Introduction to Big Data with Spark and Hadoop from Coursera

What's inside

Syllabus

What Is Big Data?

In this module, you’ll begin your acquisition of Big Data knowledge with the most up-to-date definition of Big Data. You’ll explore the impact of Big Data on everyday personal tasks and business transactions with Big Data Use Cases. You’ll also learn how Big Data uses parallel processing, scaling, and data parallelism. Going further, you’ll explore commonly used Big Data tools and explain the role of open-source in Big Data. Finally, you’ll go beyond the hype and explore additional Big Data viewpoints.

Introduction to the Hadoop Ecosystem

In this module, you'll gain a fundamental understanding of the Apache Hadoop architecture, ecosystem, practices, and commonly used applications, including Distributed File System (HDFS), MapReduce, Hive, and HBase. You’ll also gain practical skills in hands-on labs when you query the data added using Hive, launch a single-node Hadoop cluster using Docker, and run MapReduce jobs.

Apache Spark

In this module, you’ll turn your attention to the popular Apache Spark platform, where you will explore the attributes and benefits of Apache Spark and distributed computing. You'll gain key insights about functional programming and Lambda functions. You’ll also explore Resilient Distributed Datasets (RDDs), parallel programming, resilience in Apache Spark, and relate RDDs and parallel programming with Apache Spark. Then, you’ll dive into additional Apache Spark components and learn how Apache Spark scales with Big Data. Working with Big Data signals the need for working with queries, including structured queries using SQL. You’ll also learn about the functions, parts, and benefits of Spark SQL and DataFrame queries, and discover how DataFrames work with Spark SQL.

DataFrames and Spark SQL

In this module, you’ll learn about Resilient Distributed Datasets (RDDs), their uses in Apache Spark, and RDD transformations and actions. You'll compare the use of datasets with Spark's latest data abstraction, DataFrames. You'll learn to identify and apply basic DataFrame operations. You’ll explore Apache Spark SQL optimization and learn how Spark SQL and memory optimization benefit from using Catalyst and Tungsten. Finally, you’ll fortify your skills with guided hands-on lab to create a table view and apply data aggregation techniques.

Development and Runtime Environment Options

In this module, you’ll explore how Spark processes the requests that your application submits and learn how you can track work using the Spark Application UI. Because Spark application work happens on the cluster, you need to be able to identify Apache Cluster Managers, their components, and benefits. You’ll also know how to connect with each cluster manager and how and when you might want to set up a local, standalone Spark instance. Next, you’ll learn about Apache Spark application submission, including the use of Spark’s unified interface, “spark-submit,” and learn about options and dependencies. You’ll also describe and apply options for submitting applications, identify external application dependency management techniques, and list Spark Shell benefits. You’ll also look at recommended practices for Spark's static and dynamic configuration options and perform hands-on labs to use Apache Spark on IBM Cloud and run Spark on Kubernetes.

Monitoring and Tuning

Platforms and applications require monitoring and tuning to manage issues that inevitably happen. In this module, you'll learn about connecting the Apache Spark user interface web server and using the same UI web server to manage application processes. You’ll also identify common Apache Spark application issues and learn about debugging issues using the application UI and locating related log files. Further, you’ll discover and gain real-world knowledge about how Spark manages memory and processor resources using the hands-on lab.

Final Project and Assessment

In this module, you’ll perform a practice lab where you’ll explore two critical aspects of data processing using Spark: working with Resilient Distributed Datasets (RDDs) and constructing DataFrames from JSON data. You will also apply various transformations and actions on both RDDs and DataFrames to gain insights and manipulate the data effectively. Further, you’ll apply your knowledge in a final project where you will create a DataFrame by loading data from a CSV file and applying transformations and actions using Spark SQL. Finally, you’ll be assessed based on your learning from the course.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Provides hands-on labs to practice concepts learned, thus offering practical experience and immediate feedback

Develops proficiency in big data tools, such as Apache Hadoop and Apache Spark, which are widely used in the industry

Taught by subject-matter experts: Romeo Kienzler, Rav Ahuja, and Aije Egwaikhide, who hold expertise in big data

Provides an overview of the platform, diving into the components of Apache Spark

Incorporates a blend of media, including videos, readings, and discussions, to create a multi-modal learning experience

Strengthens learners' foundation in big data, allowing for further advancement in the field

Reviews summary

Well-received big data course

Learners say that this course is well received with an overall rating of 79% positive, according to our analysis of 74 reviews. Many students praise the practical labs, comprehensive content, and engaging assignments that help make this a great course.

rated as largely positive

"This is really helpful for me to understand"

"That is a well packaged course"

"I found this course very interesting"

features engaging assignments

"The labs are short and have concise material."

"Fantastic blend of theory and practical (labs)."

