Learn Big Data in 8 weeks, make your career switch, and earn $20-$60k more – even if you don’t know Hadoop.

Congratulations on reading and finishing The Ultimate Guide to Switching Careers to Big Data. (If you haven’t, I suggest you go back and finish it.) You’ve done more than most people to learn how to make a career switch to Big Data.

You’re faced with a difficult choice. How do you proceed with switching careers? You have all sorts of routes to learn Big Data. Most of them are dead ends. Few are actually proven to work. Some are cheap and some are expensive, but only a distinct few are worth every penny.

This choice will dictate whether you’re successful or not at your career switch.

It’s amazing to me that I got a job at [a leading Hadoop company]. I owe that 100% to Jesse.

Greg W.

Solutions Engineer

I was then invited for an interview at [a leading Hadoop company] and […] I got the job and I’ve been here for three months.

Imran Y.

Data Scientist

I was able to interview with [a premier data consultancy in Mountain View], and I got a job. It’s much more interesting than what I was doing before.

Jeff L.

Big Data Engineer

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Wouldn’t it be great if someone took you by the hand and led you through learning Big Data?

There are so many Big Data technologies out there. Which technologies does someone need to learn to switch careers? Which ones are actually used at companies? How do people use these technologies in the real world to create a solution?

You need someone like me to guide you. I’ll guide you throught what companies expect you to know when you make a career switch. I’ll show you the tips and tricks that professionals should know. Most importantly, I’ll teach you the exact technologies that are the most commonly used in Big Data.

I know what companies are looking for because I travel the world teaching and mentoring data engineering teams. I’ve asked the managers what they look for when they’re hiring. I ask the teams which technologies they’re using in production or looking at in the future. I pass all of these learnings on to you in my course.

Many of my students come to me after struggling to learn Big Data. People think that learning Big Data is just like learning any other technology and they’re DEAD WRONG.

Many of my students share the same struggle to learn Big Data. Imran took my course after struggling to learn Big Data on his own. “I’d go to interviews and be shaky, and I felt like I was talking nonsense with my answers. I knew some of it, but it was like a half-basic knowledge of what they wanted.”

“I knew that to transition from academia to industry I had to know the tools people use in industry. There are so many tools that people talk about, like Hadoop and Spark. I couldn’t find any good online courses, so I just tried to learn from books. Book knowledge is good, but the practical application is missing.”

Another one of my students, Greg, tried learning Big Data on his own, but was struggling. “There’s a whole zoo of technologies out there: HBase, Cassandra, Spark, Flume, Kafka… it’s a massive field. It’s difficult to start learning when you don’t know how any of these technologies fit together.”

My student Shikha struggled to find where she should even start learning Big Data. “I wanted to change my career path to big data. I’m a Senior Java Developer — I have those skills — but I had no idea about the big data ecosystem.”

Students come to my class after struggling with Big Data for months or a year. They’ve spent countless hours on their own struggling to learn Big Data. They’re frustrated and they don’t know what to focus on. They still don’t have the skills to complete a Big Data project or get a data engineering job.

The unfortunate truth is that learning Big Data is really difficult. Many developers can’t admit to themselves that they’ve been doing it dead wrong. You can’t just spent a few hours watching YouTube videos. You can’t learn it by cracking open that 800 page tome on Hadoop. You can’t read over some HelloWorld code.

You have to learn Hadoop a different way.

Shikha was one of my students who had struggled to learn Big Data on her own and really appreciated my unique and innovative approach to learning Big Data. “Jesse had this whole curriculum, with a logical flow from week to week. He would teach us the material and technology, and we’d finish with a hands-on ‘job task’ relevant to that technology.”

“[Jesse] explained Mapping in an interactive, layman’s way. He did it using a deck of cards. First, he distributed the cards. Then he’d say things like, ‘okay, now remove all the jokers and face cards’, and that was cleaning the data. And then he said to put all the clubs together, and the hearts, etc., so you were mapping all the cards and then giving each suit to one reducer to count. The way he taught, I don’t need to go back to read the book — it’s just in my mind. Even a kid could understand it!”

In an hour, I was able to teach Shikha more than she’d learned in months of self-study. This is the kind of radically different approach to learning Big Data that people needed. She needed my five principles of learning data engineering that are so simple and straightforward that even a kid could understand it.

That’s my approach to teaching Big Data. We learn the concepts and the concepts are made crystal clear by using a tactile approach. Then we practice them with code.

