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Big Data Essentials

A Cloud Guru

Big Data Essentials is a comprehensive introduction to the world of big data. Starting with the definition of big data, we describe the various characteristics of big data and its sources. Using real world examples, we highlight the growing importance of big data. We discuss architectural requirements, principles of big data infrastructures, and the intersection of cloud computing with big data. We also provide an overview of the most popular big data technologies including core Hadoop, the Hadoop ecosystem (Hive, Pig, Sqoop, Flume, Kafka, Storm, Ambari, Oozie, Zookeeper), NoSQL databases, and Apache Spark. We conclude this lesson with a tour of the different types of analytics that can be performed on big data as well as the various techniques and tools used.

This course is no longer available. Find something similar by browsing:
Big Data Hadoop NoSQL Apache Spark Data Analytics Cloud Computing Data Warehousing

Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Includes case studies to strengthen the understanding of concepts
In-depth walkthroughs of core Hadoop technologies and ecosystem tools
overview of popular big data technologies: Hadoop, Spark, NoSQL
Taught by leading educators in the field of big data
Introduces architectural requirements for big data infrastructure
Explores the intersection of cloud computing and big data

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Reviews summary

Foundational big data concepts overview

According to learners, "Big Data Essentials" provides a solid foundational understanding of big data. Students highlight its ability to offer a comprehensive overview of key concepts, architectural principles, and a wide array of popular big data technologies like Hadoop, Spark, and NoSQL. The lectures are often praised for their clarity, making complex topics digestible for beginners and professionals seeking conceptual knowledge. However, a significant number of reviews point out a lack of hands-on practical application, suggesting the course is more theoretical. Some reviews also suggest the content could become outdated quickly due to the fast-evolving nature of the field. Overall, it's seen as an excellent starting point for conceptual understanding but requires supplemental study for practical skills.
Instructors clearly explain complex big data concepts and principles.
"The explanations are clear, making complex topics digestible."
"The instructors explain concepts very well."
"The instructor's explanations were very clear, especially for the complex topics."
"It clearly laid out the 'why' behind big data infrastructure."
Ideal for conceptual understanding, less for hands-on technical skills.
"Perfect for getting a broad understanding before diving deeper."
"Useful for managers or those needing a conceptual understanding, less so for aspiring data engineers."
"It sets a good stage for further learning."
"Ideal for a professional seeking to understand the domain."
Provides a broad and foundational understanding of big data concepts.
"Fantastic introduction to big data concepts! The course provides a solid foundation, touching upon all major technologies..."
"Good overview for beginners. I appreciated the breadth of topics covered, from architecture to tools."
"An excellent foundational course for anyone new to big data. It demystifies the jargon and provides a clear roadmap..."
"Perfect for a high-level overview. If you need to understand the big data landscape, this course delivers."
Some content may be outdated due to rapid technology evolution.
"While it touched on various big data tools, some of the information felt a bit dated, particularly the specific versions of Hadoop ecosystem tools mentioned."
"The material is okay for an introduction, but it feels like it hasn't been updated in a while."
"Some technologies evolve fast, and the course content should reflect that. It's a bit too general."
Emphasizes concepts and principles over practical application.
"My only minor critique is that it's more theoretical than practical; I would have loved more hands-on labs with real data."
"It gives you a lot of names and concepts but not enough on how to actually *use* them."
"Disappointed. This course is just a glossary of terms. It barely scratches the surface of actual big data processing. No real projects, just theory."
"I expected more practical application and less high-level theory. Not ideal if you're looking to gain actionable skills immediately."

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 Big Data Essentials with these activities:
Connect with Big Data Analytics Professionals
Seek guidance and support from experienced big data analytics professionals to accelerate your learning journey.
Browse courses on Big Data Analytics
Show steps
  • Identify potential mentors through professional networking platforms like LinkedIn.
  • Reach out to individuals who align with your career aspirations and request guidance.
  • Schedule regular meetings or calls to receive personalized advice and insights.
Review Hadoop Ecosystem
Review the Hadoop Ecosystem to strengthen your foundational understanding of key technologies used in big data processing.
Show steps
  • Identify the components of the Hadoop Ecosystem, such as HDFS, Yarn, MapReduce, Hive, Pig, Sqoop, Flume, Kafka, Storm, Ambari, Oozie, and Zookeeper.
  • Describe the purpose and functionality of each component.
  • Explore real-world examples of how these components are used in big data applications.
Curate a Big Data Analytics Resource Collection
Gather and organize a comprehensive collection of big data analytics resources to support your learning and future reference.
Browse courses on Big Data Analytics
Show steps
  • Identify and collect valuable resources such as articles, tutorials, books, and datasets.
  • Categorize and organize the resources based on topics or themes.
  • Store the collection in a centralized location like a digital notebook or online platform.
  • Continuously update and expand the collection as new resources become available.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Engage in Big Data Analytics Discussions
Engage with peers to exchange ideas, clarify concepts, and expand your perspectives on big data analytics.
Browse courses on Big Data Analytics
Show steps
  • Participate in online forums or discussion groups dedicated to big data analytics.
  • Attend webinars or meetups to connect with industry experts and fellow learners.
  • Collaborate on projects or case studies with peers to enhance your teamwork and problem-solving skills.
Practice Big Data Analytics Techniques
Engage in hands-on practice to enhance your proficiency in big data analytics techniques.
Browse courses on Big Data Analytics
Show steps
  • Apply data visualization techniques to explore and analyze big data.
  • Utilize statistical and machine learning algorithms for big data analysis.
  • Experiment with different big data analytics tools and technologies.
Explore Advanced Big Data Technologies
Expand your knowledge of advanced big data technologies to deepen your understanding of the field.
Browse courses on Big Data Technologies
Show steps
  • Seek out tutorials on emerging big data technologies such as Apache Spark, Flink, and Cassandra.
  • Follow tutorials to implement these technologies in practical scenarios.
  • Experiment with different tools and frameworks to enhance your proficiency.
Build a Personal Big Data Analytics Portfolio
Create a showcase of your big data analytics skills and projects to demonstrate your capabilities to potential employers.
Browse courses on Big Data Analytics
Show steps
  • Identify a portfolio theme that aligns with your career goals.
  • Select projects that highlight your proficiency in different big data analytics techniques.
  • Develop a professional portfolio website or utilize platforms like GitHub to showcase your work.
  • Continuously update your portfolio with new projects and accomplishments.
Develop a Big Data Analytics Project
Apply your acquired knowledge and skills to create a comprehensive big data analytics project that showcases your abilities.
Browse courses on Big Data Analytics
Show steps
  • Define a problem statement and gather relevant data.
  • Apply appropriate big data analytics techniques to analyze the data.
  • Develop visualizations and insights to communicate your findings.
  • Present your project to demonstrate your understanding of big data analytics.