"I like the content about Spark, it was well organised and demonstrated with hands-on lab"

has technical difficulties

"IBM stopped to maintain/support it's platform"

"The course is great, but when they updated the course a while ago, the certificate disappeared"

"Some labs environments where is not possible to work on the practical exercises."

includes difficult exams

"To much concepts and theory, i need more practice examples and less theory concepts"

"Too dry and technical"

"personally, I've found that the knowledge delivering method should be redone in a more attractive manner."

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 Introduction to Big Data with Spark and Hadoop with these activities:

Connect with experienced Big Data professionals

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Seek guidance and support from individuals with expertise in Big Data.

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Attend industry events or join online communities.
Reach out to professionals on LinkedIn or other platforms.

Attend a Big Data meetup or conference

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Connect with other learners and industry professionals to expand your network and knowledge.

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Find upcoming Big Data events in your area or online.
Register and attend the event.
Engage in discussions and ask questions.

Practice parallel processing concepts

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Helps you solidify your understanding of parallel processing concepts to prepare for Hadoop

Browse courses on Parallel Processing

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Review the concept of parallel processing
Solve practice problems involving parallel processing

Ten other activities

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Show all 13 activities

Review basic probability and statistics

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Refresh your knowledge of probability and statistics to support your understanding of data analysis concepts.

Browse courses on Probability

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Review concepts such as probability distributions, hypothesis testing, and statistical inference.
Solve practice problems to reinforce your understanding.

Review parallel programming concepts

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Strengthen your foundation in parallel programming before starting the course.

Browse courses on Parallel Programming

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Review concepts such as concurrency, synchronization, and thread management.
Practice writing simple parallel programs.

Gather resources on Big Data tools

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Organize and review relevant materials to enhance your understanding of Big Data tools.

Browse courses on Hadoop

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Search for articles, tutorials, and documentation on Hadoop and Spark.
Compile the resources in a folder or online repository.
Review the resources to reinforce your knowledge.

Explore additional tutorials on big data technologies

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Tutorials will provide supplemental instruction and insights that can complement the course content, enhancing your understanding of big data concepts.

Browse courses on Big Data Technologies

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Identify relevant tutorials on platforms like YouTube, Pluralsight, or LinkedIn Learning.
Choose tutorials that cover specific topics or concepts you want to delve deeper into.
Follow the tutorials and complete any accompanying exercises or challenges.

Explore Hadoop Ecosystem Tools

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Provides hands-on experience with Hadoop tools such as HDFS and MapReduce, enhancing your understanding.

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Find tutorials on Hadoop Ecosystem tools
Follow the tutorials to work with HDFS and MapReduce

Solve Hadoop and Spark practice problems

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Practice applying Hadoop and Spark concepts by solving problems and challenges.

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Review Hadoop and Spark concepts covered in the course.
Find practice problems online or in textbooks.
Attempt to solve the problems on your own.
Compare your solutions to provided solutions or discuss with classmates.

Follow Spark SQL tutorials

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Gain hands-on experience with Spark SQL by following guided tutorials.

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Find Spark SQL tutorials online or in documentation.
Follow the tutorials step-by-step.
Experiment with different queries and datasets.

Perform practice problems on Hadoop and Spark

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Practice problems will reinforce your understanding of Hadoop and Spark concepts and help you develop proficiency in using these tools.

Browse courses on Hadoop

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Access online practice problems or exercises from educational platforms like Coursera, edX, or Udemy.
Set aside dedicated time for practice and work through the problems.
Review your solutions and identify areas for improvement.

Build a Spark application

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Apply your knowledge by building a functional Spark application.

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Identify a problem or task that can be solved using Spark.
Design the application architecture.
Implement the application using Spark APIs.
Test and debug the application.
Deploy the application.

Develop a blog post or presentation on a specific aspect of big data

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Creating content will allow you to synthesize your knowledge, demonstrate your understanding, and reinforce key concepts through the process of teaching others.

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Choose a specific topic or concept related to big data that you want to explore further.
Research and gather information from reliable sources.
Organize your ideas and outline a structure for your blog post or presentation.
Create your content, ensuring it is well-written, visually appealing, and engaging.
Share your blog post or presentation with others and seek feedback.

Career center

Learners who complete Introduction to Big Data with Spark and Hadoop will develop knowledge and skills that may be useful to these careers:

Big Data Architect

Big Data Architects design and manage big data systems. This course may be useful for Big Data Architects as it provides a strong foundation in big data, Hadoop, and Spark, all of which are essential technologies for working with large and complex datasets. By understanding these technologies, Big Data Architects can improve their ability to design and manage big data systems that can handle the demands of big data.

