The goal of the practical project-based course End-to-End Data Engineering Project using Databricks Free Edition is to assist students in creating a real-world data engineering pipeline from the ground up. Learners use industry-standard tools and procedures to ingest, convert, orchestrate, and visualize data inside the Databricks ecosystem using a business [...]

DLT Fundamentals is a free, practical course that uses the open-source dlt (Data Load Tool). Python library to explain the fundamental ideas of contemporary data engineering. Data intake, schema evolution, incremental loading, API integration, authentication, pipeline state management, metadata tracking, and data quality are all covered in this course, which [...]

Using Azure Data Lake Storage Gen2, Data Lake Fundamentals introduces students to the fundamental ideas of contemporary data lakes. The course describes how businesses use a scalable cloud environment to store, manage, and analyze massive amounts of both organized and unstructured data. You’ll discover how Azure Data Lake Storage varies [...]

The fundamental ideas of Apache Airflow, the industry-standard workflow orchestration platform used in data engineering, are covered in the beginner-friendly course Airflow Fundamentals. Using Airflow’s essential elements, including DAGs (Directed Acyclic Graphs), tasks, operators, scheduling, dependencies, and the Airflow UI, the course explains how to create, schedule, monitor, and manage [...]

Overview of Apache The foundational ideas of Apache Kafka, one of the most popular event-streaming technologies, are covered in the beginner-friendly course Kafka Fundamentals. The course describes how event-driven architectures, scalable data pipelines, and real-time data streaming are made possible by Kafka. Along with helpful advice on incorporating Kafka into [...]

The foundations of Big Data and the Hadoop ecosystem are introduced to students in the beginner-friendly course Introduction to Apache Hadoop. The course describes how Hadoop uses distributed computing to store and handle large datasets for businesses. Along with Hadoop installation and practical Big Data applications, you will learn about [...]

Using industry-standard technologies like Shell (Bash) scripting, Apache Airflow, and Apache Kafka, this IBM course explains how to design, create, automate, and manage ETL (Extract, Transform, Load) processes and data pipelines. Students have practical experience building batch and streaming data pipelines that are utilized in contemporary data engineering settings. [...]

The fundamental technologies utilized in contemporary Big Data ecosystems are introduced practically in this course. It covers quick distributed computing using Apache Spark, large-scale data processing with MapReduce, and distributed storage with HDFS. Students get practical experience working with actual datasets and discover how these technologies are used in sectors [...]

You will learn how to create, construct, and oversee enterprise data warehouses for business intelligence applications with this specialization. Data modeling, SQL, ETL (Extract, Transform, Load) procedures, data integration, OLAP principles, dashboard design, and visual analytics will all be covered. The curriculum ends in a practical capstone project where you [...]

The Google Cloud Data Engineer Learning Path is a thorough, role-based training program created to assist students in developing the abilities required to plan, create, oversee, and optimize Google Cloud data processing systems. It offers real-world exposure with industry-leading data engineering tools and services like BigQuery, Dataflow, Dataproc, Data Fusion, [...]