Youâre designing the next big e-commerce platform, and everything looks greatâuntil your database starts slowing down đ.
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Data is everywhere. Data powers customer management in databases, speeds up websites through caching, and fuels decision-making in executive dashboards.
I know, I know, whatâs the simplest way to share a large dataset with another team?
Hello, all data lovers đ ! Today, I want to talk about a subject that is absolutely crucial for every organization. We all know that data architecture is a broad discipline encompassing data modeling, processing, governance, security, and storage.
Today, weâll take a step back and look at the big picture: how all data components come together to form a unified data architecture.
Data modeling consists of three key layers, each playing a unique role in the lifecycle of data architecture.
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.
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.
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¤!).
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.
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.
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.
In todayâs data-driven world, data teams vary widely in their tooling, infrastructure, and maturity.
Data Mesh is not a product, nor a specific tool. Itâs an organizational model based on four core principles.
A Data Lakehouse builds on the foundation of a Data Lake by adding core warehouse capabilities : transactional updates, etc.
Data Fabric is a logical architecture that unifies access to distributed data systems through virtualization, metadata-driven orchestration, and centralized governance.