Andreas Kretz - Learn Data Engineering
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Learn Data Engineering with Andreas Kretz
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What's the biggest danger when using MongoDB?
Anonymous Poll
24%
The db becoming a dump
34%
Messing up the indexing
42%
Using it but not knowing why
Also going to upload a Vlog later today 😉
How would you feel if I announce a 25% Black Friday sale and you just bought my Data engineering academy full price
Anonymous Poll
48%
Hey, I'm happy for the new guys
17%
Ah, it's OK
27%
I would be annoyed
9%
I'd want a refund and never trust you again
!! Update !! I decided that there will be no Black Friday discount.
I heard a lot of comments and options here and on LinkedIn. I think it would be unfair to the many people who recently bought it full price.

I personally would be super annoyed too.
Not going to do it

If you want a really good data engineering education then check out my academy: https://learndataengineering.com/p/academy

Its a holistic approach to data engineering with a lot of courses inside. Very cheap compared to for instance Udemy's data engineering course.

I'm working every day on bringing more courses and improving the current ones.

We also have a private slack community without the Bitcoin scammers.

if you have any questions just let me know I'm here on telegram
Did a small upgrade of the power plant.
Mini ITX life
Before with the old cpu and small cooler
NEW COURSE!!! Modern Data Warehouses no longer need you to load the data into them. Many warehouses like AWS Redshift, BigQuery or Snowflake allow you to load data directly from files in your Data Lake. This Data Lake integration is the key to flexibility of how you interact with your data. It makes a modern Data Warehouse so nice to use for all kinds of analytics workloads.

In this course you will learn how easy it is to use Data Lakes, Warehouses and BI tools. Load your files into the lake and visualize it in a report. 🚀

Course Contents
Where Data Warehouses fit into a platform
Data Warehouses ETL vs ELT data integration
Direct Access of Data Lake?
Data Warehouses and Data Lakes on AWS & GCP
GCP hands on Example with Cloud Storage, BigQuery, & Data Studio
AWS hands on example with S3, Glue, Athena, and Quicksight
Example with AWS Redshift


https://learndataengineering.com/p/modern-data-warehouses
Moving everything to one cloud is most of the time not possible. Or should we just keep the data where it is and try to connect where possible? Could be a practical approach. Always use the right tool for the job, or is this too complicated?

Link to the video on YouTube: https://youtu.be/bsSUa1CrWqo
Cloud billing can be very difficult to forecast. There are so many variables in play. Are you looking for a solution? In this post @Zach Quinn is showing you a way how to use BigQuery's meta data to calculate and forecast the costs. Really cool idea! Makes me wonder what other services offer this kind of meta data, too.

Here is a quick view in the article:
Data engineers can leverage SQL statements to fetch database metadata in order to calculate costs incurred with PaaS products like BigQuery.
In the article you’ll learn:
-How to access table metadata in BigQuery
-How to use standard SQL to convert bytes to GB and TB
-How to calculate per gigabyte rates

Read "How Data Engineers Can Use SQL to Estimate BigQuery Storage Costs" in our publication "Plumbers of Data Science" on Medium.

https://medium.com/plumbersofdatascience/how-data-engineers-can-use-sql-to-estimate-bigquery-storage-costs-cbcdfca18899