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Janani Ravi
Knowing how to program MapReduce is only half the battle. In this course, you'll learn how to set up the correct MapReduce based on what you want to accomplish.
Read more
Knowing how to program MapReduce is only half the battle. In this course, you'll learn how to set up the correct MapReduce based on what you want to accomplish.
Read more
Knowing how to program MapReduce is only half the battle. In this course, you'll learn how to set up the correct MapReduce based on what you want to accomplish.
This course, Applying MapReduce to Common Data Problems, helps you with three unique MapReduce patterns: summarizing numeric data, filtering large datasets, and building an index for fast data lookup. First, you'll learn about how you start "Thinking MapReduce" including what's involved and what needs to be broken down to start thinking in these terms. Next, you'll explore how to compute numeric summary metrics, and how to filter large data sets. Finally, you'll wrap up the course by learning about building indices, and why an inverted index is so important in the context of search engines. After watching this course, you'll have the confidence to spot patterns in MapReduce problems and will be on you're way to mastering this programming model.
Register for this course and see more details by visiting:
OpenCourser.com/course/m1try9/applying
What's inside
Syllabus
Course Overview
Thinking MapReduce
Computing Numeric Summary Metrics
Filtering Large Data Sets
Read more
Syllabus
Course Overview
Thinking MapReduce
Computing Numeric Summary Metrics
Filtering Large Data Sets
Read more
Building Indexes
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Explores key MapReduce patterns that are relevant to industry, such as summarizing numeric data, filtering large datasets, and building an index for fast data lookup
Taught by Janani Ravi, an recognized expert in the field of data engineering
Develops the foundational block of programming models by offering a clear understanding of how to "Think MapReduce"
Suitable for learners with some prior experience in programming and data structures
Save this course
Save Applying MapReduce to Common Data Problems 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 Applying MapReduce to Common Data Problems with these
activities:
Review Java Programming Fundamentals
Show steps
Before starting the course, refresh your basic Java programming skills to ensure you have a strong foundation for MapReduce coding.
Browse courses on
Java Programming
Show steps
-
Review core Java concepts like variables, data types, operators, and control flow.
-
Practice writing simple Java programs.
Learn MapReduce Concepts with Hadoop Tutorials
Show steps
Before starting the course, familiarize yourself with MapReduce concepts by watching beginner-friendly tutorials on YouTube, Coursera, or edX.
Browse courses on
MapReduce
Show steps
-
Locate reputable tutorials that provide a clear explanation of MapReduce fundamentals.
-
Take notes on key concepts like mappers, reducers, combiners, and job configurations.
-
Complete hands-on exercises to reinforce your understanding.
Join a MapReduce Study Group
Show steps
Participate in weekly study sessions with fellow students to discuss course concepts, share insights, and work on problems together.
Show steps
-
Find or create a study group with individuals who have similar learning goals.
-
Establish a regular meeting schedule and agenda.
-
Take turns presenting and discussing different topics.
-
Collaborate on assignments and projects.
Five other activities
Expand to see all activities and additional details
Show all eight activities
MapReduce Coding Drills and Exercises
Show steps
Strengthen your MapReduce coding skills by solving practice problems and exercises found on platforms like LeetCode, HackerRank, and the Apache Hadoop website.
Browse courses on
Java Programming
Show steps
-
Choose coding problems that align with the course content and difficulty level.
-
Implement MapReduce solutions using Java or other supported languages.
-
Analyze your solutions, identify potential optimizations, and learn from your mistakes.
Create a MapReduce Infographic
Show steps
Design an infographic that visually explains key MapReduce concepts, workflow, and applications.
Browse courses on
Visual learning
Show steps
-
Gather information and research the topic thoroughly.
-
Identify the most important concepts and create a storyboard.
-
Use a design tool to create a visually appealing infographic.
-
Share your infographic with others and gather feedback.
Attend a MapReduce Workshop
Show steps
Enroll in a MapReduce workshop to gain hands-on experience and insights from industry experts.
Show steps
-
Research and identify reputable MapReduce workshops.
-
Register for a workshop that aligns with your skill level and interests.
-
Actively participate in the workshop, ask questions, and network with other attendees.