This is why I’ve become the trusted source for Fortune 100 companies. They depend on me to teach their Software Developers the skills to become Data Engineers. I’m the expert that other industry experts recognize as the best Big Data trainer.

Jesse has explained with Legos how […] Spark works. He’s done some great YouTube videos to introduce the concepts. I suggest you all go look at them.

Jesse has explained with Legos how […] Spark works. He’s done some great YouTube videos to introduce the concepts. I suggest you all go look at them.

Doug Cutting

Creator of Apache Hadoop

One of the finest trainers in Big Data.

Ben Lorica

Chief Data Scientist at O’Reilly Media

There are so many different Big Data technologies out there. Which ones should you learn so you don’t want waste your time?

Robert came to my class wondering which Big Data technologies he should learn. He knew learning Big Data was a major time investment. He reasoned “If you know what a consultant makes in the Bay Area it was an expensive decision to take time off to study.” But just before I closed enrollment, he did join, “I justified it by looking into the future. I knew the course would give me the necessary background to evolve into the space way more rapidly. The faster I learned it, the faster I could get back to my clients and add even more value to them. The ROI was worth it.”

“[Jesse’s Course] provided an understanding of this technology from the very beginning, to what’s used the most today, and what’s the newest technology and how it all fits together. One example is he’d tell us about the technology common to 80% of companies now, and then about the companies that are always on the leading edge and what they are using, and then the other pieces you need to learn and explore. More than anything he builds a larger picture of the ecosystem.”

But it’s not just the big picture you need to know. I want to give you a real-world understanding of how each technology works, so the course is exercise and materials heavy. “There were a lot of hands on examples. Here’s how you set it up, run it, what you should experience, and what’s required of the end user to get it going. And the materials were first rate. In all the interviews I had after I basically used Jesse’s slides to talk about what I knew. I’ve probably gone through them four or five times and that solidified my understanding of these technologies.”

After finishing my course, Robert went back to consulting. It’s led to many new opportunities, “I was working with a client, and eventually met with someone from [a leading Hadoop company]. This connection invited me in to interview for a project management role, but when they heard about my tech experience and ability to explain the technology clearly we decided Data Scientist and Evangelist was a better fit. I’m producing tech for the company, and engaging the community around it. I still might do a startup five or ten years down the road, but for now I love what I’m doing.”

And the salary bump is a great perk. “In my previous career path there was a cap on how much I could progress, and the salary could reach maybe $100,000 to $130,000 — in data it’s 60% higher and I’m no longer stuck on that other track.”

After learning the right technologies, Robert can take a long-term view of the Big Data industry. I asked Robert to share his advice for people that may be on the fence about joining, here’s what he said:

“Look at where you see the industry going five to ten years down the road. It’s a generic question but it allows you to see the rapid development of technology. The only tech that will scale and handle this data is Hadoop and the related tools. In order for you to enter this space you need the understanding to know what it’s all about. Jesse’s course does that. It’s a basic, comprehensive course that opens doors to anything data.”

Recall how Robert hesitated about the time/financial commitment; to finish up I asked him about that again. “Looking back from where I am today this has been a really good decision, the long term ROI is obvious.”

What will your life look like as a Data Engineer?

You go to work excited about the challenges every day. You’re a data engineer now. Your data products land on the CEO’s desk every day. The other execs and managers see your data dashboards. Your analytics and data engineering are making an appreciable difference at the company. Your data is the lifeblood of the company.

You go to conferences where the job boards are filled to the brim and companies have booths just to hire data engineers. Companies are literally tripping over themselves to hire qualified data engineers. They’re paying more for qualified data engineers and that puts $20,000 to $60,000 extra in your pocket per year.

You don’t have to fight with the DBA over a full table scan for a query. Your queries operate over terabytes of data and millions of rows. You’re limited by your creativity instead of technology.

That’s your future…if you become a data engineer.

Let’s not kid ourselves. Becoming a data engineer isn’t easy. Otherwise, everyone would be doing it.

It doesn’t just take time and effort; it takes programming skills. You also need to know or learn Java. Beyond that it takes great instruction and materials to make you successful.

That’s why my students choose my materials over others. They’re practical, technically correct, and they transform students into data engineers.

How did I start my own transformation?

