mikulskibartosz.name
Start here
About me
efficacious.engineering
mlops.today
Bartosz Mikulski
Building trustworthy data pipelines because AI cannot learn from dirty data
Featured
Data engineers are data librarians or how to upgrade your data lake to 2500 BCE technology.
What can data engineers learn from (ancient) librarians?
The problem with software testing in data engineering
What if we found a bug in our data pipelines? What if that bug were easy to fix, but it would require a lot of...
Data flow - what functional programming and Unix philosophy can teach us about data streaming
What does stream processing have in common with functional programming and Unix?
Four books to boost your programmer career
I quit my dream job because of a book
All Stories
How to write technical documentation
How to document a software project?
ETL vs ELT - what's the difference? Which one should you choose?
Should you use a data warehouse or build a data lake? When is a data warehouse a better choice? When is it better to build a data lake?
Selecting rows in Pandas
How to use loc, iloc, slice, and row filtering in Pandas
Python decorators explained
How can we define a Python decorator, and when should we use Python decorators.
What is shuffling in Apache Spark, and when does it happen?
When does an Apache Spark cluster perform the shuffle operation?
What is the root cause of problems in software engineering?
What is the primary, unrepairable cause of almost all bugs, data leaks, human problems, etc.?
How to become a better programmer
What's stopping us from getting better at coding
How to teach programming workshops to adults
How to prepare an enjoyable programming workshop that teaches people the skills they need without overwhelming them with new knowledge.
How does a bad interview look like in data engineering
What you should avoid when you interview programmers for a data engineer positition
How to throw useful exceptions
How to make debugging easier by paying attention to the errors you report
Why are programmers slow, and what to do about it?
The one practice that makes every team faster (in the long run)
How to advertise to software engineers, or how do we make terrible tech choices
Why do programmers make wrong decisions when they choose the tools they use?
« Prev
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
Next »