Develop a Mini MapReduce Project
Show steps
Apply your MapReduce knowledge by building a small-scale project that addresses a real-world data problem. This could involve analyzing log files, processing large datasets, or creating custom data pipelines.
Browse courses on
Data Analysis
Show steps
-
Define the problem statement and gather the necessary data.
-
Design the MapReduce job flow, including mappers, reducers, and combiners.
-
Implement the MapReduce code and configure the Hadoop job.
-
Run the job and analyze the results.
-
Write a report summarizing your project, findings, and potential improvements.
Contribute to Open-Source MapReduce Projects
Show steps
Enhance your MapReduce skills and contribute to the community by participating in open-source projects on platforms like GitHub and Apache Software Foundation.
Browse courses on
Open Source
Show steps
-
Identify open-source MapReduce projects that align with your interests.
-
Review the project documentation and codebase.
-
Find an area where you can contribute, such as bug fixes, feature enhancements, or documentation improvements.
-
Submit a pull request with your contributions and provide clear documentation.
Review Java Programming Fundamentals
Show steps
Before starting the course, refresh your basic Java programming skills to ensure you have a strong foundation for MapReduce coding.
Browse courses on
Java Programming
Show steps
Show steps
Show steps
- Review core Java concepts like variables, data types, operators, and control flow.
- Practice writing simple Java programs.
Learn MapReduce Concepts with Hadoop Tutorials
Show steps
Before starting the course, familiarize yourself with MapReduce concepts by watching beginner-friendly tutorials on YouTube, Coursera, or edX.
Browse courses on
MapReduce
Show steps
Show steps
Show steps
- Locate reputable tutorials that provide a clear explanation of MapReduce fundamentals.
- Take notes on key concepts like mappers, reducers, combiners, and job configurations.
- Complete hands-on exercises to reinforce your understanding.
Join a MapReduce Study Group
Show steps
Participate in weekly study sessions with fellow students to discuss course concepts, share insights, and work on problems together.
Show steps
Show steps
Show steps
- Find or create a study group with individuals who have similar learning goals.
- Establish a regular meeting schedule and agenda.
- Take turns presenting and discussing different topics.
- Collaborate on assignments and projects.
Five other activities
Expand to see all activities and additional details
Show all eight activities
MapReduce Coding Drills and Exercises
Show steps
Strengthen your MapReduce coding skills by solving practice problems and exercises found on platforms like LeetCode, HackerRank, and the Apache Hadoop website.
Browse courses on
Java Programming
Show steps
Show steps
Show steps
- Choose coding problems that align with the course content and difficulty level.
- Implement MapReduce solutions using Java or other supported languages.
- Analyze your solutions, identify potential optimizations, and learn from your mistakes.
Create a MapReduce Infographic
Show steps
Design an infographic that visually explains key MapReduce concepts, workflow, and applications.
Browse courses on
Visual learning
Show steps
Show steps
Show steps
- Gather information and research the topic thoroughly.
- Identify the most important concepts and create a storyboard.
- Use a design tool to create a visually appealing infographic.
- Share your infographic with others and gather feedback.
Attend a MapReduce Workshop
Show steps
Enroll in a MapReduce workshop to gain hands-on experience and insights from industry experts.
Show steps
Show steps
Show steps
- Research and identify reputable MapReduce workshops.
- Register for a workshop that aligns with your skill level and interests.
- Actively participate in the workshop, ask questions, and network with other attendees.
Develop a Mini MapReduce Project
Show steps
Apply your MapReduce knowledge by building a small-scale project that addresses a real-world data problem. This could involve analyzing log files, processing large datasets, or creating custom data pipelines.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Define the problem statement and gather the necessary data.
- Design the MapReduce job flow, including mappers, reducers, and combiners.
- Implement the MapReduce code and configure the Hadoop job.
- Run the job and analyze the results.
- Write a report summarizing your project, findings, and potential improvements.
Contribute to Open-Source MapReduce Projects
Show steps
Enhance your MapReduce skills and contribute to the community by participating in open-source projects on platforms like GitHub and Apache Software Foundation.
Browse courses on
Open Source
Show steps
Show steps
Show steps
- Identify open-source MapReduce projects that align with your interests.
- Review the project documentation and codebase.
- Find an area where you can contribute, such as bug fixes, feature enhancements, or documentation improvements.
