NoSQL Database Basics
Explore NoSQL database architecture through interactive learning with popular platforms, essential principles and real-world deployment strategies.
Explore NoSQL database architecture through interactive learning with popular platforms, essential principles and real-world deployment strategies.
This comprehensive course introduces you to NoSQL database fundamentals and provides practical experience with leading platforms. Learn about the four main categories of NoSQL databases and their distinct characteristics. Get hands-on experience with MongoDB, Cassandra, and IBM Cloudant, performing essential database management tasks including creation, replication, querying, and data indexing. The course covers crucial concepts like scalability, distributed databases, and the CAP theorem, preparing you for real-world data management challenges.
4.4
(66 ratings)
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What you'll learn
Define NoSQL technology and understand its key characteristics
Master the four main categories of NoSQL datastores and their architectural differences
Gain practical experience with MongoDB for document database management
Learn Cassandra architecture and implementation for distributed databases
Develop skills in IBM Cloudant for cloud-based NoSQL solutions
Understand factors affecting database deployment decisions
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
Shareable certificate
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There are 5 modules in this course
This course provides a comprehensive introduction to NoSQL databases and their role in modern data management. Students learn about different NoSQL categories including document, key-value, column, and graph databases. The curriculum covers practical implementation using popular platforms like MongoDB, Cassandra, and IBM Cloudant. Through hands-on labs and projects, participants gain experience with database operations, data modeling, querying, and performance optimization. The course emphasizes real-world applications and best practices for working with distributed databases.
Introducing NoSQL
Module 1
Introducing MongoDB
Module 2
Introducing Apache Cassandra
Module 3
Introducing IBM Cloudant
Module 4
Final Project
Module 5
Fee Structure
Instructors
Technology Education and Skills Development Leader at IBM
Rav Ahuja serves as the Chief Content Officer and Global Program Director at IBM Skills Network, where he leads curriculum creation, growth strategy, and partner programs. After earning his B.Eng. from McGill University and MBA from the University of Western Ontario, he co-founded Cognitive Class, an IBM initiative focused on democratizing access to in-demand technology skills. Based at the IBM Canada Lab in Toronto, he specializes in developing instructional solutions for AI, Data Science, Cloud Computing, and Cybersecurity. His impact on technology education is evidenced through his role as architect of numerous IBM Professional Certificates and instructor for over 35 online courses, including popular offerings like "What is Data Science?", "Introduction to Cloud Computing," and "Introduction to Artificial Intelligence (AI)." His courses have reached hundreds of thousands of learners worldwide, with "What is Data Science?" alone enrolling over 638,000 students. His recent work includes developing new Generative AI courses and career guidance content for IBM's Professional Certificate programs, demonstrating his ongoing commitment to preparing learners for emerging technology careers
Data Engineering and Technology Education Expert
Ramesh Sannareddy serves as a freelance technology educator and content developer, bringing over two and a half decades of experience in Information Technology Infrastructure Management, Database Administration, and Information Integration. After earning his Bachelor's Degree in Information Systems from Birla Institute of Technology, Pilani, he built an impressive career working with leading technology companies including Intergraph, Genpact, HCL, and Microsoft. Currently focused on his passion for teaching, he specializes in developing and delivering courses in Data Science, Machine Learning, Programming, and Databases. His educational impact is evidenced through his extensive course portfolio, which includes specialized programs in Data Engineering, Data Warehousing, Linux Commands, Machine Learning with Apache Spark, and Python Programming. His teaching reaches over 11,800 learners globally, maintaining a strong 4.5 rating for his educational content
Testimonials
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4.4 course rating
66 ratings
Frequently asked questions
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