A Hybrid Data Warehouse unifies two complementary ecosystems. It combines the structure and governance of a Data Warehouse for BI, with the scalability of a Data Lake for big data, ML, and diverse formats.
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Traditional data warehousing follows a structured, schema-on-write approach. Data is ingested through ETL pipelines into a centralized data warehouse, often complemented by downstream data marts.
Every day, your data lake ingests 20 million client records. If you store all these records, you will accumulate around 7.3 billion records per year (and Jeff Bezos will definitely love charging you for that đ¤!).
Cause youâre a sky, full of stars, Iâm gonna give my heart.” đś Today weâll be exploring the galaxies and the stars of data modeling.
At its core, normalization follows the principle of Donât Repeat Yourself (DRY). This ensures that each piece of data is stored only once, reducing redundancy and improving data integrity.
I know, I know, whatâs the simplest way to share a large dataset with another team?
Data modeling consists of three key layers, each playing a unique role in the lifecycle of data architecture.
In todayâs data-driven world, data teams vary widely in their tooling, infrastructure, and maturity.
Hello, all data lovers! Today, I want to talk about a subject that is absolutely crucial for…
Youâre designing the next big e-commerce platform, and everything looks greatâuntil your database starts slowing down đ.
Data is everywhere. Data powers customer management in databases, speeds up websites through caching, and fuels decision-making in executive dashboards.
The massive amount of data a company generates daily. Every time you buy something online, swipe your credit card, or even just browse a website, youâre generating data.