- Submit a pull request with your contributions and provide clear documentation.
Career center
Learners who complete Applying MapReduce to Common Data Problems will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts use their knowledge of data analysis, programming, and statistics to extract meaningful information from data. This course, Applying MapReduce to Common Data Problems, can help you develop the skills needed to succeed in this role by providing you with a solid understanding of how to use MapReduce to solve common data problems. You'll learn how to summarize numeric data, filter large datasets, and build an index for fast data lookup. These skills are essential for Data Analysts who want to be able to effectively analyze data and extract meaningful insights.
Data Scientist
Data Scientist
Data Scientists use their knowledge of data analysis, machine learning, and statistics to build models that can predict future outcomes. This course, Applying MapReduce to Common Data Problems, can help you develop the skills needed to succeed in this role by providing you with a solid understanding of how to use MapReduce to solve common data problems. You'll learn how to summarize numeric data, filter large datasets, and build an index for fast data lookup. These skills are essential for Data Scientists who want to be able to effectively analyze data and build models that can make accurate predictions.
Business Analyst
Business Analyst
Business Analysts use their knowledge of business processes and data analysis to improve business outcomes. This course, Applying MapReduce to Common Data Problems, can help you develop the skills needed to succeed in this role by providing you with a solid understanding of how to use MapReduce to solve common data problems. You'll learn how to summarize numeric data, filter large datasets, and build an index for fast data lookup. These skills are essential for Business Analysts who want to be able to effectively analyze data and identify opportunities for improvement.
Statistician
Statistician
Statisticians collect, analyze, and interpret data. This course, Applying MapReduce to Common Data Problems, can help you develop the skills needed to succeed in this role by providing you with a solid understanding of how to use MapReduce to solve common data problems. You'll learn how to summarize numeric data, filter large datasets, and build an index for fast data lookup. These skills are essential for Statisticians who want to be able to effectively analyze data and extract meaningful insights.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. This course, Applying MapReduce to Common Data Problems, can help you develop the skills needed to succeed in this role by providing you with a solid understanding of how to use MapReduce to solve common data problems. You'll learn how to summarize numeric data, filter large datasets, and build an index for fast data lookup. These skills are essential for Software Engineers who want to be able to develop efficient and scalable software applications.
Database Administrator
Database Administrator
Database Administrators design, implement, and maintain databases. This course, Applying MapReduce to Common Data Problems, can help you develop the skills needed to succeed in this role by providing you with a solid understanding of how to use MapReduce to solve common data problems. You'll learn how to summarize numeric data, filter large datasets, and build an index for fast data lookup. These skills are essential for Database Administrators who want to be able to design and maintain efficient and scalable databases.
Information Security Analyst
Information Security Analyst
Information Security Analysts use their knowledge of information security to protect organizations from cyber attacks. This course, Applying MapReduce to Common Data Problems, can help you develop the skills needed to succeed in this role by providing you with a solid understanding of how to use MapReduce to solve common data problems. You'll learn how to summarize numeric data, filter large datasets, and build an index for fast data lookup. These skills are essential for Information Security Analysts who want to be able to effectively protect organizations from cyber attacks.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data pipelines. This course, Applying MapReduce to Common Data Problems, can help you develop the skills needed to succeed in this role by providing you with a solid understanding of how to use MapReduce to solve common data problems. You'll learn how to summarize numeric data, filter large datasets, and build an index for fast data lookup. These skills are essential for Data Engineers who want to be able to build efficient and scalable data pipelines.
Cloud Architect
Cloud Architect
Cloud Architects design, develop, and maintain cloud computing solutions. This course, Applying MapReduce to Common Data Problems, can help you develop the skills needed to succeed in this role by providing you with a solid understanding of how to use MapReduce to solve common data problems. You'll learn how to summarize numeric data, filter large datasets, and build an index for fast data lookup. These skills are essential for Cloud Architects who want to be able to design and maintain efficient and scalable cloud computing solutions.
