We may earn an affiliate commission when you visit our partners.
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.

Enroll now

Here's a deal for you

We found an offer that may be relevant to this course.
Save money when you learn. All coupon codes, vouchers, and discounts are applied automatically unless otherwise noted.

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

Save this course

Save Big Data Essentials to your list so you can find it easily later:
Save

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 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.
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.
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.
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.
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.
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.

Share

Help others find this course page by sharing it with your friends and followers:

Similar courses

Here are nine courses similar to Big Data Essentials.
Introduction to Big Data with Spark and Hadoop
Most relevant
Cloud Computing Applications, Part 2: Big Data and...
Most relevant
Introduction to Big Data
Most relevant
Intro to Hadoop and MapReduce
Most relevant
Big Data, Hadoop, and Spark Basics
Most relevant
SQL Big Data Convergence - The Big Picture
Most relevant
Introduction to PySpark
Most relevant
Data Modeling, Transformation, and Serving
Most relevant
Introduction to Big Data Analytics on GCP
Most relevant
Our mission

OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.

Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.

Find this site helpful? Tell a friend about us.

Affiliate disclosure

We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.

Your purchases help us maintain our catalog and keep our servers humming without ads.

Thank you for supporting OpenCourser.

© 2016 - 2024 OpenCourser