NoSQL, Big Data, and Spark Foundations

Get started with NoSQL Databases with this beginner-friendly introductory course! This course will provide technical, hands-on knowledge of NoSQL databases and Database-as-a-Service (DaaS) offerings. With the advent of Big Data and agile development methodologies, NoSQL databases have gained a lot of relevance in the database landscape. Their main advantage is the ability to handle scalability and flexibility issues modern applications raise. You will start this course by learning the history and the basics of NoSQL databases (document, key-value, column, and graph) and discover their key characteristics and benefits. You will learn about the four categories of NoSQL databases and how they differ. You’ll also explore the differences between the ACID and BASE consistency models, the pros and cons of distributed systems, and when to use RDBMS and NoSQL. You will also learn about vector databases, an emerging class of databases popular in AI. Next, you will explore the architecture and features of several implementations of NoSQL databases, namely MongoDB, Cassandra, and IBM Cloudant. You will learn about the common tasks that they each perform and their key and defining characteristics. You will then get hands-on experience using those NoSQL databases to perform standard database management tasks, such as creating and replicating databases, loading and querying data, modifying database permissions, indexing and aggregating data, and sharding (or partitioning) data. At the end of this course, you will complete a final project where you will apply all your knowledge of the course content to a specific scenario and work with several NoSQL databases. This course suits anyone wanting to expand their Data Management and Information Technology skill set.

NOTE: This course is currently replaced with IBM Machine Learning with Apache Spark.

Further your data engineering career with this self-paced course about machine learning with Apache Spark! Organizations need skilled, forward-thinking Big Data practitioners who can apply their business and technical skills to unstructured data such as tweets, posts, pictures, audio files, videos, sensor data, and satellite imagery and more to identify behaviors and preferences of prospects, clients, competitors, and others.

In this short course you'll gain these practical skills when you learn how to work with Apache Spark for Data Engineering and Machine Learning (ML) applications. You will work hands-on with Spark MLlib, Spark Structured Streaming, and more to perform extract, transform and load (ETL) tasks as well as Regression, Classification, and Clustering.

In this course you will learn about data sources, streaming output modes, and supported data destinations. You will gain insights about the advantages of Apache Spark GraphFrames and complete a number of hands-on labs to apply your knowledge.

You will then move on to learning about machine learning using SparkML, the Spark Machine Learning library. You will gain an understanding of both supervised and unsupervised machine learning, classification and regression tasks, as well as clustering.

The course ends with a final project where you will create your own Apache Spark application for performing Extract, Transform, and Load (ETL) processes.

NOTE: This course requires that you have foundational skills for working with Apache Spark and Jupyter Notebooks. The Introduction to Big Data with Spark and Hadoop course from IBM will equip you with these skills and it is recommended that you have completed that course or have skills similar to the ones learnt in that course.