My own Big Data journey started a similar way. I read the books and realized they didn’t teach concepts; they served as good reference materials. The technology was changing rapidly. The technologies were changing rapidly and Stack Overflow answers were too old out-of-date to use. I looked at the online training materials and found them to inaccurate, useless synopsis of a technology. The in-person classes are good, but are expensive, require you to travel to that location, and pay for your accommodations (about $5,000 total cost). You never knew who was teaching the class, if they knew their materials, or how competent a programmer they are. Most classes are two to four days long and have to spend precious hours on introductory materials that I already knew. There simply wasn’t enough time to get to the advanced materials I wanted to learn.

They were all missing the point of data engineering. Creating a data pipeline isn’t about learning a single technology. It’s learning those technologies, how they fit together, and how to create a data pipeline out of them. In Big Data, learning a single technology will get you nowhere.

I decided that my training would be different. It would simplify the difficulties of learning Big Data concepts I had while learning these technologies. It would reflect my observations of teaching thousands of students. It would use the most innovative techniques I’ve in my Big Data classes for years. Finally, it would focus on technologies and skills that people need to become a Data Engineer.

The result is that my materials take Software Developers and transform them into Data Engineers.

What were my results?

Without hyperbole: I completely changed my life.

I got my Dream Job at my Dream Company (Cloudera). I was covered in such prestigious places as: The Wall Street Journal, BBC, Wired, and Ars Technica.

My life has dramatically changed since then. I’m published on O’Reilly and Pragmatic Programmers. My projects have continued being covered in Tech Crunch and other places. I’ve spoken at the top industry conferences such as Strata and HBaseCon.

More importantly, I get to see my data engineering students get new jobs or promotions paying $20,000-$60,000 more. I beam with pride every time I see that one of my former students get a promotion or get prominent position at a well-known company.

How do you make it happen?

So you’re looking around at which technologies to learn. There is a massive field to choose from. Which ones are companies actually using? Which ones do you need to know to get a job? They all have weird names like Pig and Hive. What do they do?

I was in NYC and [my client] was amazed on how I was giving an overview of the entire stack, and analytics on top of that. I could clearly and easily talk about these different technologies, and they were impressed — I know that comes back to Jesse.

Robert H.

Data Scientist & Evangelist

There’s a whole zoo of technologies out there: HBase, Cassandra, Spark, Flume, Kafka… it’s a massive field. It’s difficult to start learning when you don’t know how any of these technologies fit together.

Greg W.

Solutions Engineer

I’m a Senior Java Developer — I have those skills — but I had no idea about the big data ecosystem.

Shikha S.

Data Engineering Student

Then you’re faced with the arduous task of finding sources of information about Big Data. Many online resources are out-of-date or just plain wrong. Books are meant for reference and don’t really teach. Training courses just regurgitate the poor documentation. Few resources look like they were written by someone who knows how to program or is an expert on the technology.

You get the sneaking suspicion that you are constantly wasting your time and not getting anywhere.

The Five Principles of Becoming a Data Engineer

I’ve taught thousands of students. Out of this vast experience, I’ve created a system that helps Software Developers become Data Engineers by following five principles:

  1. You need to learn many different technologies to become a Data Engineer.
  2. Passive learning isn’t possible with Big Data. You must practice with each technology.
  3. Big Data concepts can be explained faster and with higher understanding by using and moving around physical objects.
  4. You need to create a solid foundation in the fundamentals before continuing on.
  5. Knowing how to create a data pipeline is the distinguishing factor that gets a Data Engineer a job.

All five of these principles permeate my training materials

Let’s talk a little more about about what each one of these principles and their impact on you.

Knowing Different Technologies

A common question I’m asked is “should I learned MapReduce or Spark?” That’s a good starting point. However, it makes the assumption that learning Big Data is simply learning a new API. That couldn’t be further from the truth.

This is the source of many peoples’ failed Big Data journeys. They learn technology X, update their LinkedIn profile, get an interview, and fail miserably. During the interviews they are asked how to create a data pipeline and they simply don’t know.

Practice

Many online course say or intimate that passive learning is possible to learn Big Data technologies. That simply isn’t the case. There is a very distinct difference between students who practice and those who don’t. I can see it in five minutes of talking to a student. Your interviewers will see it too.

My most unsuccessful students are those that don’t listen to me about exercises and practice. All of my teaching leads to the exercises where you practice and make the synaptic connections. You fail in the safety of a class instead of crashing and burning during an interview at the whiteboard.

