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  • Book Overview & Buying Databricks Certified Associate Developer for Apache Spark Using Python
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Databricks Certified Associate Developer for Apache Spark Using Python

Databricks Certified Associate Developer for Apache Spark Using Python

By : Saba Shah
5 (3)
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Databricks Certified Associate Developer for Apache Spark Using Python

Databricks Certified Associate Developer for Apache Spark Using Python

5 (3)
By: Saba Shah

Overview of this book

Spark has become a de facto standard for big data processing. Migrating data processing to Spark saves resources, streamlines your business focus, and modernizes workloads, creating new business opportunities through Spark’s advanced capabilities. Written by a senior solutions architect at Databricks, with experience in leading data science and data engineering teams in Fortune 500s as well as startups, this book is your exhaustive guide to achieving the Databricks Certified Associate Developer for Apache Spark certification on your first attempt. You’ll explore the core components of Apache Spark, its architecture, and its optimization, while familiarizing yourself with the Spark DataFrame API and its components needed for data manipulation. You’ll also find out what Spark streaming is and why it’s important for modern data stacks, before learning about machine learning in Spark and its different use cases. What’s more, you’ll discover sample questions at the end of each section along with two mock exams to help you prepare for the certification exam. By the end of this book, you’ll know what to expect in the exam and gain enough understanding of Spark and its tools to pass the exam. You’ll also be able to apply this knowledge in a real-world setting and take your skillset to the next level.
Table of Contents (18 chapters)
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1
Part 1: Exam Overview
3
Part 2: Introducing Spark
6
Part 3: Spark Operations
10
Part 4: Spark Applications
13
Part 5: Mock Papers
14
Chapter 9: Mock Test 1
15
Chapter 10: Mock Test 2

Problem statement

Let’s dive into a case study where we’ll explore the art of predicting house prices using historical data. Picture this: we have a treasure trove of valuable information about houses, including details such as zoning, lot area, building type, overall condition, year built, and sale price. Our goal is to harness the power of ML to accurately forecast the price of a new house that comes our way.

To accomplish this feat, we’ll embark on a journey to construct an ML model exclusively designed for predicting house prices. This model will leverage the existing historical data and incorporate additional features. By carefully analyzing and understanding the relationships between these features and the corresponding sale prices, our model will become a reliable tool for estimating the value of any new house that enters the market.

To achieve this, we will go through some of the steps defined in the previous section, where we talked about the ML...

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Databricks Certified Associate Developer for Apache Spark Using Python
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