$38B compound startup that no one understands
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How Databricks grew to $38B as a compound startup
TLDR about data engineering tools:
A Data Lake is like a big lake that holds all kinds of water, or in this case, information.
A Data Lakehouse is like a special house by the lake that takes some water and sorts it into clean, organized rooms.
A Data Warehouse is like a big building that only keeps water that's super clean and carefully sorted into bottles.
Each way helps people find the information they need but in different ways. And Databricks does all of that.
Data Lake vs. Data Lakehouse vs. Data Warehouse
Databricks started by building open-source software with a self-serve motion. A significant breakthrough came once they adjusted their positioning towards enterprises and hired a bunch of sales reps (with a $350k salary in 2017!). This opened a new distribution channel to sell more data products to the same customers.
The data engineering software market is worth $70B and growing 15% annually. No wonder they managed to get a $38B valuation with the growth of a need for AI operations (AI needs a ton of data to be helpful!).