What is Data warehouse?Data warehouse kya hai?

🧸 Think of it likeAise samjho

Like a big, well organised godown of a company's history, arranged so you can quickly answer questions like 'sales by city last year'.Company ke itihaas ka ek bada, saaf suthra godown jaisa, aise lagaaya ki 'pichhle saal shehar ke hisaab se sales' jaise sawaal jaldi answer ho jaayein.

📘 ExplainedVistaar se

Warehouses store data in columnar form so analytics queries over millions of rows are fast. Teams load raw data into them (ELT), model it into fact and dimension tables, and use it for dashboards, metrics and ML training data. Lakehouses (like Databricks) combine warehouse features with cheap file storage.Warehouses data ko columnar roop mein rakhte hain taaki laakhon rows pe analytics queries tez chalein. Teams raw data load karti hain (ELT), use fact aur dimension tables mein model karti hain, aur dashboards, metrics aur ML training data ke liye use karti hain. Lakehouses (jaise Databricks) warehouse ke features ko saste file storage ke saath jodte hain.

💡 Real exampleAsli example

Running 'monthly revenue by city' over 3 years of orders in seconds.3 saal ke orders pe 'shehar ke hisaab se monthly revenue' seconds mein chalana.

📖 Learn it step by stepStep by step seekho

• Data modelling: designing tables for analysisData modelling: analysis ke liye tables design karna
• Data pipelines: ETL, Airflow, dbt and SparkData pipelines: ETL, Airflow, dbt aur Spark

🔗 Related termsJude hue shabd

• ETL and ELTWays to move data: transform before loading (ETL) or load raw data then transform inside the warehouse (ELT).Data le jaane ke tarike: load se pehle transform (ETL) ya raw data load karke warehouse ke andar transform (ELT).
• Data pipelineAutomated steps that move data from sources, clean and transform it, and deliver it for reports or models.Apne aap chalne wale steps jo sources se data laate, saaf aur transform karte, aur reports ya models tak pahunchaate hain.