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Mohit Batra
Learn the Fundamentals of Apache Spark 3: process data, set up the environment, use RDDs & DataFrames, optimize apps, build pipelines with Databricks and Azure Synapse. Familiarize yourself with Spark's ecosystem here in this course.
Read more
Learn the Fundamentals of Apache Spark 3: process data, set up the environment, use RDDs & DataFrames, optimize apps, build pipelines with Databricks and Azure Synapse. Familiarize yourself with Spark's ecosystem here in this course.
Read more
Learn the Fundamentals of Apache Spark 3: process data, set up the environment, use RDDs & DataFrames, optimize apps, build pipelines with Databricks and Azure Synapse. Familiarize yourself with Spark's ecosystem here in this course.
Apache Spark is one of the most widely used analytics engines. It performs distributed data processing and can handle petabytes of data. Spark can work with a variety of data formats, process data at high speeds, and support multiple use cases. Version 3 of Spark brings a whole new set of features and optimizations. In this course, Apache Spark 3 Fundamentals, you'll learn how Apache Spark can be used to process large volumes of data, whether batch or streaming data, and about the growing ecosystem of Spark. First, you'll learn what Apache Spark is, its architecture, and its execution model. You'll then see how to set up the Spark environment. Next, you'll learn about two Spark APIs – RDDs and DataFrames – and see how to use them to extract, analyze, clean, and transform batch data. Then, you'll learn various techniques to optimize your Spark applications, as well as the new optimization features of Apache Spark 3. After that, you'll see how to reliably store data in a Data Lake using the Delta Lake format and build streaming pipelines with Spark. Finally, you'll see how to use Spark in cloud services like Databricks and Azure Synapse Analytics. By the end of this course, you'll have the knowledge and skills to work with Apache Spark and use its capabilities and ecosystem to build large-scale data processing pipelines. So, let's get started!
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What's inside
Syllabus
Course Overview
Getting Started with Apache Spark
Setting up Spark Environment
Working with RDDs - Resilient Distributed Datasets
Read more
Syllabus
Course Overview
Getting Started with Apache Spark
Setting up Spark Environment
Working with RDDs - Resilient Distributed Datasets
Read more
Cleaning and Transforming Data with DataFrames
Working with Spark SQL, UDFs, and Common DataFrame Operations
Performing Optimizations in Spark
Features in Apache Spark 3
Building Reliable Data Lake with Spark and Delta Lake
Handling Streaming Data with Spark Structured Streaming
Working with Spark in Cloud
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Taught by Mohit Batra, an expert in Apache Spark
Covers the fundamentals of Apache Spark 3, a widely used analytics engine
Develops skills in working with RDDs and DataFrames, essential Apache Spark concepts
Explores advanced topics such as optimizations and features in Apache Spark 3
Provides practical experience in building reliable data pipelines with Spark and Delta Lake
Covers streaming data handling with Spark Structured Streaming
Save this course
Save Apache Spark 3 Fundamentals to your list so you can find it easily later:
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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 Apache Spark 3 Fundamentals with these
activities:
Read 'Learning Spark: Lightning-Fast Data Analytics'
Show steps
Expand your knowledge beyond the course materials by delving into this comprehensive guide to Spark, gaining insights into its architecture, programming models, and best practices.
View
Learning Spark: Lightning-Fast Big Data Analysis
on Amazon
Show steps
-
Read chapters 2-4 to understand Spark's core concepts
-
Work through the code examples provided in the book
Learn About RDDs and DataFrames
Show steps
Familiarize yourself with RDDs (Resilient Distributed Datasets) and DataFrames, two essential Spark APIs for extracting, analyzing, and transforming large-scale data.
Show steps
-
Complete the RDD and Dataframe Tutorial on Spark's official website
-
Explore the Spark DataFrame API documentation
Practice Data Cleaning and Transformation
Show steps
Engage in practical exercises involving data cleaning and transformation using DataFrames and Spark SQL to solidify your understanding of data wrangling techniques in Spark.
Browse courses on
Data Cleaning
Show steps
-
Work through the data cleaning and transformation exercises in the Spark documentation
-
Complete a coding challenge on LeetCode or HackerRank related to data cleaning in Spark
Three other activities
Expand to see all activities and additional details
Show all six activities
Build a Data Pipeline with Spark and Delta Lake
Show steps
Apply your knowledge by constructing a data pipeline that leverages Spark and Delta Lake's capabilities to reliably store, process, and analyze large datasets.
Browse courses on
Data Pipeline
Show steps
-
Design the architecture of your data pipeline
-
Implement the data pipeline using Spark and Delta Lake
-
Test and evaluate the performance of your pipeline
Develop a Streaming Data Application
Show steps
Put your skills to the test by building a streaming data application that utilizes Spark Structured Streaming to process and analyze real-time data streams.
