Learn Data Architecture
A structured collection of FREE tutorials to build your data architecture expertise
Structured Learning Path
Follow our carefully structured learning path to master data architecture concepts step by step
01

Introduction
Data architecture is essential for data scientists. It's the foundation on which data scientists build their models and insights.
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02

Data Types - Structured Data
In this article, we will explore the typical flow of structured data before it reaches the hands of data scientists or business analysts.
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03

Data Types - UnStructured Data
In the realm of data science, unstructured data presents a unique challenge and opportunity for organizations.
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04

Data Types - Semi-Structured Data
In this chapter, we explore the captivating realm of semi-structured data, where tags and markers define hierarchies and structures.
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05

Datawarehousing
The data warehouse serves as the cornerstone for organizing and analyzing structured data.
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07

DataLakeHouse
In this blog post, we will explore the concept of a data lakehouse, which aims to address the challenges associated with integrating structured and unstructured data.
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09

Streaming Data Architecture
In this blog post, I'm going to get you up to speed with streaming data and its profound impact on data science.
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10

Vector Databases
In the vast landscape of data science, vector databases have emerged as a powerful tool for storing and leveraging vector embeddings.
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11

Feature Stores
In this blog post, we'll delve into the concept of feature stores and their pivotal role in machine learning workflows.
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12

Data Contracts: Don't Leave Quality To Chance
In this blog post, we'll explore the concept of data contracts and their importance in ensuring data quality across organizations.
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13

Data Fabric: Realize the Data Mesh
Explore how Data Fabric can help realize the principles of Data Mesh, creating a robust and flexible data architecture for modern enterprises.
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