Using Physical Objects

Understanding Big Data and distributed systems concepts are incredibly difficult. You could look over book diagrams for an hour or I could show the same thing in ten minutes with Legos or playings cards. I could tell you how, but why don’t you just see me in action?

You just learned more in twelve minutes that you could in one hour reading a book. You also had a vastly higher comprehension and memorization of the concepts.

You won’t find these innovative teaching methods anywhere else. Other courses give unintelligible and incomprehensible explanations that take forever. An incomprehensible explanation will set you up for failure once you start coding.

Solid Foundations

Have you ever tried to learn an advanced topic and failed while trying to implement it? This was likely due to a lack of foundation on the topic. Before continuing on to an advanced subject, you must understand and have practiced the simple subjects behind it.

Without learning from someone who focuses on building on a solid foundation, you will constantly have trouble with the advanced topics (i.e. what gets you a job). I’ve seen so many classes that just throw out advanced topics, without actually giving the background knowledge on that subject.

Skills + Data Pipelines = Job/Success

What do employers want from Data Engineers? They’ll of course want skills in the relevant technologies, like Hadoop and Spark. However, learning Hadoop and Spark aren’t enough. You’re going to need to know how to create a data pipeline. This is how companies make money on data and what they expect you to do. They will expect you to create a data pipeline.

Other courses and materials don’t do this. This is why those people won’t be switching careers. They’ll give nonsense answers that show a lack of mastery in creating a data pipeline. They won’t get the job.

I looked around online. Jesse has videos about the material and I liked the way he was teaching. I knew I would learn a lot, so I joined.

Shikha S.

Data Engineering Student

I couldn’t find any good online courses, so I just tried to learn from books. Book knowledge is good, but the practical application is missing. Plus my progress was slow because I wasn’t getting any time to study at my old job.

Imran Y.

Data Scientist

It was frustrating. We were failing and the Vice President was watching us do it. We had all the books and tried to work through the steps […]

Steve R.

Senior Hadoop Engineer

I was doing these other things in my career, including working as an adjunct professor, as a way to stay current on programming methodologies. It was kind of perpetually sharpening the saw of what I could do with my engineering background. Jesse helped me appreciate another way of learning, i.e., a technology solution focussed course and how much value that could provide. Having a well laid out program and instructions shifted my mentality from needing to do these academic courses to how you can get that knowledge much more quickly if you have the right pedagogical approach, a knowledgeable instructor, and coursework that has you participate in coming up with solutions.

Nate S.

Senior Hadoop Engineer

Get instant access to the materials to make your career switch and earn $20-$60k more.

These are the same materials I use to teach Data Engineering teams at Fortune 100 companies. I have the reputation amongst my peers as a world class expert on Big Data and a phenomenal trainer.

The result of my training is that people like you get the skills to become Data Engineers. They make the switch to a Big Data career. They have the skills to be successful in their Big Data projects.

[Jesse’s Course] provided an understanding of this technology from the very beginning, to what’s used the most today, and what’s the newest technology and how it all fits together.

Robert H.

Data Scientist & Evangelist

The way he taught, I don’t need to go back to read the book — it’s just in my mind. Even a kid could understand it!

Shikha S.

Data Engineering Student

Jesse explains in concrete ways that make the lessons easy to understand. There is the famous playing card example, and we’d play with lego blocks to learn difficult concepts. It was nice.

Jeff L.

Big Data Engineer

Introducing Professional Data Engineering

  • Learn difficult technologies: Understand technologies like MapReduce and Spark … even if you’re just starting out
  • Target the right technologies: Learn the technologies the technologies the industry is and isn’t using
  • Stop wasting your time: See the techniques I used to teach Big Data at over twenty Fortune 100 companies
  • Earn more money: Qualified Data Engineers earn $20,000-$60,000 more

Watch the trailer

Watch the extended preview showing what the class looks like, how code is shown in slides, and how exercises went through.

For the first time, I’m making my training available to individuals. I’ve had many individuals contact me about learning my data engineering methods. Normally, my trainings are held at large companies for groups of 10-20 people. These trainings cost between $20,000 – $30,000 for two to four days. These are just cost and time prohibitive for individuals or small teams.

This means that individuals can get the same training I provide at these Fortune 100 companies.

Online training allows you to go deeper and more comprehensive than ever before.

How do you know if this course really works? This course already runs at companies. It has taken teams of developers and made them teams of Data Engineers. This course already runs at training facilities. It has already taken students who were Software Developers and made them Data Engineers who started earning $20,000-$60,000 more.

