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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.
Register for this course and see more details by visiting:
OpenCourser.com/course/s09eih/big
Good to know
Good to know
Know what's good ,
what to watch for , 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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Save Big Data Essentials 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 Big Data Essentials with these
activities:
Connect with Big Data Analytics Professionals
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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.
Connect with Big Data Analytics Professionals
Show steps
Seek guidance and support from experienced big data analytics professionals to accelerate your learning journey.
Browse courses on
Big Data Analytics
Show steps
Show steps
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
Show steps
Review the Hadoop Ecosystem to strengthen your foundational understanding of key technologies used in big data processing.
Show steps
Show steps
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
Show steps
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
Show steps
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
Show steps
Engage with peers to exchange ideas, clarify concepts, and expand your perspectives on big data analytics.
Browse courses on
Big Data Analytics
Show steps
Show steps
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
Show steps
Engage in hands-on practice to enhance your proficiency in big data analytics techniques.
Browse courses on
Big Data Analytics
Show steps
Show steps
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
Show steps
Expand your knowledge of advanced big data technologies to deepen your understanding of the field.
Browse courses on
Big Data Technologies
Show steps
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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:
For more career information including salaries, visit:
OpenCourser.com/course/s09eih/big
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
Big Data Essentials.
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.
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.
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.
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.
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.
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.
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.
Save
Provides a comprehensive overview of computer vision, a subfield of artificial intelligence that gives computers the ability to see and understand the world around them. It would be a useful resource for students who want to learn more about the different techniques used in computer vision.
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.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/s09eih/big
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Level
Intermediate
Via
Pluralsight
Instructor
A Cloud Guru
Language
English
Good to know
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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