Browse courses on
Streaming Data
Show steps
-
Identify a real-world use case for streaming data processing
-
Design the architecture of your streaming application
-
Implement the streaming application using Spark Structured Streaming
-
Deploy and monitor your streaming application
Contribute to the Spark Community
Show steps
Immerse yourself in the Spark ecosystem by actively contributing to open-source projects, reporting bugs, or suggesting improvements, thus enhancing your understanding and staying abreast of the latest developments.
Browse courses on
Open Source
Show steps
-
Identify an area within the Spark community where you can contribute
-
Start contributing by reporting bugs or suggesting improvements
-
Attend online community meetings or discussions
Read 'Learning Spark: Lightning-Fast Data Analytics'
Show steps
Expand your knowledge beyond the course materials by delving into this comprehensive guide to Spark, gaining insights into its architecture, programming models, and best practices.
View
Learning Spark: Lightning-Fast Big Data Analysis
on Amazon
Show steps
Show steps
Show steps
- Read chapters 2-4 to understand Spark's core concepts
- Work through the code examples provided in the book
Learn About RDDs and DataFrames
Show steps
Familiarize yourself with RDDs (Resilient Distributed Datasets) and DataFrames, two essential Spark APIs for extracting, analyzing, and transforming large-scale data.
Show steps
Show steps
Show steps
- Complete the RDD and Dataframe Tutorial on Spark's official website
- Explore the Spark DataFrame API documentation
Practice Data Cleaning and Transformation
Show steps
Engage in practical exercises involving data cleaning and transformation using DataFrames and Spark SQL to solidify your understanding of data wrangling techniques in Spark.
Browse courses on
Data Cleaning
Show steps
Show steps
Show steps
- Work through the data cleaning and transformation exercises in the Spark documentation
- Complete a coding challenge on LeetCode or HackerRank related to data cleaning in Spark
Three other activities
Expand to see all activities and additional details
Show all six activities
Build a Data Pipeline with Spark and Delta Lake
Show steps
Apply your knowledge by constructing a data pipeline that leverages Spark and Delta Lake's capabilities to reliably store, process, and analyze large datasets.
Browse courses on
Data Pipeline
Show steps
Show steps
Show steps
- Design the architecture of your data pipeline
- Implement the data pipeline using Spark and Delta Lake
- Test and evaluate the performance of your pipeline
Develop a Streaming Data Application
Show steps
Put your skills to the test by building a streaming data application that utilizes Spark Structured Streaming to process and analyze real-time data streams.
Browse courses on
Streaming Data
Show steps
Show steps
Show steps
- Identify a real-world use case for streaming data processing
- Design the architecture of your streaming application
- Implement the streaming application using Spark Structured Streaming
- Deploy and monitor your streaming application
Contribute to the Spark Community
Show steps
Immerse yourself in the Spark ecosystem by actively contributing to open-source projects, reporting bugs, or suggesting improvements, thus enhancing your understanding and staying abreast of the latest developments.
Browse courses on
Open Source
Show steps
Show steps
Show steps
- Identify an area within the Spark community where you can contribute
- Start contributing by reporting bugs or suggesting improvements
- Attend online community meetings or discussions
Career center
Learners who complete Apache Spark 3 Fundamentals will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts examine big data to extract actionable insights for companies. This course introduces the core concepts of Apache Spark 3, a widely used analytics engine for big data processing. You'll learn how to set up a Spark environment, work with Spark APIs (RDDs and DataFrames), and perform optimizations to maximize performance. These skills are essential for Data Analysts who need to handle large volumes of data effectively.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain big data systems. This course provides a comprehensive overview of Apache Spark 3, a powerful tool for data engineering. You'll learn how to set up a Spark environment, work with Spark APIs (RDDs and DataFrames), and use Spark to handle data in various formats. This knowledge will help you build and manage data pipelines efficiently.
Data Scientist
Data Scientist
Data Scientists use statistical and machine learning techniques to extract insights from data. This course introduces Apache Spark 3, a popular analytics engine for big data. You'll learn how to set up a Spark environment, work with Spark APIs (RDDs and DataFrames), and perform optimizations to maximize performance. This course will provide you with a solid foundation for leveraging Spark in your data science projects.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build and deploy machine learning models. This course introduces Apache Spark 3, a powerful tool for distributed machine learning. You'll learn how to set up a Spark environment, work with Spark APIs (RDDs and DataFrames), and use Spark to train and deploy machine learning models. This knowledge will help you develop and implement machine learning solutions at scale.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. This course provides a foundation in Apache Spark 3, a popular analytics engine for big data. You'll learn how to set up a Spark environment, work with Spark APIs (RDDs and DataFrames), and perform optimizations to maximize performance. This knowledge will enable you to develop scalable and efficient software solutions for big data processing.
