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Xavier Morera

Apache Spark is one of the fastest and most efficient general engines for large-scale data processing. In this course, you'll learn how to develop Spark applications for your Big Data using Scala and a stable Hadoop distribution, Cloudera CDH.

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Apache Spark is one of the fastest and most efficient general engines for large-scale data processing. In this course, you'll learn how to develop Spark applications for your Big Data using Scala and a stable Hadoop distribution, Cloudera CDH.

At the core of working with large-scale datasets is a thorough knowledge of Big Data platforms like Apache Spark and Hadoop. In this course, Developing Spark Applications Using Scala & Cloudera, you’ll learn how to process data at scales you previously thought were out of your reach. First, you’ll learn all the technical details of how Spark works. Next, you’ll explore the RDD API, the original core abstraction of Spark. Then, you’ll discover how to become more proficient using Spark SQL and DataFrames. Finally, you'll learn to work with Spark's typed API: Datasets. When you’re finished with this course, you’ll have a foundational knowledge of Apache Spark with Scala and Cloudera that will help you as you move forward to develop large-scale data applications that enable you to work with Big Data in an efficient and performant way.

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What's inside

Syllabus

Course Overview
Why Spark with Scala and Cloudera?
Getting an Environment and Data: CDH + StackOverflow
Refreshing Your Knowledge: Scala Fundamentals for This Course
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Understanding Spark: An Overview
Getting Technical with Spark
Learning the Core of Spark: RDDs
Going Deeper into Spark Core
Increasing Proficiency with Spark: DataFrames and Spark SQL
Continuing the Journey on DataFrames and Spark SQL
Working with a Typed API: Datasets
Final Takeaway and Continuing the Journey with Spark

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Strong foundation for beginners to develop working knowledge of big data applications using Spark, Scala, and Hadoop distribution
Provides hands-on labs and interactive materials to enhance understanding and application of concepts
Taught by Xavier Morera, an experienced instructor in Apache Spark and big data applications
Examines technical details and core abstractions of Spark, such as RDDs and DataFrames
Develops proficiency in working with Spark's typed API, Datasets
Course material is relevant to big data processing in industry and academia

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Developing Spark Applications Using Scala & Cloudera with these activities:
Review Spark
Refresh your knowledge of Apache Spark before starting this course to strengthen your foundation.
Browse courses on Apache Spark
Show steps
  • Review Spark documentation
  • Work through Spark tutorials
Find a Spark Mentor
Enhance your learning by finding an experienced Spark mentor who can provide valuable insights and guidance.
Show steps
  • Identify potential mentors
  • Reach out and connect with mentors
Hello Spark App
Put your Spark skills to practice by building a simple Spark application.
Show steps
  • Create a new Spark project
  • Write Spark code to read and process data
  • Run your Spark application
Five other activities
Expand to see all activities and additional details
Show all eight activities
Practice Spark RDDs
Strengthen your understanding of Spark RDDs through repetitive exercises.
Show steps
  • Work through RDD exercises
  • Implement RDD operations in your own code
Practice Spark DataFrames and Spark SQL
Solidify your knowledge of Spark DataFrames and Spark SQL through targeted exercises.
Show steps
  • Work through DataFrames and Spark SQL exercises
  • Apply DataFrames and Spark SQL in your own code
Mentor Junior Spark Developers
Expand your knowledge by mentoring others and reinforcing your understanding of Spark concepts.
Show steps
  • Identify opportunities to mentor
  • Provide guidance and support to mentees
Create a Spark Tutorial
Deepen your understanding of Spark by creating a tutorial that explains a specific aspect of the framework.
Show steps
  • Choose a topic
  • Research and gather information
  • Write and record your tutorial
  • Publish your tutorial
Participate in Spark Hackathons
Challenge yourself and test your Spark skills by participating in hackathons.
Show steps
  • Identify Spark hackathons
  • Form a team or participate individually
  • Develop and submit your Spark project

