The course is designed for software engineers, data scientists, or BI developers who would like to change their
careers and become data engineers. If designing web services, writing CSS animations or preparing static analytics dashboards don't make you happy anymore
and you think that the data engineering will be your new passion, it's the place for you.
That description sounds familiar to me because I was in that case in 2016. After the years spent on web and web services development with Java, I decided to look around to find a new passionate domain. After a short hesitation between data science and data engineering I chose the latter one.
Unfortunately, I spent almost 2 years at discovering data engineering concepts and figuring out how to use them. Meantime I read many books and blog posts about the data, did different data projects, worked with streaming and batch pipelines, applied serverless approach, tested different data architectures and made a lot of mistakes. I shared a lot of things on my blog waitingforcode.com, various data conferences like Spark+AI Summit 2019, and local meetups like Apache Spark Meetup. As you can see it took me a lot of time to figure the things out. If you want to take a shortcut please keep reading.
I used all my previous experiences to create a learning path based on faced problems, different solutions and patterns. It will help you to assimilate basic data concepts faster and gain some hands-on experience before you write your first data pipelines at work. I'm saying hands-on because you'll not only get a learning material from me but also a complete end-to-end data pipeline to implement at your own!
Curious about the learning path? See the description of the content below.
You've just joined data engineering team at MyBlogAnalytics
and you were asked to implement different data pipelines with
Apache Spark, Apache Kafka, Apache Airflow, Elasticsearch
and PostgreSQL, preferably with Python or Scala.
During your first week you will have to implement a data ingestion part, i.e. move the data from your consumers to the system.
Just after that you'll implement an analytical pipeline and make it visible to the end and not technical users.
By the end of your mission, you'll expose your data through an API to other technical departments of your company. You'll also collaborate with the data scientists of your team to create a Machine Learning pipeline.
Every week you will discover a new data engineering concept in more than 10 lessons between 3-10 minutes each. During each one you will learn data pattern, see them in action and implement them in your own.
To master the presented topics you will work on a data pet project and use modern data technologies like Apache Spark, Apache Kafka and Apache Airflow. You will also receive a course workbook explaining their basics.
I will give you an individual feedback for every homework you do. And if you want, you can also ask your classmates for one!
Every time I will ask you to work on a new technology, I will provide you a short guide summarizing the basics.
A problem at work that you want to share with your classmates? A doubt about something else? Simply ask, even after the course completion!
Every week we will meet in a live meeting and discuss the problems encountered by all students during the last 7 days.
That's only the first data course organized by myself. I'll create 2 new ones soon and, as the member of Become a Data Engineer course, you will get 30% off for one of them.
Data engineering is continuously evolving. New data sources, new approaches, new problematics... you will be aware of them with every course update
If you are not feeling confortable to ask questions in English, you can do it in French or Polish. I will answer you in your prefered language!
$4684 During the first year for only $397
I did my best to give you a valuable content. If for whatever reason you're not satisfied, you can use my "30-Day Money-Back guarantee". Just reply to your purchase receipt email and I will issue a refund.
What I will be capable of by the end of the course?
After 12 weeks you should be able to:
When does the course start and how long does it take?
The course is intended to start 4 times a year, February, April, August and October. You will need at least 12 weeks to finish it. Joining the course outside these dates won't be possible. If you missed the date, you can subscribe to mailing list and be alerted before the next opening.
How long do I have access to this course?
You get a lifelong access to the course, including all updates.
Can I get the access to all lessons at once?
The idea of adding a new topic every week is twofold. First, to not overwhelm you and let you the time to assimilate every week's topic, have time to think about it, make some extra research. Also, it lets the whole group go through the course at the same time and discuss them during live calls.
What do I need to follow the course?
Time and motivation. The content tries to cover as many data engineering parts as possible so it will require a motivation during at least 12 weeks. And technically, if you want to make the homework exercises, you should be able to execute Docker images on your computer.
What if I'm not satisfied with the course?
If for any reason the course doesn't satisfy you, we will issue a refund. The guarantee is valid for 30 days from the first week publication date.
Will I get an invoice?
Sure, just let me know on firstname.lastname@example.org.
Do you have a group offer?
YES! If you have a team of 3 people or more, contact me at email@example.com and you will get a 10% discount!
Can I pay for the course in installments?
It's too complicated logistically, I prefer one-time payment.
How can I communicate with you?
The content and all live calls are in English but if something is unclear I can explain it in French or Polish as well.
How will I get an access to the course?
After the confirmation of your payment I will send you an e-mail with all details to access the learning platform.
How to join live calls?
Every Monday I will send you a date with the link to our Live meetings.
What do I need to make the homework exercises?
You should be able to run Docker images and use an IDE. I will give all my examples on IntelliJ and PyCharm so having the same tools will facilitate the troubleshooting.
What is the format of the course?
Most of the time you will see me explaining concepts with a blackboard. From time to time I will hide myself and show you me screen to explain data concepts with some code or slides. You'll also get some text workbooks to help you to start with the tools and frameworks that we'll use during the course.
If you are still wondering if this course is for you, write to me and describe your doubts. I will try and answer your questions.
My email: firstname.lastname@example.org