Big Data is changing constantly, how do I know this course is up-to-date? This course already runs at companies and those companies expect that their students are learning from up-to-date materials. The materials and code are updated to the latest versions of CDH. My courses cover current and future technologies. Many of my students are hired because they’ve learned a future technology that the company wants to start using.

Which technologies should you learn? I’ve curated and tested this course to teach the technologies and concepts that companies and Data Engineers need. Even better are the technologies and concepts it doesn’t cover. This course removes the unnecessary concepts for developers and technologies that don’t make sense or aren’t used. Given my industry expertise, we even cover up and coming technologies that will set apart on your job search.

Which order should you learn the technologies in? A haphazard learning progression can make learning Big Data even more difficult. I’ve tested the order and progression of concepts in classes. This make each module, chapter and section have a logical progression. The knowledge and experience builds until you’re a Data Engineer.

How will you be productive and start coding? Installing Big Data tools is an ordeal unto itself (trust me). You don’t want to waste hours getting things installed and configured before you can even start being productive. I’ve created a virtual machine that gets you up and running quickly. Everything is already installed and configured for you. It has Hadoop, Spark, many ecosystem projects, and Eclipse installed. You just install VirtualBox, import the VM, and you’re ready to go. No wasting time.

How will you practice the skills that you need to master? The course makes heavy use of exercises to practice the skills that you have just learned. There is a full exercise guide that gives you instructions on what to do. These exercises gradually increase in difficulty as you start to master new skills. Each programming exercise has a full sample solution that you can peek at if you get stuck or want to compare your solution with mine. At the end of most modules there is a final. This final helps you check if you have really mastered the skills you need.

Does this course just cover Big Data technologies? This course covers all of the technologies you need to master to become a Data Engineer. Some of these are Big Data technologies and others are common technologies. These common technologies are what the professional Data Engineers use to be more productive and produce enterprise-ready software. Some of these technologies that we cover are Maven, Regular Expressions and Apache Avro. Knowing these technologies will set you apart on your job search.

Do you have to go in order? Advanced programmers can skip over rudimentary materials. Intermediate programmers can start at the beginning and progress to the advanced materials. Those who need more help can start with the rudimentary bonus materials. This is something I can’t do in a class.

Is there a bounding time frame? The class is designed to be done in eight weeks. However, you aren’t bound by the time I’m teaching in a classroom. You’re completing materials when it’s convenient for you and can take more than eight weeks to complete it.

How does this compare to training from company X? There are various sources out there for Big Data training. There is a vast difference in quality, veracity, and teaching out there. The majority of them are on the lower end of quality. Purchasing a low quality course isn’t just a waste of money; it’s a waste of your time and you won’t get the job. Quality training is the difference between a becoming a Data Engineer and not getting a job.

How does this course make you a Data Engineer? The five principles of becoming a Data Engineer permeate this course. This comes from having taught thousands of students. You go through the modules, practice what you’re learning, and master the skills you need to become a Data Engineer. It’s all laid out for you; you just need to follow it.

Can I get my company to reimburse me? Yes, other students who have purchased this course have had their purchase reimbursed by their company. Many companies have continuing education budgets or new projects have money allotted for training. This is especially true for new and difficult initiatives like Big Data. I will help you however I can to get your purchase reimbursed by your company. Send this PDF to your boss or Human Resources department to convince them to reimburse you.

There were a lot of hands on examples. Here’s how you set it up, run it, what you should experience, and what’s required of the end user to get it going. And the materials were first rate. In all the interviews I had after I basically used Jesse’s slides to talk about what I knew. I’ve probably gone through them four or five times and that solidified my understanding of these technologies.

Robert H.

Data Scientist & Evangelist

It’s great when you have a teacher like Jesse because you have a lot of problems, but you don’t quite see how they all fit together. With his breadth of knowledge, Jesse is able to navigate those in the most effective order and make sure you are only consuming new information at the point you are ready.

Nate S.

Senior Hadoop Engineer

Get instant access to the materials to make your career switch and earn $20-$60k more.

