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How to use nested data types effectively in SQL

Start Data Engineering

Using nested data types in data processing 3.3.1. STRUCT enables more straightforward data schema and data access 3.3.2. Nested data types can be sorted 3.3.3. Use STRUCT for one-to-one & hierarchical relationships 3.2. Use ARRAY[STRUCT] for one-to-many relationships 3.3.

SQL 130
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Improving Meta’s global maps

Engineering at Meta

This new data schema was born partly out of our cartographic tiling logic, and it includes everything necessary to make a map of the world. Daylight ensures that our maps are up-to-date and free of geometry errors, vandalism, and profanity.

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Practical Magic: Improving Productivity and Happiness for Software Development Teams

LinkedIn Engineering

We discuss the difference between “data” and “insights,” when you want to use qualitative (objective) data vs. qualitative (subjective) data , how to drive decisions (and provide the right data for your audience), and what data you should collect (including some thoughts about data schemas for engineering data).

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Implementing the Netflix Media Database

Netflix Tech

A schemaless system appears less imposing for application developers that are producing the data, as it (a) spares them from the burden of planning and future-proofing the structure of their data and, (b) enables them to evolve data formats with ease and to their liking. This is depicted in Figure 1.

Media 97
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Data News — Week 22.45

Christophe Blefari

Modeling is often lead by the dimensional modeling but you can also do 3NF or data vault. When it comes to storage it's mainly a row-based vs. a column-based discussion, which in the end will impact how the engine will process data.

BI 130
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Data-Oriented Programming with Python

Towards Data Science

Lookup time for set and dict is more efficient than that for list and tuple , given that sets and dictionaries use hash function to determine any particular piece of data is right away, without a search. The existence of data schema at a class level makes it easy to discover the expected data shape.

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Serverless Data Pipelines On DataCoral

Data Engineering Podcast

How does the concept of a data slice play into the overall architecture of your platform? How do you manage transformations of data schemas and formats as they traverse different slices in your platform? How does the concept of a data slice play into the overall architecture of your platform?