data teams are hiring for: part analyst, part engineer. ✅ Land the job with S + A. Pass the interview with conceptual knowledge of B Tier 👇 🟡 B TIER — Interview-Aware Know what they solve. Don't expect to code from scratch: ⚙️ Airflow (~17%) — orchestration. Built on DAGs (directed acyclic graphs). ⚡ Spark (~38%) — distributed computing for processing large datasets. 🌊 Kafka (~19%) — real-time event streaming between systems. All these depend on a foundational knowledge of Python & SQL; don't jump the gun learning these. 🟢 C TIER — Data Platform Awareness Pick the one your company uses. Understand both conceptually: ❄️ Snowflake (~26%) — pure SQL warehouse. Optimized for analytics. Modern-stack favorite. 🧱 Databricks (~24%) — lakehouse on Spark. Handles structured + unstructured. ML/AI heavy teams. 🔵 D TIER — Versatility Multipliers Lower headline demand, but high value per hour: 📊 Power BI (~15%) / Tableau (~10%) — but the kicker: in AE roles these jump to 28% / 33%. Modern data teams want pipeline builders who can also visualize. For analysts pivoting to DE, lead with this in interviews. 🟣 E TIER — Path-Dependent High demand on paper, but concentrated in legacy enterprise stacks. Skip until your job requires it: ☕ Java (~25%) — legacy enterprise data infrastructure ⚖️ Scala (~22%) — Spark's native language. Spark-heavy shops. 🎥 How did I derive this ranking? In my latest video, I walk through the concepts first (the DE lifecycle, what each tool actually solves) and then derive the tiers. (Link in comments 👇)

@Tech_p001 Indifferent

DATA ENGINEERS ONLY How would your team react if AI coding tools disappeared tomorrow?

@Asantesamuels @AtindanaVictor1 @icgroupofficial Liquidity in the mud

@AtindanaVictor1 my buy on @icgroupofficial has never been excuted for months now...always pending and then nothing...mtchew.

The year Calbank Plc share will do a 1000% increase, Ghana cannot count young millionaires...