Professional Data Engineering teaches you everything you need to know to switch careers. It has in-depth coverage of technologies such as:

  • Apache Hadoop
  • Apache Spark
  • Apache Hive
  • Apache Kafka
  • Apache Crunch
  • Hue
  • Apache Oozie

 

You’ll get:

  • Three different levels for different budgets: Base, Master, Platinum
  • 17+ hours of base videos covering Hadoop and its ecosystem
  • 20+ hours of exercises over 24 different exercises to help you practice and solidify your understanding of Big Data
  • A Virtual Machine (VM) that’s loaded and configured with the latest version of Hadoop, Spark, other ecosystem projects, and your IDE so that you’re programming instead of trying to get things running.
  • Access to ask me unlimited questions about the course at the Platinum level

You’ll need:

  • A 64-bit computer with at least 1 GB of free memory or access to a cluster
  • VirtualBox 5.0.x installed
  • Intermediate-level Java skills

This course features eight modules.

Each module is designed to take a week to accomplish. Each module gradually builds your data engineering knowledge until you’re a qualified Data Engineer.

Module 1

Thinking in Big Data

  • What exists in the Big Data ecosystem so you can use the right tool for the right job.
  • An understanding of how HDFS works and how to interact with it.
  • An understanding of how MapReduce works and how each phase works.

Step 0 Learning

  • Learn from the success and failures of previous students’ habits.
  • See how I’ve mastered so many different difficult technologies.

Pre-Big Data

  • How to understand Big Data concepts without even using a framework.
  • Understand the basic patterns of Big Data before you learn the frameworks.

Effective Personal Projects

  • Learn how to create an effective personal project that shows master despite not having experience.
  • Learn why some personal projects get people a job and why others don’t.
  • Get the framework that creates effective personal projects.

Module 2

Coding With MapReduce

  • The basics of coding a MapReduce job with Java to build your Big Data foundation.
  • How to use an IDE like Eclipse when writing Big Data code to speed up development and improve your efficiency.
  • Which tools professional data engineers use like Regular Expressions and Maven to make their lives easier.
  • How to use languages other than Java with MapReduce.

Advanced MapReduce

  • What the advanced features of the MapReduce API that only the true experts know.
  • How unit testing MapReduce code with MRUnit makes you vastly more efficient and improves your code quality.
  • What is Avro, how it works with MapReduce, and how top data engineers use it to make maintainable and evolving data schemas.

Using Parquet

  • How columnar formats like Parquet differ from row oriented formats like Avro.
  • How to use save out and read in files with Parquet format.

Module 3

Crunch

  • How Apache Crunch gives you a very different API from MapReduce and gives you a more Java-centric API.
  • How to create pipelines with Crunch to process data easily.

Advanced Crunch

  • How to use Apache Crunch to do the things not humanly possible in MapReduce like joining datasets and performing secondary sorts.
  • How to keep your Crunch code modular and unit testable.

Module 4

Using Hive

  • The simple and advanced SQL-like commands available in Hive.
  • How to create tables and run Hive queries.

Augmenting Hive

  • How to extend Hive commands with custom non-Java code to do company or use case specific queries.
  • How to create Java code that runs as a function during a Hive command to use existing Java code or do use case specific queries.

Module 5

Coding With Spark

  • An understanding of how Spark works and how each phase works.
  • What are Java 8 Lambdas and how they make your Spark code humanly readable.
  • The basics of coding a Spark job with Java to build your Big Data foundation.
  • The various API methods in Spark and what they do.
  • How to use an IDE like Eclipse when writing Big Data code to speed up development and improve your efficiency.

Spark SQL

  • How SQL can be used with a Spark job and when that vastly improves your productivity and code.
  • How to create Java code that runs as a function during a Spark SQL command to use existing Java code or do use case specific queries.

Module 6

Moving and Accessing Data

  • How to move data out of and into relational databases like MySQL and Oracle from Hadoop/Spark using Apache Sqoop.
  • How to move files and network data from many different computers to Hadoop using Apache Flume.

Creating Workflows

  • What is Hue and how it aids in creating browser-based data products.
  • How Apache Oozie makes it possible to create repeatable workflows that enterprises need.

HBase – Just the Basics

  • Learn the concepts behind HBase and when to use it.

Kafka

  • Learn how to make a real-time data pipeline at scale.
  • Understand the publisher and consumer API.

D3.js

  • Understand why visualization is so important to creating a full data pipeline.
  • Learn how to use D3.js and Dimple to create browser-based charts.
  • See how to analyze data with a Big Data technology and visualize it.

Module 7

Hadoop Architectures

  • How all of these technologies come together as a solution for ETL, click stream, and sessionization use cases.
  • The steps and iterations to take when creating a Big Data solution.

