IBM Data Engineering

Description

This professional program provides a comprehensive foundation in data engineering, equipping participants with essential technical skills to manage, process, and transform large datasets for business intelligence and analytics. The curriculum is designed to bridge the gap between data science and IT operations, preparing students to become proficient data engineers capable of working in fast-paced, data-driven environments. The program begins with an introduction to data engineering, where students explore the fundamental concepts of data pipelines, data architecture, and ETL (Extract, Transform, Load) processes. Students then move on to relational database administration (DBA) and relational database management systems (RDBMS), where they learn to manage, query, and maintain relational databases. This section emphasizes SQL proficiency and the skills needed to support structured data storage. Students gain hands-on experience with data warehousing fundamentals, where they learn to design and manage data warehouses for large-scale analytics. This knowledge is further expanded with an introduction to NoSQL databases, enabling students to understand and work with non-relational database systems for unstructured and semi-structured data. The curriculum also covers ETL and data pipelines, with a focus on using tools like Shell, Apache Airflow, and Kafka to automate and orchestrate data workflows. Students then progress to machine learning with Apache Spark, which provides them with the knowledge to process large datasets and build machine learning models for advanced data analysis. The program introduces students to big data technologies, including Spark and Hadoop, which are essential for processing and analyzing massive datasets. Students also explore hands-on Linux commands and shell scripting, enabling them to manage server environments and automate routine tasks. The integration of cloud platforms is also a key focus, with students learning configuration management and cloud data integration techniques. A significant part of the program is the exploration of business intelligence (BI) dashboards using IBM Cognos Analytics and Google Looker, giving students the ability to visualize data insights and support decision-making processes. Students are also trained on Python for Data Science, AI, and development, as well as using Python to build data engineering projects. To consolidate their learning, students complete a data engineering capstone project where they apply all the skills learned throughout the program. The project includes the design, development, and deployment of a full data pipeline, from data ingestion to visualization. Students also receive a comprehensive data engineering career guide and interview preparation, ensuring they are ready for roles as data engineers, ETL developers, or big data specialists.