Author: Frank Kane

Our courses are led by Frank Kane, a former Amazon and IMDb developer with extensive experience in machine learning and data science. With 26 issued patents and 9 years of experience at the forefront of recommendation systems, Frank brings real-world expertise to his teaching. His ability to explain complex concepts in accessible terms has helped over one million students worldwide gain valuable skills in machine learning, data engineering, and AI development.

Our new course is about… making great courses!

Sundog Education recently celebrated two huge milestones – over 200,000 students have enrolled in our courses worldwide, bringing in over one million dollars in revenue. Never in our wildest dreams did we envision that kind of impact when we started in 2015.

To give back to the community, we’ve created a 4-hour course encapsulating everything we learned along the way on how to create successful online courses. We know this course won’t sell as well as our technology courses, but we want others to have the same opportunity we did to change the world in a measurable way.

Go check out the promo video and curriculum at Udemy. If you have knowledge to share with the world, it’s an opportunity you shouldn’t pass by!

Apache Spark Tutorial for Beginners Part 1 – Installing Spark

Apache Spark is arguably the hottest technology in the field of big data right now. It allows you to process and extract meaning from massive data sets on a cluster, whether it is a Hadoop cluster you administer or a cloud-based deployment.

In this series of 8 videos, we will walk through installing Spark on a Hortonworks sandbox running right on your own PC, and we will talk about how Spark works and its architecture. We will then dive hands-on into the origins of Spark by working directly with RDDs Resilient Distributed Datasets and then move on to the modern Spark 2.0 way of programming with Datasets.

You will get hands-on practice writing a few simple Spark applications using the Python programming language, and then we will actually build a movie recommendation engine using real movie ratings data, and Sparks machine learning library MLLib. We will end with an exercise you can try yourself for practice, along with my solution to it.

In this video, we will focus on getting VirtualBox, a Hortonworks Data Platform (HDP) sandbox, and the MovieLens data set installed for use in the rest of the series. Your instructor is Frank Kane, who spent nine years at Amazon.com and IMDb.com as a senior engineer and senior manager, wrangling their massive data sets.

Explore the full course on Udemy

Apache Spark Tutorial for Beginners Part 2 – Introduction to Spark

Apache Spark is arguably the hottest technology in the field of big data right now. It allows you to process and extract meaning from massive data sets on a cluster, whether it is a Hadoop cluster you administer or a cloud-based deployment.

In this series of 8 videos, we will walk through installing Spark on a Hortonworks sandbox running right on your own PC, and we will talk about how Spark works and its architecture. We will then dive hands-on into the origins of Spark by working directly with RDDs Resilient Distributed Datasets and then move on to the modern Spark 2.0 way of programming with Datasets.

You will get hands-on practice writing a few simple Spark applications using the Python programming language, and then we will actually build a movie recommendation engine using real movie ratings data, and Sparks machine learning library MLLib. We will end with an exercise you can try yourself for practice, along with my solution to it.

In this video, we will focus on getting VirtualBox, a Hortonworks Data Platform (HDP) sandbox, and the MovieLens data set installed for use in the rest of the series. Your instructor is Frank Kane, who spent nine years at Amazon.com and IMDb.com as a senior engineer and senior manager, wrangling their massive data sets.

Explore the full course on Udemy

Apache Spark Tutorial for Beginners Part 3 – Resilient Distributed Dataset

Apache Spark is arguably the hottest technology in the field of big data right now. It allows you to process and extract meaning from massive data sets on a cluster, whether it is a Hadoop cluster you administer or a cloud-based deployment.

In this series of 8 videos, we will walk through installing Spark on a Hortonworks sandbox running right on your own PC, and we will talk about how Spark works and its architecture. We will then dive hands-on into the origins of Spark by working directly with RDDs Resilient Distributed Datasets and then move on to the modern Spark 2.0 way of programming with Datasets.

You will get hands-on practice writing a few simple Spark applications using the Python programming language, and then we will actually build a movie recommendation engine using real movie ratings data, and Sparks machine learning library MLLib. We will end with an exercise you can try yourself for practice, along with my solution to it.

In this video, we will focus on getting VirtualBox, a Hortonworks Data Platform (HDP) sandbox, and the MovieLens data set installed for use in the rest of the series. Your instructor is Frank Kane, who spent nine years at Amazon.com and IMDb.com as a senior engineer and senior manager, wrangling their massive data sets.

Explore the full course on Udemy

Apache Spark Tutorial for Beginners Part 4 – Using RDDs in Spark

Apache Spark is arguably the hottest technology in the field of big data right now. It allows you to process and extract meaning from massive data sets on a cluster, whether it is a Hadoop cluster you administer or a cloud-based deployment.

In this series of 8 videos, we will walk through installing Spark on a Hortonworks sandbox running right on your own PC, and we will talk about how Spark works and its architecture. We will then dive hands-on into the origins of Spark by working directly with RDDs Resilient Distributed Datasets and then move on to the modern Spark 2.0 way of programming with Datasets.

You will get hands-on practice writing a few simple Spark applications using the Python programming language, and then we will actually build a movie recommendation engine using real movie ratings data, and Sparks machine learning library MLLib. We will end with an exercise you can try yourself for practice, along with my solution to it.

In this video, we will focus on getting VirtualBox, a Hortonworks Data Platform (HDP) sandbox, and the MovieLens data set installed for use in the rest of the series. Your instructor is Frank Kane, who spent nine years at Amazon.com and IMDb.com as a senior engineer and senior manager, wrangling their massive data sets.

