Big Data: Demystifying the Big Idea
By the end of this course, you'll be able to define what makes data "big," distinguish Big Data from a traditional data warehouse, and map the core layers of a Big Data architecture from ingestion to serving. You'll compare processing approaches like batch, streaming, and Lambda, weigh core technologies such as Hadoop, Spark, and Kafka, and evaluate on-premise, cloud, and hybrid hosting against the principles that matter most: performance, availability, scalability, flexibility, and cost. Led by a data and AI cloud architect with over a decade of hands-on experience building solutions for com
Skills you'll learn
We may earn a commission if you enroll through our links — it never affects the price you pay.
Similar courses
PySpark Foundations: Process, analyze, and summarize data
Did you know that a billion records are processed daily in PySpark by companies worldwide? As big data is on the rise, you’ll need tools like PySpark to process massive amounts of data. This guided project was designed to introduce data analysts and data science beginners to data analysis in PySpark. By the end of this 2-hour-long guided project, you’ll create a Jupyter Notebook that processes, analyzes, and summarizes data using PySpark. Specifically, you will set up a PySpark environment, explore and clean large data, aggregate and summarize data, and visualize data using real-life examples
All levels
Apache Kafka - An Introduction
Apache Kafka is a powerful, open-source stream processing platform that enables businesses to process and analyze data in real-time. This course introduces the core concepts and architecture of Apache Kafka, guiding learners. This course is designed for aspiring data engineers, software developers interested in data processing, and IT professionals looking to diversify into data engineering. It also targets data scientists seeking to understand real-time analytics platforms and technical managers overseeing data-driven projects. Learners are expected to have a basic understanding of programm
All levels
Graduate Admission Prediction with Pyspark ML
In this 1 hour long project-based course, you will learn to build a linear regression model using Pyspark ML to predict students' admission at the university. We will use the graduate admission 2 data set from Kaggle. Our goal is to use a Simple Linear Regression Machine Learning Algorithm from the Pyspark Machine learning library to predict the chances of getting admission. We will be carrying out the entire project on the Google Colab environment with the installation of Pyspark. You will need a free Gmail account to complete this project. Please be aware of the fact that the dataset and th
All levels
Leading with Questions
Based on the best-selling book by Michael J. Marquardt and Bob Tiede. In today's fast-paced and ever-evolving business environment, the ability to ask the right questions is a powerful leadership skill. "Leading with Questions" focuses on equipping leaders with the tools to foster innovation, build trust, and improve team collaboration through strategic inquiry. By mastering the art of asking insightful questions, leaders can uncover new perspectives, spark creativity, and drive organizational success. This course will help learners enhance their leadership capabilities by teaching them how t
All levels