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Why write separate INSERT and UPDATE queries… when one MERGE can do everything? Here’s the magic πŸ‘‡ MERGE target t USING source s ON t.id = s.id WHEN MATCHED THEN UPDATE SET t.name = s.name WHEN N
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Self Join looks confusing… but it’s actually simple. It’s just using the SAME table twice to find relationships. Example: Employee β†’ Manager πŸ‘‡ SELECT e1.name AS employee, e2.name AS manager FROM e
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You send money in 5 seconds… but what actually happens behind the scenes? πŸ“± You click β€œPay” πŸ”΅ UPI connects your bank 🏦 Bank verifies balance & PIN πŸ’Έ Money moves instantly βœ… Both get confirmation
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Every app you use runs on a data pipeline. You click β†’ data is generated API sends it β†’ stored in database Processed β†’ turned into insights Shown on dashboards Simple flow. Massive impact. This is
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Every time you use an app… you’re using an API. You just don’t see it. Order food β†’ API sends request Check balance β†’ API fetches data Login β†’ API verifies you It’s just a messenger between: πŸ“± App
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Every time you order food, you’re using OLTP. Every time a company analyzes sales, it’s OLAP. Same data. Different purpose. πŸ‘€ OLTP β†’ handles real-time user actions πŸ“Š OLAP β†’ turns data into insigh
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Most people think Data Lake, Warehouse, and Lakehouse are just β€œdifferent storage systems”. That’s wrong. They’re actually an evolution πŸ‘‡ 🌊 Data Lake β†’ Store everything (raw, messy, cheap) 🏒 Da
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Most people use Airflow… without understanding what actually runs under the hood. A DAG is not just a diagram β€” it’s the brain of your data pipeline. Every node = a task Every arrow = a dependency
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Most data pipelines fail because they mix everything in one layer. Raw + cleaned + business logic = chaos. Medallion architecture fixes that πŸ‘‡ πŸ₯‰ Bronze: Raw data (no filtering) πŸ₯ˆ Silver: Cleane
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Most people don’t actually see the difference between Star and Snowflake schema. They just memorize definitions. Here’s the truth πŸ‘‡ ⭐ Star Schema: - One fact table - Direct dimension tables - Few
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Most people think ETL and ELT are the same. They’re not. The real difference? πŸ‘‰ It’s NOT about tools πŸ‘‰ It’s about where transformation happens ETL = Transform BEFORE storing data ELT = Transform
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Everything you need to know about BigQuery β€” explained on a whiteboard πŸ–ŠοΈ Four layers. One engine. Zero infrastructure to manage. Save this before your next data interview πŸ‘‡ Whether you're a data en
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About @data.engg

Data Engineering in 60 seconds is a Instagram growing creator. With 1 followers and 12 posts, data.engg's account shows a follower-to-following ratio of 0.5:1. View all content anonymously on Pictame - no login required.

data.engg's engagement rate sits at 83.33%. That's 1567% above what you'd typically see for accounts with 1K-10K followers (average 5%) β€” a sign of a genuinely connected audience.

Out of the last 12 posts, 67% are videos and 33% are photos. Notably, videos tend to get 2.0x more engagement than photos. Captions lean detailed, averaging 522 characters.

83.33% engagement rate β€” 1567% above average for 1K-10K follower accounts.
67% videos, 33% photos. Videos get 2.0x more engagement here.

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@data.engg

Data Engineering in 60 seconds

Public
Posts
12
Followers
1
Top Hashtags
#DataEngineering#SQL#BigQuery#DataWarehouse#ETL#DataPipeline#dataengineering#dataengineer

Explore the most impactful hashtags used in recent posts. These tags highlight the core interests and trending topics that define this profile's digital influence and content strategy.

Following
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Avg. Comments
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Engagement
100.00%

Data Engineering made visual Pipelines | Systems | Concepts Follow to actually understand data