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Databricks Certified Associate Developer for Apache Spark Using Python: The ultimate guide to getting certified in Apache Spark using practical exampl - Paperback
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by Saba Shah (Author), Rod Waltermann (Foreword by)
Learn the concepts and exercises needed to confidently prepare for the Databricks Associate Developer for Apache Spark 3.0 exam and validate your Spark skills with an industry-recognized credential
Key Features:
- Understand the fundamentals of Apache Spark to design robust and fast Spark applications
- Explore various data manipulation components for each phase of your data engineering project
- Prepare for the certification exam with sample questions and mock exams
- Purchase of the print or Kindle book includes a free PDF eBook
Book Description:
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.
What You Will Learn:
- Create and manipulate SQL queries in Apache Spark
- Build complex Spark functions using Spark's user-defined functions (UDFs)
- Architect big data apps with Spark fundamentals for optimal design
- Apply techniques to manipulate and optimize big data applications
- Develop real-time or near-real-time applications using Spark Streaming
- Work with Apache Spark for machine learning applications
Who this book is for:
This book is for data professionals such as data engineers, data analysts, BI developers, and data scientists looking for a comprehensive resource to achieve Databricks Certified Associate Developer certification, as well as for individuals who want to venture into the world of big data and data engineering. Although working knowledge of Python is required, no prior knowledge of Spark is necessary. Additionally, experience with Pyspark will be beneficial.
Table of Contents
- Overview of Certification Guide and Exam
- Understanding Apache Spark and Its Applications
- Spark Architecture & Transformations
- Spark Datarames and its Operations
- Advanced Operations in Spark
- SQL Queries in Spark
- Structured Streaming in Spark
- Machine Learning with Spark ML
- Mock Test
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Standard U.S. returns are accepted for eligible new, unopened items within 30 days of carrier delivery. Returns are by mail. Customers are responsible for standard return shipping, there is no restocking fee, and standard exchanges are not offered. Once an approved return is received and inspected, the refund is issued to the original payment method within 5 business days. Damaged, defective, or incorrect items are handled at no return-shipping cost to the customer.
Return eligibility is subject to the full policy. Opened, read, or unsealed books with broken seals or visible wear are final sale. Digital downloads, gift cards, and personalized products are non-refundable. Original shipping charges are not refunded for standard returns. Contact us within 7 days of delivery about damaged, defective, or incorrect items.
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