Career center

Learners who complete Developing Spark Applications Using Scala & Cloudera will develop knowledge and skills that may be useful to these careers:
Big Data Engineer
A Big Data Engineer designs, builds, and maintains systems for managing and processing large volumes of data. This course may be useful in learning the core concepts of Apache Spark, Scala, and Cloudera, which are essential tools for Big Data engineering tasks.
Data Engineer
A Data Engineer designs, builds, tests, and maintains data management systems and databases. This course can help build a foundation for working with Apache Spark, Scala, and Cloudera, which are popular tools and technologies used for data engineering tasks.
Data Scientist
A Data Scientist uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from data in various forms, both structured and unstructured. This course can help build a foundation in Apache Spark, Scala, and Cloudera, which are widely used for data science tasks.
Machine Learning Engineer
A Machine Learning Engineer designs, develops, and maintains machine learning models and systems. This course can help build a foundation in Apache Spark, Scala, and Cloudera, which are widely used for machine learning tasks.
Data Integration Engineer
A Data Integration Engineer designs and implements data integration solutions to combine data from multiple sources. This course can help build a foundation in Apache Spark, Scala, and Cloudera, which are commonly used for data integration tasks.
Analytics Engineer
An Analytics Engineer designs, builds, and maintains data analytics systems and tools. This course can help build a foundation in Apache Spark, Scala, and Cloudera, which are widely used for data analytics tasks.
Software Architect
A Software Architect designs and oversees the development of software systems and ensures their scalability, reliability, and performance. This course can help build a foundation in Apache Spark, Scala, and Cloudera, which are essential tools for designing and developing large-scale software systems.
Cloud Architect
A Cloud Architect designs and manages cloud computing systems and infrastructure. This course can help build a foundation in Apache Spark, Scala, and Cloudera, which are widely used in cloud computing environments.
Database Administrator
A Database Administrator manages and maintains database systems and ensures their optimal performance. This course can help build a foundation in Apache Spark, Scala, and Cloudera, which can be beneficial when working with large-scale databases.
Enterprise Architect
An Enterprise Architect designs and manages the overall architecture of an organization's IT systems and infrastructure. This course may be useful in learning the basics of Apache Spark, Scala, and Cloudera, which can be beneficial when working on projects that involve designing and implementing IT solutions for large organizations.
DevOps Engineer
A DevOps Engineer collaborates with software developers and IT operations professionals to ensure the smooth development and deployment of software systems. This course may be useful in learning about Apache Spark, Scala, and Cloudera, which are used in DevOps pipelines for building, testing, and deploying software applications.
Data Visualization Engineer
A Data Visualization Engineer designs and creates data visualizations to communicate data insights effectively. This course may be useful in learning about Apache Spark, Scala, and Cloudera, which can be beneficial when working on projects that involve visualizing large datasets.
Chief Data Officer
A Chief Data Officer is responsible for managing and governing an organization's data assets. This course may be useful in gaining knowledge about Apache Spark, Scala, and Cloudera, which can be beneficial when working on projects that involve managing and analyzing large datasets.
Data Analyst
A Data Analyst collects, analyzes, and interprets data to help organizations make informed decisions. This course may be useful in gaining knowledge about Apache Spark, Scala, and Cloudera, which can be helpful when working on data analysis and visualization projects.
Software Engineer
A Software Engineer develops, maintains, and analyzes software in computer systems and applications. This course may be useful in learning the basics of Apache Spark, Scala, and Cloudera, which can be beneficial when working on projects that involve designing, developing, testing, and implementing software solutions.

Reading list

We've selected seven books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Developing Spark Applications Using Scala & Cloudera.
Is considered the authoritative guide to Apache Spark. It covers the entire Spark ecosystem, including its core concepts, APIs, and advanced topics such as machine learning and streaming.
Provides a comprehensive overview of Apache Spark, covering its core concepts, APIs, and use cases. It is particularly useful as a reference guide for developers who want to learn more about Spark's technical details.
Delves into the performance optimization techniques for Apache Spark applications. It provides insights into Spark's internals and offers practical guidance on how to improve the performance of Spark jobs.
Provides practical guidance on using Hadoop for data processing tasks. It covers topics such as data ingestion, transformation, and analysis.
Provides a comprehensive overview of the Hadoop ecosystem, including its core components such as HDFS and MapReduce. It is particularly useful for understanding the underlying infrastructure that supports Apache Spark.
Provides a collection of recipes and solutions for common Scala programming tasks. It can serve as a useful reference for developers who want to expand their Scala knowledge.

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