Career center

Learners who complete Big Data Essentials will develop knowledge and skills that may be useful to these careers:

Reading list

We've selected eight 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 Big Data Essentials.
Cover image
Data Science for Business
Save
Would be a great addition to the course because it provides a comprehensive overview of data science, including the basics of big data. It would be particularly useful for students who are new to the field.
Data Science for Business: What You Need to Know...
Paperback
$$
Data Science for Business: What You Need to Know...
Kindle Edition
$$
Cover image
Hadoop: The Definitive Guide
Save
Comprehensive guide to Hadoop, the open-source framework for storing and processing big data. It would be a valuable resource for students who want to learn more about the technical aspects of big data.
Hadoop: The Definitive Guide: Storage and Analysis...
Paperback
$$
Hadoop: The Definitive Guide
Kindle Edition
$$
Hadoop: The Definitive Guide
Paperback
$$
Hadoop: The Definitive Guide
Paperback
$$
Cover image
Machine Learning
Save
Provides a comprehensive overview of machine learning, a subfield of artificial intelligence that gives computers the ability to learn from data. It would be a useful resource for students who want to learn more about the different techniques used in machine learning.
Machine Learning (McGraw-Hill International...
Paperback
$$$
Cover image
NoSQL Distilled
Save
Provides a comprehensive overview of NoSQL databases, which are a type of database that is designed to handle large amounts of data. It would be a useful resource for students who want to learn more about the different types of NoSQL databases and how they can be used.
NoSQL Distilled: A Brief Guide to the Emerging...
Kindle Edition
$$
Cover image
Data Mining
Save
Provides a comprehensive overview of data mining, a process of extracting knowledge from data. It would be a useful resource for students who want to learn more about the different techniques used in data mining.
Data Mining: Practical Machine Learning Tools and...
Paperback
$$$
DATA MINING, 4TH EDITION
Paperback
$$
Data Mining: Practical Machine Learning Tools and...
Kindle Edition
$$
Cover image
Deep Learning
Save
Provides a comprehensive overview of deep learning, a subfield of machine learning that uses artificial neural networks to learn from data. It would be a useful resource for students who want to learn more about the different techniques used in deep learning.
Deep Learning
Paperback
$$$
Deep Learning (Adaptive Computation and Machine...
Hardcover
$$$
Deep Learning (Adaptive Computation and Machine...
Kindle Edition
$$$
Cover image
Reinforcement Learning, second edition
Save
Provides a comprehensive overview of reinforcement learning, a type of machine learning that allows computers to learn from their mistakes. It would be a useful resource for students who want to learn more about the different techniques used in reinforcement learning.
Reinforcement Learning, second edition: An...
Hardcover
$$$
Reinforcement Learning, second edition: An...
Kindle Edition
$$$
Cover image
Natural Language Processing with Python
Save
Provides a comprehensive overview of natural language processing, a subfield of artificial intelligence that gives computers the ability to understand and generate human language. It would be a useful resource for students who want to learn more about the different techniques used in natural language processing.
Natural Language Processing with Python: Analyzing...
Paperback
$$$
Natural Language Processing with Python: Analyzing...
Kindle Edition
$$

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Course image
Level
Intermediate
Via
Pluralsight
Instructor
A Cloud Guru
Language
English
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Includes case studies to strengthen the understanding of concepts
In-depth walkthroughs of core Hadoop technologies and ecosystem tools
overview of popular big data technologies: Hadoop, Spark, NoSQL
Taught by leading educators in the field of big data
Introduces architectural requirements for big data infrastructure
Explores the intersection of cloud computing and big data
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