See salaries and explore the career path for Big Data Architect

Machine Learning Engineer

Machine Learning Engineers build and deploy machine learning models. This course may be useful for Machine Learning Engineers as it provides a strong foundation in big data, Hadoop, and Spark, all of which are essential technologies for working with large and complex datasets. By understanding these technologies, Machine Learning Engineers can improve their ability to build and deploy machine learning models that can handle the demands of big data.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

Data Scientists use data to build models and make predictions. This course may be useful for Data Scientists as it provides a strong foundation in big data, Hadoop, and Spark, all of which are essential technologies for working with large and complex datasets. By understanding these technologies, Data Scientists can improve their ability to build and train models that can make accurate predictions.

See salaries and explore the career path for Data Scientist

Data Warehouse Architect

Data Warehouse Architects design and build data warehouses. This course may be useful for Data Warehouse Architects as it provides a strong foundation in big data, Hadoop, and Spark, all of which are essential technologies for working with large and complex datasets. By understanding these technologies, Data Warehouse Architects can improve their ability to design and build data warehouses that can handle the demands of big data.

See salaries and explore the career path for Data Warehouse Architect

Data Security Analyst

Data Security Analysts protect data from unauthorized access, use, disclosure, disruption, modification, or destruction. This course may be useful for Data Security Analysts as it provides a strong foundation in big data, Hadoop, and Spark, all of which are essential technologies for working with large and complex datasets. By understanding these technologies, Data Security Analysts can improve their ability to protect data from unauthorized access, use, disclosure, disruption, modification, or destruction.

See salaries and explore the career path for Data Security Analyst

Data Engineer

Data Engineers design, build, and maintain data pipelines and systems. This course may be useful for Data Engineers as it provides a strong foundation in big data, Hadoop, and Spark, all of which are essential technologies for working with large and complex datasets. By understanding these technologies, Data Engineers can improve their ability to build and manage data pipelines and systems that can handle the demands of big data.

See salaries and explore the career path for Data Engineer

Cloud Architect

Cloud Architects design and manage cloud computing systems. This course may be useful for Cloud Architects as it provides a strong foundation in big data, Hadoop, and Spark, all of which are essential technologies for working with large and complex datasets. By understanding these technologies, Cloud Architects can improve their ability to design and manage cloud computing systems that can handle the demands of big data.

See salaries and explore the career path for Cloud Architect

Systems Engineer

Systems Engineers design, implement, and maintain computer systems. This course may be useful for Systems Engineers as it provides a strong foundation in big data, Hadoop, and Spark, all of which are essential technologies for working with large and complex datasets. By understanding these technologies, Systems Engineers can improve their ability to design, implement, and maintain computer systems that can handle the demands of big data.

See salaries and explore the career path for Systems Engineer

Data Governance Analyst

Data Governance Analysts develop and enforce data governance policies and procedures. This course may be useful for Data Governance Analysts as it provides a strong foundation in big data, Hadoop, and Spark, all of which are essential technologies for working with large and complex datasets. By understanding these technologies, Data Governance Analysts can improve their ability to develop and enforce data governance policies and procedures that can help organizations manage their data effectively.

See salaries and explore the career path for Data Governance Analyst

Information Security Analyst

Information Security Analysts protect information systems from unauthorized access, use, disclosure, disruption, modification, or destruction. This course may be useful for Information Security Analysts as it provides a strong foundation in big data, Hadoop, and Spark, all of which are essential technologies for working with large and complex datasets. By understanding these technologies, Information Security Analysts can improve their ability to protect information systems from unauthorized access, use, disclosure, disruption, modification, or destruction.

See salaries and explore the career path for Information Security Analyst

Software Engineer

Software Engineers design, develop, and maintain software systems. This course may be useful for Software Engineers as it provides a strong foundation in big data, Hadoop, and Spark, all of which are essential technologies for working with large and complex datasets. By understanding these technologies, Software Engineers can improve their ability to design and develop software systems that can handle the demands of big data.

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Database Administrator

Database Administrators maintain and manage databases. This course may be useful for Database Administrators as it provides a strong foundation in big data, Hadoop, and Spark, all of which are essential technologies for working with large and complex datasets. By understanding these technologies, Database Administrators can improve their ability to maintain and manage databases that can handle the demands of big data.

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Business Analyst

Business Analysts analyze business data to identify opportunities and solve problems. This course may be useful for Business Analysts as it provides a strong foundation in big data, Hadoop, and Spark, all of which are essential technologies for working with large and complex datasets. By understanding these technologies, Business Analysts can improve their ability to analyze business data and make better recommendations to businesses.

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Data Analyst

Data Analysts collect, analyze, interpret, and present data in order to help businesses make informed decisions. This course may be useful for Data Analysts as it provides a strong foundation in big data, Hadoop, and Spark, all of which are essential technologies for working with large and complex datasets. By understanding these technologies, Data Analysts can improve their ability to extract insights from data and make better recommendations to businesses.

See salaries and explore the career path for Data Analyst