Doing Data Science on the NFL Play by Play Dataset

  • Learn how I took raw data and created a usable dataset from it.
  • Use American Football as metaphor for business to see to make data augmented decisions.

Million Monkeys

  • Learn the strategies I used to optimize my Big Data algorithms.
  • See how a Monte Carlo simulation landed me in the BBC and The Wall Street Journal to name a few.

Module 8

Engineering Big Data Solutions

  • Learn how to run successful Big Data projects.
  • See the step by step and iterative methods I teach Fortune 100 companies to use in their Big Data projects.

Interviewing

  • Learn and practice the most common interview questions for Data Engineering positions.
  • Get the tips and tricks to make your career switch even if you don’t have previous experience.

Live Coding

  • Watch me code live to see how I maximize productivity and code quality.
  • See the tips and tricks I use to write my Big Data projects.

Get Your Course Questions Answered Via Email

  • Get email access to a qualified Big Data instructor to ask questions about the class concepts or class code.
  • Provides the extra safety net if you get stuck or need help.

He’s completely up to date on what’s going on in the industry, and he reframes student questions to be the right question that everyone would understand and then gives the answer. He’s really on top of his game.

Imran Y.

Data Scientist

We came in each day with a set of challenges or questions. Not only could Jesse answer the question as though he was anticipating it, he was able to lead us directly to what our next question would be and why.

Steve R.

Senior Hadoop Engineer

Limited Offer

For the first 21 students, I will do a one hour remote session on whatever you’d like to talk about from the course. This could be anything from practicing for an interview, to a question about code, to a clarification of an advanced topic. You choose. Once you’ve bought the course, we’ll schedule a time.

100% Money Back Guarantee

I stand behind this course 100%. I want you to love this course 100% percent too. If you don’t love this course, I’ll give you 100% of your money back. That’s right 100% money back guarantee, no matter how deep you are in the course.

Go through the materials. See that they’re the best. Go through the exercises and see yourself becoming the Data Engineer you want to become. I’m confident you’ll be successful.

I’ve built my teaching methods over years of teaching Data Engineering classes. These methods are honed over class after class. No one else is offering classes like these that are so comprehensive. No one else is teaching with such innovative methods. No one else is teaching practical skills.

This course isn’t for everyone as we established before. This course is for Software Engineers. Even within that group, not everyone has the programming skills to be a Data Engineer and I understand that. I’ll give you your money back.

Here is my simple offer: if you don’t love this course within 60 days, I insist that you get 100% of your money back. Guaranteed. Join at the level that’s right for you and see how you become the Data Engineer you’ve always dreamed of becoming.

In 1998 I had training on UML from Martin Fowler – I can honestly say I feel I received equal value in this regard!

Jesse is definitely proficient in his material. He is not just a “walking encyclopedia”, but also an effective practitioner of his art. His material was accompanied by effective examples of the cost of certain decisions. He follows an effective pedagogical order to the concepts taught and avoids diving into topics before he is able to give sufficient context to allow students to steer through the material effectively. I would love to have him come teach at our business again!

Steve R.

Senior Hadoop Engineer

After the program I felt like I had a much better understanding, so I was more confident and always relaxed. I got the job and I’ve been here for three months.

Imran Y.

Data Scientist

Jesse is an engineer’s engineer. He’s the kind of guy you’d want to back you up if you were struggling with something. He has a rich set of ideas and concepts to put together, and you get out of that exactly what you need to focus on. I felt like it really boosted my execution ability using HBase and other technologies. If you are still on the fence, think about the limited time you have. You will learn more from a focused course coming from Jesse then you will in self study or at meetups. He is very easy to learn from and adapts very well to different learning levels — I’d highly recommend you look into his background and what he can do to help you.

Nate S.

Senior Hadoop Engineer

Get instant access to the materials to make your career switch and earn $20-$60k more.

Part of making my materials as accessible as possible is to make it easy for people to pay for it. I have two methods of paying for a course, one is an installment plan and the other is an outright purchase. The installment plan breaks up the course’s payments into monthly payments over 12 months. With the outright purchase, you pay for the entire course at once.

Get instant access to the materials to make your career switch and earn $20-$60k more.

21638561920_f6aa89acc4_o (1)Here I am speaking at the preeminent Big Data conference, Strata and Hadoop World. There are over 1,000 submissions and only ~100 speaking spots. Only the best speakers and experts are chosen to speak. I speak at Strata year after year.

