
See how Presto queries data in place from HDFS, MySQL, and Kafka, delivering fast analytics with compute–storage separation and in‑memory processing.
Define three end users—data scientist, data engineer, and BI analyst—and design a Presto-based solution that enables Tableau, SQL, and R Studio access to data from Hdfs, MySQL, and Kafka.
Explore a four-bucket tech stack: data source, storage, compute, and consume, where GitHub data sits in MySQL, Kafka, or Hdfs, processed by Presto in an interactive compute layer.
Download Tableau desktop, install it, set up the Presto JDBC driver, and connect Tableau to Presto by configuring localhost, restarting Tableau, and running queries.
Explore the Presto web UI to monitor and manage queries, view cluster metrics, and trace activity from RStudio, Tableau, and Presto CLI.
Install the MySQL server, create a database and table, load a local file into the table, and query the data from both the MySQL and Presto command lines.
Welcome to my Presto course - Hands On Presto Mastery - Learn by doing!. I want to first of all thank you for considering this course. Without you the student, I will not be able to create this course.
Big Data is very hot at the moment and Presto is one of the exciting projects in the Big Data ecosystem. Presto is a distributed query engine that excels at crunching petabytes of data efficiently and low latency analytics.
This Presto course contains everything you need to get started with Presto. We will go from zero to Presto in this single course and back it up with a lot of hands on work. By the end of this course, you will be full of confidence, skilled up and ready to take your career to the next level. So let's buckle up and lean in
In this course, we will wear many hats as we solve an end to end Big Data problem:
Product Hat: We will start with a real world scenario and dive into the user segments and pain points.
Architect Hat: Once we understand the pain points, we will architect a Presto based solution to address those pain points
Engineer Hat: Once we have that, we will then install and setup Presto. We will cover the fundamentals – coordinator, worker, connectors etc.
Engineer Hat: One nice thing is that presto forces you to interact with other projects in the Big Data ecosystem.
And so, to visualize data, we will setup clients like the Presto CLI, RStudio and Tableau.
For storage, we will setup data sources like the MySQL DB, Kafka and HDFS. Presto is strictly a compute engine and this means that it does not have its own storage.
Engineer Hat: Once we are done, we will demo the final product and show you how to join data from multiple data sources in a single query.
At the end of this journey, we will deliver a solution that solves the pain points identified earlier.