Cloud Architect
Cloud Architect
Cloud Architects design and manage cloud computing solutions. This course introduces Apache Spark 3, a powerful tool for big data processing in the cloud. You'll learn how to set up a Spark environment in the cloud, work with Spark APIs (RDDs and DataFrames), and use Spark to build and manage big data pipelines. This knowledge will help you design and implement scalable and cost-effective cloud solutions.
Business Analyst
Business Analyst
Business Analysts analyze business data to identify opportunities and solve problems. This course introduces Apache Spark 3, a powerful tool for big data analysis. You'll learn how to set up a Spark environment, work with Spark APIs (RDDs and DataFrames), and use Spark to extract insights from large volumes of data. This knowledge will help you become a more effective Business Analyst and make data-driven decisions.
Data Visualization Engineer
Data Visualization Engineer
Data Visualization Engineers design and develop data visualizations. This course introduces Apache Spark 3, a powerful tool for big data processing. You'll learn how to set up a Spark environment, work with Spark APIs (RDDs and DataFrames), and use Spark to prepare data for visualization. This knowledge will help you create compelling and informative data visualizations.
Database Administrator
Database Administrator
Database Administrators manage and maintain databases. This course introduces Apache Spark 3, a powerful tool for big data processing. You'll learn how to set up a Spark environment, work with Spark APIs (RDDs and DataFrames), and use Spark to optimize database performance. This knowledge will help you become a more effective Database Administrator and manage large-scale databases efficiently.
Software Developer
Software Developer
Software Developers design, develop, and maintain software applications. This course introduces Apache Spark 3, a powerful tool for big data processing. You'll learn how to set up a Spark environment, work with Spark APIs (RDDs and DataFrames), and use Spark to build and deploy big data applications. This knowledge will help you develop scalable and efficient software solutions for big data challenges.
Systems Engineer
Systems Engineer
Systems Engineers design, implement, and maintain computer systems. This course introduces Apache Spark 3, a powerful tool for big data processing. You'll learn how to set up a Spark environment, work with Spark APIs (RDDs and DataFrames), and use Spark to optimize system performance. This knowledge will help you become a more effective Systems Engineer and manage complex computer systems efficiently.
IT Manager
IT Manager
IT Managers plan, organize, and direct IT operations. This course introduces Apache Spark 3, a powerful tool for big data processing. You'll learn how to set up a Spark environment, work with Spark APIs (RDDs and DataFrames), and use Spark to optimize IT infrastructure. This knowledge will help you become a more effective IT Manager and manage IT operations efficiently.
Data Architect
Data Architect
Data Architects design and manage data systems. This course introduces Apache Spark 3, a powerful tool for big data processing. You'll learn how to set up a Spark environment, work with Spark APIs (RDDs and DataFrames), and use Spark to build and manage scalable data architectures. This knowledge will help you become a more effective Data Architect and design data systems that meet the needs of your organization.
Chief Technology Officer
Chief Technology Officer
Chief Technology Officers (CTOs) lead and manage technology strategy and innovation. This course introduces Apache Spark 3, a powerful tool for big data processing. You'll learn how to set up a Spark environment, work with Spark APIs (RDDs and DataFrames), and use Spark to drive technology innovation. This knowledge will help you become a more effective CTO and lead your organization's technology strategy.
Chief Information Officer
Chief Information Officer
Chief Information Officers (CIOs) lead and manage information technology (IT) operations and strategy. This course introduces Apache Spark 3, a powerful tool for big data processing. You'll learn how to set up a Spark environment, work with Spark APIs (RDDs and DataFrames), and use Spark to optimize IT operations. This knowledge will help you become a more effective CIO and lead your organization's IT strategy.
For more career information including salaries, visit:
OpenCourser.com/course/m2t6h8/apache
Reading list
We've selected five 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
Apache Spark 3 Fundamentals.
Provides a comprehensive overview of Spark, useful as a reference guide, commonly used as a textbook in academic or training settings.
Provides a comprehensive overview of the fundamentals of MapReduce for processing large data sets.
Save
Practical guide to using Apache Spark for data analytics, with a focus on real-world use cases.
Covers Apache Spark SQL, with a focus on real-world use cases.
Covers advanced topics such as machine learning, graph processing, and streaming with Spark.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/m2t6h8/apache
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Time
379 hours
Level
Introductory
Via
Pluralsight
Instructor
Mohit Batra
Language
English
Good to know
Taught by Mohit Batra, an expert in Apache Spark
Covers the fundamentals of Apache Spark 3, a widely used analytics engine
Develops skills in working with RDDs and DataFrames, essential Apache Spark concepts
Explores advanced topics such as optimizations and features in Apache Spark 3
Provides practical experience in building reliable data pipelines with Spark and Delta Lake
Covers streaming data handling with Spark Structured Streaming
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