Introduction

1. Introduction

Hello. My name is Elliot. I will be your instructor with this case study using Databricks.

2. Overview

Databricks is a cloud-based platform for data analytics, combining big data processing with tools for querying and visualizing data. It enables teams to analyze large datasets efficiently, collaborate on data projects, and create dashboards, all in one workspace. In this case study you will be working as a consultant data analyst at a consultancy company called Data X, a firm specializing in advanced data analytics solutions. So who will be your client? Your client is Airbnb. They have tasked us with analyzing one of their most active markets, New York City. As a consultant, your goal is to help them to understand its performance and the customer behavior in this market. Why we choose New York City? Because it offers a diverse dataset with thousands of listings, which present us with a unique opportunity to answer questions like: How do prices vary across different areas? What type of properties are most popular in each borough?

3. Goal of the case study

So your key objectives will include understanding the Airbnb dataset structure, analyzing the listings and the reviews to identify trends, provide insights for pricing, customer experience and operational strategies. By analyzing the data, you will provide our clients with insights to improve pricing, customer experience, and other strategic decisions.

4. Dataset overview

Next, let's explore the dataset provided by Airbnb in New York City, which is divided into two sets. The first asset is the property listing table. This table provides details about each property, including price, location, property type, and host information, across New York's five boroughs. The second data is our customer reviews table. This captures the data on customer reviews, including number of reviews, scores, last reviewed date and additional metrics related to hosts.

5. Get started!

Now let's connect to our Databricks workspace and start our exploration. In the provided Databricks workspace, we'll start by exploring the structure of these datasets and identifying key variables for analysis.

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Case Study: Data Analysis in Databricks

AdvancedSkill Level

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In this chapter, you’ll set up your Databricks environment and explore the Airbnb Open Dataset. You’ll learn essential SQL skills for querying, filtering, and summarizing data within the Databricks Lakehouse. Through exercises, you’ll practice simplifying complex queries with subqueries and optimizing performance for large datasets using the `OPTIMIZE` command. This chapter provides the foundational skills needed to analyze and manage data efficiently in Databricks.

Exercise 1: Introduction

Current Exercise

Exercise 2: Databricks environment Exercise 3: Analyze data structure Exercise 4: First query Exercise 5: Data quality assessment Exercise 6: Subqueries

In this chapter, you will explore advanced analytical techniques using the Airbnb dataset in Databricks SQL Notebooks. Through hands-on exercises, you will calculate descriptive statistics and leverage window functions for complex computations. You’ll also perform feature engineering to create meaningful metrics that drive actionable insights. By the end of this chapter, you’ll be equipped to confidently apply advanced analytics in real-world consulting scenarios for actionable insights, aligning with Data X’s data-driven strategy.

Exercise 1: Insight analytics Exercise 2: Analysing relationships Exercise 3: Using window functions Exercise 4: Feature engineering Exercise 5: Compose summary statistics Exercise 6: Data driven recommendations

Data visualization is a powerful tool for enhancing the understanding of complex datasets and communicating key insights effectively. In this chapter, you will explore the art and science of designing interactive visualizations and building comprehensive dashboards using Databricks. By leveraging various chart types, such as bar charts, heatmaps, and maps, you’ll uncover data distributions, correlations, and geographical trends. Interactive filters will enable dynamic exploration of neighborhoods, review scores, and price ranges.

Exercise 1: Visualization using Databricks AI/BI Exercise 2: Dashboard setup Exercise 3: Define the goal Exercise 4: Visualize trends Exercise 5: Visualize correlations Exercise 6: Conclusion