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Querying Data Using Map-reduce in MongoDB

Buddhini Samarakkody

MongoDB’s Map-reduce stands strong as the number one choice for big data analytics. Learn about condensing a large volume of document data into a small set of aggregated results by creating versatile map and reduce functions in JavaScript.

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MongoDB’s Map-reduce stands strong as the number one choice for big data analytics. Learn about condensing a large volume of document data into a small set of aggregated results by creating versatile map and reduce functions in JavaScript.

Working with large volumes of document data for analytics requires the power and flexibility of MongoDB’s Map-reduce feature. In this course, Querying Data Using Map-reduce in MongoDB, you’ll gain the ability to get yourself fully equipped to confidently apply the Map-reduce pattern to any data set no matter how large it could be. First, you’ll discover the requirement for Map-reduce among other aggregation capabilities of MongoDB. Next, you’ll explore how to create a custom JavaScript map function and a reduce function that are needed to perform map and reduce operations. Finally, you’ll learn how to use these custom functions to perform a Map-reduce operation on a MongoDB data set to aggregate results across documents. When you’re finished with this course, you’ll have the skills and knowledge of working with the Map-reduce function in MongoDB that will help you leverage the power of MongoDB’s aggregation feature for data crunching requirements in your next project.

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What's inside

Syllabus

Course Overview
Discovering the Need for Map-reduce in MongoDB
Creating Custom JavaScript Functions for Map and Reduce
Applying Custom JavaScript Functions to MongoDB Map-reduce
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Putting the Pieces Together: Build a Complete Map-reduce Solution in MongoDB

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Teaches a core technique for big data analytics in the industry
Employs JavaScript for practical implementation of map and reduce
Facilitates customized aggregation of document data

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Career center

Learners who complete Querying Data Using Map-reduce in MongoDB will develop knowledge and skills that may be useful to these careers:
Big Data Architect
Big Data Architects design, develop, and manage big data systems. This course provides the opportunity to gain experience working with big data and using mapreduce functions to analyze data. This may be a useful course to consider to help build a foundation in designing, developing, and managing big data systems.
Data Engineer
Data Engineers design, build, and maintain data pipelines and systems. This course provides the opportunity to gain experience working with big data and using mapreduce functions to analyze data. This may be a useful course to consider to help build a foundation in retrieving and analyzing data for use in developing and maintaining data pipelines and systems.
Data Scientist
Data Scientists use data to solve complex business problems. This course provides the opportunity to gain experience working with big data and using mapreduce functions to aggregate data. This may be a useful course to consider to help build a foundation in retrieving and analyzing data for use in solving business problems.
Market Research Analyst
Marketing Research Analysts conduct surveys, collect data, and analyze data to determine the effectiveness of marketing and advertising campaigns or to identify new markets and opportunities for products and services. This course can help students build a foundation in collecting and analyzing data, including leveraging mapreduce functions to condense large volumes of data into summarized results.
Statistician
Statisticians collect, analyze, and interpret data to draw conclusions. This course can help Statisticians build a foundation in collecting and analyzing data, including leveraging mapreduce functions to condense large volumes of data into summarized results.
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze data and make predictions. This course may be useful for Quantitative Analysts who wish to enhance their data science skills, particularly in working with big data and mapreduce functions to analyze data.
Data Analyst
Data Analysts collect data from internal databases and/or external sources and analyze it for use in marketing and advertising campaigns, product development, or financial forecasting. This course provides the opportunity to gain experience working with big data and using mapreduce functions to aggregate data. This may be a useful course to consider to help build a foundation in retrieving and analyzing data for use in marketing, advertising, and product development.
Database Administrator
Database Administrators maintain and manage databases. This course can help Database Administrators build a foundation in managing and maintaining databases, including how to analyze data using mapreduce functions.
Financial Analyst
Financial Analysts provide financial advice and guidance to individuals and businesses. This course may be useful for Financial Analysts who wish to enhance their data science skills, particularly in working with big data and mapreduce functions to analyze data.
Risk Analyst
Risk Analysts identify, assess, and manage risks. This course may be useful for Risk Analysts who wish to enhance their data science skills, particularly in working with big data and mapreduce functions to analyze data.
Business Analyst
Business Analysts use data to identify opportunities, solve problems, and provide recommendations to improve business processes. This course may be useful for Business Analysts who wish to enhance their data science skills, particularly in working with big data and mapreduce functions to analyze data.
Data Architect
Data Architects design and oversee the implementation of data systems. This course can help Data Architects build a foundation in designing and implementing data systems, including how to analyze data using mapreduce functions.
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy machine learning models. This course may be useful for Machine Learning Engineers who wish to learn more about using mapreduce functions to analyze data. Mapreduce functions are commonly used in big data processing, so this course can help Machine Learning Engineers who work with big data to become more efficient.
Software Engineer
Software Engineers develop, maintain, and improve software applications. This course may be useful for Software Engineers who wish to learn more about using mapreduce functions to analyze data. Mapreduce functions are commonly used in big data processing, so this course can help Software Engineers who work with big data to become more efficient.

Reading list

We've selected nine 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 Querying Data Using Map-reduce in MongoDB.
Provides a comprehensive overview of MapReduce design patterns. It valuable resource for anyone who wants to learn more about how to use MapReduce for data analysis.
Provides a practical guide to using Java for big data analytics. It covers a wide range of topics, including data cleaning, aggregation, and visualization.
Provides a comprehensive overview of Hadoop, a popular big data analytics platform. It covers a wide range of topics, including data loading, processing, and storage.
Provides a practical guide to using data science for business applications. It covers a wide range of topics, including data collection, analysis, and visualization.
Provides a comprehensive overview of machine learning for data science. It covers a wide range of topics, including data preparation, model selection, and evaluation.
Provides a practical guide to using Python for deep learning. It covers a wide range of topics, including neural networks, convolutional neural networks, and recurrent neural networks.
Provides a practical guide to using Python for data analysis. It covers a wide range of topics, including data cleaning, aggregation, and visualization.
Provides a practical guide to using R for data science. It covers a wide range of topics, including data cleaning, aggregation, and visualization.
Provides a comprehensive overview of big data analytics, from strategic planning to enterprise integration. It valuable resource for anyone who wants to learn more about how to use big data analytics to improve their business.

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