Data Warehouse Engineer
Data Warehouse Engineer
Data Warehouse Engineers design, develop, and maintain data warehouses. This course, Applying MapReduce to Common Data Problems, can help you develop the skills needed to succeed in this role by providing you with a solid understanding of how to use MapReduce to solve common data problems. You'll learn how to summarize numeric data, filter large datasets, and build an index for fast data lookup. These skills are essential for Data Warehouse Engineers who want to be able to design and maintain efficient and scalable data warehouses.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use their knowledge of mathematics, statistics, and programming to develop financial models. This course, Applying MapReduce to Common Data Problems, can help you develop the skills needed to succeed in this role by providing you with a solid understanding of how to use MapReduce to solve common data problems. You'll learn how to summarize numeric data, filter large datasets, and build an index for fast data lookup. These skills are essential for Quantitative Analysts who want to be able to develop accurate and reliable financial models.
Actuary
Actuary
Actuaries use their knowledge of mathematics, statistics, and finance to assess risk. This course, Applying MapReduce to Common Data Problems, can help you develop the skills needed to succeed in this role by providing you with a solid understanding of how to use MapReduce to solve common data problems. You'll learn how to summarize numeric data, filter large datasets, and build an index for fast data lookup. These skills are essential for Actuaries who want to be able to effectively assess risk.
Systems Engineer
Systems Engineer
Systems Engineers design, develop, and maintain computer systems. This course, Applying MapReduce to Common Data Problems, can help you develop the skills needed to succeed in this role by providing you with a solid understanding of how to use MapReduce to solve common data problems. You'll learn how to summarize numeric data, filter large datasets, and build an index for fast data lookup. These skills are essential for Systems Engineers who want to be able to design and maintain efficient and scalable computer systems.
Data Mining Engineer
Data Mining Engineer
Data Mining Engineers use their knowledge of data mining techniques to extract meaningful information from data. This course, Applying MapReduce to Common Data Problems, can help you develop the skills needed to succeed in this role by providing you with a solid understanding of how to use MapReduce to solve common data problems. You'll learn how to summarize numeric data, filter large datasets, and build an index for fast data lookup. These skills are essential for Data Mining Engineers who want to be able to effectively extract meaningful information from data.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and maintain machine learning models. This course, Applying MapReduce to Common Data Problems, can help you develop the skills needed to succeed in this role by providing you with a solid understanding of how to use MapReduce to solve common data problems. You'll learn how to summarize numeric data, filter large datasets, and build an index for fast data lookup. These skills are essential for Machine Learning Engineers who want to be able to develop and maintain efficient and scalable machine learning models.
For more career information including salaries, visit:
OpenCourser.com/course/m1try9/applying
Reading list
We've selected 11 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
Applying MapReduce to Common Data Problems.
Provides a comprehensive overview of MapReduce design patterns and best practices, making it a valuable resource for those looking to enhance their MapReduce skills.
This comprehensive guide to Hadoop provides a solid foundation for understanding the framework and its applications, including MapReduce.
Focuses on using MapReduce for text processing tasks, providing practical insights and techniques for handling large-scale text data.
Save
This practical guide covers the core concepts of MapReduce, providing step-by-step examples and case studies for real-world applications.
Provides a more advanced look at data analytics using Spark, which builds on the concepts of MapReduce but offers additional capabilities.
Save
An introduction to Spark, a popular framework for large-scale data processing that builds on the ideas behind MapReduce, offering a useful companion reference.
Save
Provides practical guidance on managing, operating, and troubleshooting Hadoop clusters, which is essential for those working with large-scale data processing systems.
Offers a comprehensive overview of big data analytics, including its strategic and operational aspects, providing context for the use of MapReduce.
Provides a broader perspective on data science and its applications in business contexts, offering valuable insights for those interested in the business value of MapReduce.
An accessible introduction to machine learning and data mining concepts, providing a helpful foundation for understanding the applications of MapReduce in these areas.
Covers Python programming for data analysis tasks, providing a valuable companion reference for those interested in using Python with MapReduce.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/m1try9/applying
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Time
122 hours
Level
Introductory
Via
Pluralsight
Instructor
Janani Ravi
Language
English
Good to know
Explores key MapReduce patterns that are relevant to industry, such as summarizing numeric data, filtering large datasets, and building an index for fast data lookup
Taught by Janani Ravi, an recognized expert in the field of data engineering
Develops the foundational block of programming models by offering a clear understanding of how to "Think MapReduce"
Suitable for learners with some prior experience in programming and data structures
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