The DeepLearning.AI Data Engineering Professional Certificate is a comprehensive online program for data engineers and practitioners looking to start or grow their careers.

Organizations of all sizes and across all industries are capturing and generating data at an ever-increasing pace. Within these organizations, every team, from executives, sales and marketing, finance and operations, product and engineering, to customer service, can derive insights and value from organizational data. Whether the end use case is data science, machine learning, or analytics, data engineering is what allows raw data to be converted to value for the business. This is why the role of data engineer is one of the highest-demand jobs in tech today.

Throughout this program, you'll learn the foundations of data engineering while gaining hands-on experience designing and implementing data architectures using AWS and open-source tools.

Taught by industry expert Joe Reis, co-author of Fundamentals of Data Engineering, this certificate equips you with the skills and knowledge to excel in a high-demand field, focusing on ingesting, processing, transforming, storing, and serving data to data stakeholders to drive organizational and business objectives. The practical labs were developed in partnership with AWS and Factored.AI to provide you with an authentic experience building data systems on the cloud.

With this certificate, you will have the tools to further your data engineering career.

Praktisches Lernprojekt

In this program, you'll: 

• Translate stakeholder needs into system requirements and choose the appropriate tools to build the system.

• Build end-to-end batch and streaming pipelines on AWS for a product recommendation system.

• Apply principles of good data architecture to assess the security, performance, reliability, and scalability of data systems on AWS.

• Explore various types of source systems and troubleshoot common connectivity issues.

• Use infrastructure and pipelines as code tools to orchestrate, automate, and monitor your data pipelines.

• Design data lake and data lakehouse storage architectures for various use cases.

• Explore the impact of data storage choices on query performance and cost.

• Model and transform data for analytics and machine learning use cases and compare centralized processing frameworks such as Pandas with distributed processing frameworks like Spark.

• Serve your data to downstream data stakeholders for business analytics and machine learning use cases.