As you look at the back of those peoples’ heads, think about this: they paid over $500 to $700 per person to hear me speak for 3 1/2 hours. Later on some of these people have found my materials so valuable, they paid to have me teach again at their companies for $20,000 to $30,000 for a few days.

I’ve also taught data engineering bootcamps. These are longer classes that last anywhere from 8-10 weeks. These classes cost anywhere from $20,000 to $40,000 per person. They require that students attend daily and represent a significant time investment. Some of my students had to move, at least temporarily, to San Francisco where I was teaching the class.

In-person classes are another class type that I’ve taught extensively. An in-person developer class costs $3,000 for four days. This doesn’t include any travel expenses, which can be another $2,000. These classes feature very intensive learning of the technology. They don’t have time for people to fall behind. If you’re behind on day one, you’re lost on day four. I’ve seen this happen so many times. The instructor simply lacks the time to help those who need more time.

I’ve established a reputation in the Big Data as one of the best. My students realize it. The conference organizers realize it. The companies I teach at realize it. You will realize it too once you join the course.

You want to get a Data Engineer job – but are you learning and doing the things that will get a you job?

Most Hadoop and Spark training is just that. It’s a Hadoop training company focused on the technologies and buzzwords they support. They are teaching individual technologies.

Companies, on the other hand, are looking for Data Engineers who can create data pipelines. This requires learning several different technologies and how they integrate. These companies need Data Engineers who can create data products, analyze data at scale, visualize data, and help them improve their businesses.

Getting a job as a data engineer isn’t about learning one technology and then calling yourself a data engineer. I’ve had students come to my class after failing several data engineering interviews. They thought Big Data is about a specific technology instead of several technologies and the knowledge of how they fit together.

The depth and breadth of this course allows you attain that knowledge. More importantly, the course forces you to practice what you have learned. This is how you ace the interviews and get several jobs offers for your career switch.

I went so far as to contact my friends who are the hiring managers for data engineering teams at large companies about this course. I went through the course materials, exercises and learning objectives of students. Their response was “where are these people so that I can hire them?” Employers will overlook a lack of experience for someone who shows applied learning.

You may have read some Big Data books, checked out other online courses, or watched some Big Data videos on YouTube already. You’ve realized that they’re a waste of time. Big Data books serve as a great reference book, but suck at teaching. Most online courses are a terrible waste of time because they are usually written and taught by someone without any programming skills or real-world Big Data skills. My courses reflect my years of heads down programming and teaching Big Data at the top data companies.

You’re spending your time watching YouTube videos on Big Data or reading someone’s blog and wondering how accurate it is. You come away learning a little, but you’re nowhere near achieving your goals. You know you still couldn’t pass a data engineering interview.

You may have failed a data engineer interview already. You may be submitting your resume and never getting a call back. You may be wondering what happened or why nothing is happening.

You may have started to feel you are wasting your time. You probably were wasting your time because you weren’t learning from an expert who trains for a living.

Becoming a data engineer is a sizable commitment in time and energy. As you’re going through the course, I want you to think about my other students who are earning $20,000-$60,000 more with their data engineering jobs. I want you to join my ranks of data engineering students who’ve switched careers to Big Data. I want to see your LinkedIn updates over the years as you get promoted.

Make your career switch to fulfill your dream of working with Big Data.

The program was new so I couldn’t know for sure that the course wasn’t going to be a waste of time and money. But once I saw the huge list of technologies Jesse promised to cover and the fact that he was a former instructor at Cloudera, I decided to take it.

Greg W.

Solutions Engineer

I wanted to do more challenging things. I saw that [Big Data] was where the future was going in machine learning, so I pivoted my career. My primary goal was to get a job at one of the Silicon Valley big name companies.

It turned out to be a great experience.

Jeff L.

Big Data Engineer

I justified it by looking into the future. I knew the course would give me the necessary background to evolve into the space way more rapidly. The faster I learned it, the faster I could get back to my clients and add even more value to them. The ROI was worth it.

Robert H.

Data Scientist & Evangelist

Are you ready to switch careers and become a Data Engineer?

Part of making my materials as accessible as possible is to make it easy for people to pay for it. I have two methods of paying for a course, one is an installment plan and the other is an outright purchase. The installment plan breaks up the course’s payments into monthly payments over 12 months. With the outright purchase, you pay for the entire course at once.

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This course is sold on an individual basis. People sharing access will be removed from the course and no refunds will be given.

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