Explore the full course on Udemy

Apache Spark Tutorial for Beginners Part 5 – Spark SQL

Apache Spark is arguably the hottest technology in the field of big data right now. It allows you to process and extract meaning from massive data sets on a cluster, whether it is a Hadoop cluster you administer or a cloud-based deployment.

In this series of 8 videos, we will walk through installing Spark on a Hortonworks sandbox running right on your own PC, and we will talk about how Spark works and its architecture. We will then dive hands-on into the origins of Spark by working directly with RDDs Resilient Distributed Datasets and then move on to the modern Spark 2.0 way of programming with Datasets.

You will get hands-on practice writing a few simple Spark applications using the Python programming language, and then we will actually build a movie recommendation engine using real movie ratings data, and Sparks machine learning library MLLib. We will end with an exercise you can try yourself for practice, along with my solution to it.

In this video, we will focus on getting VirtualBox, a Hortonworks Data Platform (HDP) sandbox, and the MovieLens data set installed for use in the rest of the series. Your instructor is Frank Kane, who spent nine years at Amazon.com and IMDb.com as a senior engineer and senior manager, wrangling their massive data sets.

Explore the full course on Udemy

Apache Spark Tutorial for Beginners Part 6 – Using DataSets

Apache Spark is arguably the hottest technology in the field of big data right now. It allows you to process and extract meaning from massive data sets on a cluster, whether it is a Hadoop cluster you administer or a cloud-based deployment.

In this series of 8 videos, we will walk through installing Spark on a Hortonworks sandbox running right on your own PC, and we will talk about how Spark works and its architecture. We will then dive hands-on into the origins of Spark by working directly with RDDs Resilient Distributed Datasets and then move on to the modern Spark 2.0 way of programming with Datasets.

You will get hands-on practice writing a few simple Spark applications using the Python programming language, and then we will actually build a movie recommendation engine using real movie ratings data, and Sparks machine learning library MLLib. We will end with an exercise you can try yourself for practice, along with my solution to it.

In this video, we will focus on getting VirtualBox, a Hortonworks Data Platform (HDP) sandbox, and the MovieLens data set installed for use in the rest of the series. Your instructor is Frank Kane, who spent nine years at Amazon.com and IMDb.com as a senior engineer and senior manager, wrangling their massive data sets.

Explore the full course on Udemy

Apache Spark Tutorial for Beginners Part 7 – Using MLLib

Apache Spark is arguably the hottest technology in the field of big data right now. It allows you to process and extract meaning from massive data sets on a cluster, whether it is a Hadoop cluster you administer or a cloud-based deployment.

In this series of 8 videos, we will walk through installing Spark on a Hortonworks sandbox running right on your own PC, and we will talk about how Spark works and its architecture. We will then dive hands-on into the origins of Spark by working directly with RDDs Resilient Distributed Datasets and then move on to the modern Spark 2.0 way of programming with Datasets.

You will get hands-on practice writing a few simple Spark applications using the Python programming language, and then we will actually build a movie recommendation engine using real movie ratings data, and Sparks machine learning library MLLib. We will end with an exercise you can try yourself for practice, along with my solution to it.

In this video, we will focus on getting VirtualBox, a Hortonworks Data Platform (HDP) sandbox, and the MovieLens data set installed for use in the rest of the series. Your instructor is Frank Kane, who spent nine years at Amazon.com and IMDb.com as a senior engineer and senior manager, wrangling their massive data sets.

Explore the full course on Udemy

Apache Spark Tutorial for Beginners Part 8 – Project Solution

Apache Spark is arguably the hottest technology in the field of big data right now. It allows you to process and extract meaning from massive data sets on a cluster, whether it is a Hadoop cluster you administer or a cloud-based deployment.

In this series of 8 videos, we will walk through installing Spark on a Hortonworks sandbox running right on your own PC, and we will talk about how Spark works and its architecture. We will then dive hands-on into the origins of Spark by working directly with RDDs Resilient Distributed Datasets and then move on to the modern Spark 2.0 way of programming with Datasets.

You will get hands-on practice writing a few simple Spark applications using the Python programming language, and then we will actually build a movie recommendation engine using real movie ratings data, and Sparks machine learning library MLLib. We will end with an exercise you can try yourself for practice, along with my solution to it.

In this video, we will focus on getting VirtualBox, a Hortonworks Data Platform (HDP) sandbox, and the MovieLens data set installed for use in the rest of the series. Your instructor is Frank Kane, who spent nine years at Amazon.com and IMDb.com as a senior engineer and senior manager, wrangling their massive data sets.

Explore the full course on Udemy

How to Choose the Right Database? – MongoDB, Cassandra, MySQL, HBase

Choosing the right database for your application is no easy task.

You have a wide variety of options relational databases such as MySQL, or distributed NoSQL solutions such as MongoDB, Cassandra, and HBase. NoSQL has come to mean not only SQL as many distributed database systems do in fact support SQL-style queries, as long as you are not doing complex join operations and this further blurs the lines between these systems.

We will talk about how to analyze the requirements of your system in terms of consistency, availability, and partition-tolerance, and how to apply the CAP theorem to guide your choice after showing you where different database technologies fall on the sides of the CAP triangle. We will also talk about more practical considerations, such as your budget, need for professional support, and the ease of integration into the other systems already in place in your organization. Maybe you don’t even need a distributed storage solution at all! Choosing the right technology for your data storage will save you a lot of pain as your application grows and evolves and making the wrong choice can lead to all sorts of maintenance problems and wasted work.

Your instructor is Frank Kane of Sundog Education, bringing nine years of experience as a senior engineer and senior manager at Amazon.com and IMDb.com, where his job involved extracting meaning from their massive data sets, and processing that data in a highly distributed manner.

Explore the full course on Udemy