The dataset spans over 175 years of performance history, covering 11.000 unique works and 20.000 performances. For this, we will leverage the New York Philharmonic dataset containing all their concerts since the Philharmonic’s first concert at the Apollo Room conducted by Ureli Corelli Hil on December 7, 1842.
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In this blog post, we will explore some of the key JSON and array functions available in SQL, including Lambda functions, and how to use them to manipulate semi-structured data. Parts Overture: The dataset, setup, and loading data into Firebolt Sonata: Analyzing the raw data Minuet: Array manipulation in SQL, including Lambda functions and flattening data Allegro: Visualizing data with Streamlit & Metabase Finale: Conclusions From ingesting and processing to visualizing the data with two popular frameworks, Streamlit and Metabase, this guide will provide you with the fundamental knowledge necessary to handle semi-structured data effectively. Specifically, we will examine the JSON format, a widely adopted standard for representing semi-structured data, and discuss how to harness its potential in building data applications. In this introductory guide, we will explore the realm of semi-structured data, focusing on its handling and management in the context of databases. One such data type is semi-structured data, which holds a unique position in the data ecosystem, blending the features of both structured and unstructured data. As the data landscape evolves, businesses encounter a wide range of data types, from structured to unstructured and everything in between.
In today's data-driven world, organizations are constantly generating, analyzing, and utilizing massive amounts of data to fuel their businesses.
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