The editors at Solutions Review have compiled this list of the best LinkedIn Learning big data courses (top-rated) to consider taking.
Big data skills are in high demand among organizations that are looking to use their collected data to generate valuable business insight. The pandemic and subsequent ‘new normal’ of remote work are furthering demands for these skills. Many are turning to online learning platforms to up their game and acquire the big data skills most likely to help them stand out. And whether you are looking to acquire those skills for work or for play, this collection of Pluralsight big data courses will help you learn the ropes so you can pilot some of the most widely used tools in no time!
With this in mind, the editors at Solutions Review have compiled this list of the best LinkedIn Learning big data courses to consider taking. The platform is perfect for those looking to take multiple courses or acquire skills in different areas, or for those who want the most in-depth experience possible through access to LinkedIn Learning’s entire course library or learning paths. In sum, LinkedIn Learning offers training in more than 13 distinct categories with thousands of modules.
Note: Our editors assembled this directory by listing the best LinkedIn Learning big data courses in the most popular associated coverage areas including Hadoop, data warehousing, ETL (Extract, Transform, Load), Cloudera, Apache Cassandra, data management, and MuleSoft.
Description: In this course, discover how to build big data pipelines around Apache Spark. Join Kumaran Ponnambalam as he takes you through how to make Apache Spark work with other big data technologies. He covers the basics of Apache Kafka Connect and how to integrate it with Spark for real-time streaming. In addition, he demonstrates how to use various technologies to construct an end-to-end project that solves a real-world business problem.
Related paths/tracks: Big Data in the Age of AI, Architecting Big Data Applications: Real-Time Application Engineering
Description: In this 2-hour long project-based course, you will learn the basics of using Power BI Desktop software. We will do this by analyzing data on credit card defaults with Power BI Desktop. Power BI Desktop is a free business intelligence application from Microsoft that lets you load, transform, and visualize data. You can create interactive reports and dashboards quite easily, and quickly. We will learn some of the basics of Power BI by importing, transforming, and visualizing the data.
Description: Learn to design data models, build data warehouses and data lakes, automate data pipelines, and work with massive datasets. At the end of the program, you’ll combine your new skills by completing a capstone project. Students will learn to create relational and NoSQL data models to fit the diverse needs of data consumers, as well as sharpen your data warehousing skills and deepen your understanding of data infrastructure.
Related path/track: Data Science on Google Cloud Platform: Designing Data Warehouses
Description: This course is designed for professionals with experience ranging from zero to already skilled professionals. Hands-on sessions cover the end-to-end setup of a Cloudera Cluster (using AWS EC2 instances to deploy the cluster). This training is targeted toward software engineers, system analysts, database administrators, DevOps engineers, and system administrators. Other IT professionals can also take the course, but might have to do some extra work to understand some of the advanced concepts.
Description: In this course, learn about the architecture of this popular database, and discover how to design Cassandra data models that support scalable applications. Dan Sullivan highlights the differences between Cassandra and relational databases, discusses the Cassandra Query Language (CQL), and shows techniques for modeling based on application query requirements. He also dives into Cassandra implementation details that impact data modeling choices, to help you reason through other design decisions while taking into account the database’s architecture and limitations.
Description: In the era of big data and data science, most businesses and institutions realize the power of data. Yet far too many fail to appreciate the legal and fiscal responsibilities and liabilities associated with it. The stakes are high, but a well-rounded data governance process can help ensure the consistent quality, availability, integrity, and usability of your data.
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