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#datascience #data #dataanalytics #datascientist
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Data Lake vs Warehouse vs Lakehouse — explained once, properly 👇 Three tools. Three different jobs. Most teams pick the wrong one. 🏞 Data Lake → Store everything raw. Cheap. But without governance
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Most data teams build dashboards. Then wonder why nobody trusts the numbers. The answer is often the same — weak foundations. Here’s what building data systems taught me 👇 🥉 BRONZE — The Landing
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Most data pipelines are built to move data. Very few are built to deliver trust. Data flows. But teams still wait. Models still break silently. Dashboards still show stale numbers. The pipeline i
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Most data pipelines don’t fail because of bad engineers. They fail because of unchecked complexity. I’ve seen it across teams — the pipeline looks great in design, solid in staging, and then somethi
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I’ve shipped data projects that nobody used. Not because the model was wrong. Not because the pipeline broke. Because we answered the wrong problem — really well. Here’s what I wish someone had to
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Most people learn data tools. Not data architecture. Here’s what actually matters 👇 I’ve seen teams spend months picking the “right” tool — Spark vs dbt, Snowflake vs BigQuery, Airflow vs Prefect.
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40% of your analyst’s week is wasted on messy data. Here’s the 4 hidden costs — and a 4-step framework to fix it. Save this 👇 Which cost hits your team most? Start with Step 1 this week. Just the a
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The best data leaders aren’t in dashboards. They’re asking these 5 questions. Which one does your team need most? 👇 Save this. The best data leaders I know spend less time in dashboards than anyone
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70% less reporting time. No new tools. No new hires. Just 4 better questions. Save this — your team needs it. 👇 Which step would help you most? A fintech team was spending 3 weeks every month just p
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Most companies think hiring a data team makes them data-driven. It doesn’t. l’ve seen orgs with 50-person analytics teams where leadership still argues over which number is right in every board meet
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Most teams are measuring data. Very few are measuring impact. We track: – dashboard views – model accuracy – data freshness But rarely: – how fast decisions are made – how often insights lead to act
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About @datageek08

DATA SCIENCE is a Instagram growing creator. With 46 followers and 12 posts, datageek08's account shows a follower-to-following ratio of 1.3:1. View all content anonymously on Pictame - no login required.

datageek08's engagement rate sits at 8.15%. That's 63% above what you'd typically see for accounts with 1K-10K followers (average 5%) — a sign of a genuinely connected audience.

Out of the last 12 posts, 92% are videos and 8% are photos. Content shows up about every 40 days. Captions lean detailed, averaging 963 characters.

8.15% engagement rate — 63% above average for 1K-10K follower accounts.
Posts about every 40 days.
1.3:1 follower-to-following ratio.

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datageek08
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@datageek08

DATA SCIENCE

Public
Posts
12
Followers
46
Top Hashtags
#Analytics#DataEngineering#DataStrategy#DataArchitecture#Databricks#AI#MedallionArchitecture#dbt

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#instagood#instagram#viral#reels#fashion#art#photooftheday#explore#beautiful#nature#travel#follow
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Avg. Comments
0
Engagement
8.70%

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