It really pains me to see people

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 our clients share the same struggle to learn Big Data. Steve’s team struggled for six months before turning to us for training. “We had all the books and tried to work through the steps, but our use of relationship database systems like MySql and Oracle meant we were approaching Hadoop in a fundamentally incorrect way. Our RDBMS solution was slowing down the Big Data solution.”

Imran took our course after struggling to learn Big Data on his own. “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 our 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’m a Senior Java Developer — I have those skills — but I had no idea about the big data ecosystem.”

Companies and students come to our 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, we were 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.

Want to see how it easy it to learn the concepts Shikha was talking about? Here’s the video:

That’s our 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 we’ve become the trusted source for Fortune 100 companies. They depend on me to teach their Software Developers the skills to become Data Engineers. We’re 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.

Doug Cutting

Creator of Apache Hadoop

One of the finest trainers in Big Data.

Ben Lorica

Chief Data Scientist at O’Reilly Media

Our innovative approach

takes teams and makes them

data engineering teams.

Focusing on a single Big Data technology like Hadoop or Spark will only get you so far. Most people don’t realize that even a simple Big Data solution is made up of 5-30 different technologies.

Courses that only focus on a single technology are 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.

We have five principles to becoming a data engineering team. Five technical principles and five management principles.

The Five Principles of

Becoming a Data Engineer

We’ve taught thousands of students. Out of this vast experience, we’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 makes a project successful.

The Five Principles of

Managing a Data Engineering Team

We’ve taught hundreds of data engineering teams. Out of this vast experience, we’ve created a system that helps managers run a data engineering team by following five principles:

 

  1. Individual contributors must specialize in Big Data to be successful.
  2. Your teams need to learn many different technologies.
  3. Big Data is constantly changing and evolving and your team should be constantly learning.
  4. Big Data projects must be iterative and build on progress of previous iterations.
  5. Even managers need to have a conceptual understanding of how things work and the technology behind it.

Following these principles

is the difference between

success and failure.

At Big Data Institute, we create successful data engineering teams. Following these principles is why our clients understand how Big Data makes you more profitable, productive, and preeminent. They create projects that make their their company millions. They avoid the failing projects that costs their companies millions.

We want to show you how to be more profitable, productive, and preeminent with your Big Data. Contact us and take the first step in guaranteeing your success with Big Data.

I want to get the most innovative training and learn the principles

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