This course provides foundational knowledge and skills to become a data engineer, covering the data ecosystem, tools, career paths, and real-world tasks.

80% of a data scientist's job is data preparation. This course is all about data preparation i.e. cleaning, transforming, summarizing data using Spark.

This course will teach you how to build a comprehensive data architecture roadmap aligned with your organization's business objectives, including state analyses, state definitions, actionable item identification, implementation sequencing, and more.

Get started with Big Data fundamentals. Learn the key concepts, tools, and technologies used to analyze and manage large datasets in modern data ecosystems.

This course introduces you to essential concepts in handling vast datasets, equipping you with the ability to implement an effective data solution.

Learn the fundamentals of working with big data with PySpark.

This course contains an overview and demonstration of numerous components in the Amazon Web Services (AWS) Big Data Stack.

Learn about the architecture and management of big data systems.

Big Data is the present and the future. It enables you to make informed decisions and predictions based on insights obtained from your data. In this course you will learn why big data matters, how it is changing the world, and what you can do about it.

Big Data technology has gone from hype-driven to enterprise-ready. This course will teach you about Big Data concepts, major technologies and dominant vendors, as well as strategic approaches for adopting Big Data analytics in your organization.

Pipelines are the lifeblood of a data analytics solution. Learn how to use Azure Synapse Analytics pipelines to build integrated data solutions that extract, transform, and load data across diverse systems.

Transactions and error handling are great, but they have some pitfalls that it helps to know about. This course will teach you how to work with transactions and error handling to make your system efficient, well-performing, and observable.