Make a career shift and become a Data Engineer
If designing web services, writing CSS styles 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.
Back in 2016 I was in one of these situations. 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, and today I can say that it was the best choice I could make at that time!
Unfortunately, I spent almost 2 years at discovering data engineering concepts and figuring out how to use them. Meantime I worked on different data projects in streaming and batch, applied serverless approach, experienced different data architectures and read a lot. It took me a lot of time to figure the things out. You can follow my path or take a shortcut.
I used all my previous experiences to create a learning path based on the problems I faced, and the solutions and patterns I found to solve them. It will help you to assimilate basic data concepts faster and gain some hands-on experience before you write your first data pipelines as a data engineer. I'm saying hands-on because you'll not only get the learning material but also have to implement a data system on your own!
Curious about the learning path? See the description of the content below.
My name is Bartosz Konieczny and I am a data engineer working with software for 2009. I'm also an Apache Spark enthusiast, AWS Big Data certified user, blogger and speaker. I like to share and you can discover it on my waitingforcode.com blog or conferences like Spark+AI Summit 2019.
- Problem statement
- Visualization types
- Data exploration
- Data exploration - Jupyter example
- Data visualization - Python frameworks
- Data visualization - Reporting tools
- Data catalog
- Data mart
- Data visualization and batch processing
- Data visualization and streaming processing
- Best practices
- Problem statement
- Main concepts
- ML workflow
- Compute environment
- ML workflow - Notebook demo
- ML workflow - automation demo (ETL)
- Online learning
- Online learning - demo
- Model quality
- Serving layer
- Rendezvous architecture
- ML engineer
- ML workflow platform
- ML workflow platform - demo
Watch 3 samples of Become a Data engineer course
What I will get?
👉 12 weeks online course
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.
👉 hands-on project using modern Open Source technologies
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.
👉 homework individual feedback
I will give you an individual feedback for every homework you do. And if you want, you can also ask your classmates for one!
👉 free guides to start with data technologies
Every time I will ask you to work on a new technology, I will provide you a short guide summarizing the basics.
👉 lifetime access to user group
A problem at work that you want to share with your classmates? A doubt about something else? Simply ask, even after the course completion!
👉 12 live calls to answer your and other students questions
Every week we will meet in a live meeting and discuss the problems encountered by all students during the last 7 days.
👉 one-time 30% off for one of next courses
That's only the first data course organized by myself. I'll create a new one soon and, as the member of Become a Data Engineer course, you will get 30% off for it.
👉 lifetime access to the course and material updates
Data engineering is continuously evolving. New data sources, new approaches, new problems... you will be aware of them with every course update
👉 English, French or Polish communication
If you are not feeling comfortable to ask questions in English, you can do it in French or Polish. I will answer you in your preferred language!
Frequently asked questions
❓ What I will be capable of by the end of the course?
After 12 weeks you should be able to:
- understand scalability in data distributed system
- design data systems
- know the most important patterns of modern data architectures
- understand globally data concepts and use that to understand existing data frameworks and data stores
- write batch and streaming pipelines with modern data frameworks and data stores
- switch faster to cloud managed services
❓When does the course start and how long does it take?
The course is intended to start 4 times a year, March, June, September and November. 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 write some code and execute Docker images on your computer.
❓What about the code snippets?
Most of the code snippets are written in Scala, Java and Python. However, they use very basic concepts of these languages, so even if you don't know any of them, you should be able to understand the examples.
❓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 [email protected]
❓Do you have a group offer?
YES! If you have a team of 3 people or more, contact me at [email protected] 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. Just let me know when you post a question on the forum.
❓How can I ask a question?
You can ask your questions during weekly Live call event or on our forum. Any other communication channel is not involved in the course.
❓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.
I will give the examples in Scala or Python but you can do the homework exercises in any language you want.
❓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.
❓What I will code during the course?
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.