[Day 170] Uber data engineering project using GCP and Mage

 Hello :)
Today is Day 170!


A quick summary of today:
  • decided to give mage.ai another go and now use it alongside GCP


Well... I obviously did not suffer enough with the countless problems I experienced when I was first learning about mage from DataTalksClub's MLOps zoomcamp, so I decided to do a cool-looking data engineering project [youtube]. The caveat is that I will use GCP. And I just hope I do not incur any major costs. I checked, and I have 21 days left on plenty of free credits. 

Getting to the project

It uses the infamous NYC taxi dataset. 

Using lucid I learned a bit amout data deminsion modelling

Then using python, some basic preprocessing was done on the raw data, to convert it into the top 8 tables. I actually put everything so far on my github, and plan on doing a nice readme documentation once everything is finished. Even though I started it after work today, I did not finish it because of *again* mage problems. 

Before I get to the mage problems, a bit about GCP.

I set up a cloud storage (similar to S3 in AWS) and uploaded the raw data.

Then I set up a VM with the right access permissions. And this gave ma nice SSH-in browser to install python and run mage. There is a lot of IP addreses, so I am afraid to share pictures today. 

Using that VM, I installed python, pip and mage, and started a project.

Added a block to import data, and a transformer block that basically follows the jupyter notebook in my repo. However whenever I run the transformer block that ends up with the above 8 tables, the kernel restarts and seems like it never starts again. I restart mage through the terminal, same thing happens.

Turns out mage bugs out when I was converting 8 datasets to dicts, and when I just retirn 8 dataframes as a tuple - it is all fine.

Then I created a user and credentials so I can connect to big query and push data to it, 

The final mage pipeline looks like this:

And ~

yay~~~

This took a bit of debugging as well due to uninstalled packages and putting access keys and secrets i n the right place and format. 

FYI, this is the project stack:

So for the final step ~ using looker for some kind of a visualisation dashboard.
This is the final dashboard (unsurprisingly, looker is very similar to powerBI). Looks awesome. Can't wait to apply what I learned to data of my choosing.

Lastly, I deleted everything from GCP, but I know what I need next time. ^^


Just a quick mention - today I kept reviewing my intro that I wrote yesterday, and added a table which I can share. 

All of these papers' summaries/notes I have shared throughout the last few weeks, and I was taught that tables like that (and even more detailed) are a good idea in a paper.


That is all for today!

See you tomorrow :)

Popular posts from this blog

[Day 198] Transactions Data Streaming Pipeline Porject [v1 completed]

Image
 Hello :) Today is Day 198! A quick summary of today: data streaming pipeline project [v1 done] Here is a link to the project's repo. Well ... I did not know I can do it in a day (~14 hours) after yesterday's issues but here we are. Turns out in order to insert the full (~70 variables with nested/list structure), I need the proper pyspark schema. And yesterday I did not have that and that is why when I was reading data in the kafka producer I was getting NULL in the columns - my schema was wrong. Well today I not only fixed the schema for the 4 variables I had yesterday, but included *all* the variables that come from the Stripe API ~ 70 (for completeness).  When I run docker-compose, the data streams and is input into the postgres db (and is still running). Unfortunately, the free Stripe API for creating realistic transactions has a limit of 25, so every 3 seconds, 25 new transactions are sent to the db. It has been running half the day (since I got that set up) and as I am wr
Read more

[Day 107] Transforming natural language to charts

Image
 Hello :) Today is Day 107! A quick summary of today: created text2chart - transforming natural language to charts [ github ] [ webapp ] I am super sick, but in the around 3 hours of my fever not being high, I managed to study a bit. I was again thinking about creating some nlp project but was stuck on the idea of how to host a model. After looking around I found this article where they create a similar app called chat2vis  and they use codellama. I know codellama is an open source model that I can get from huggingface and I wondered, how did they host it. Turns out - they ask the user to input a huggingface api key, which then allows for querying and generating content. (AWESOME! this is applicable to some of the other PDF chat apps that I have, but I will look to make an app of them later).  My fever eased a bit, and I sat on my chair to do some work ^^ Here is a breakdown of the app. I used streamlit. Also, 2 default datasets during dev: Financial Statements of Major Companies(2009-
Read more

[Day 54] I became a backprop ninja! (woohoo)

Image
 Hello :) Today is Day 54! A quick summary of today: Read through cs231n's neural network lectures [1] [2] [3] Spent 8 hours going over Andrej Karpathy's backprop ninja tutorial After yesterday's confidence boost in doing backprop, I came in today with the intention of becoming of, as Andrej Karpathy calls it, a backprop ninja.  The cs231n NN lectures were the usual about how NN became to be, param initialization, activation functions, loss functions, regularization and optimizers. Nothing out of the ordinary, but still was good to reassure myself of that knowledge. But that was definitely not the main even of today. As I mentioned, I wanted to become (or at least attempt to become) a backprop ninja today. I have watched that particular tutorial twice, the 1st time I had no idea what is going on, the 2nd time I could follow, and now it was the 3rd time - and now, I wanted to participate and actually see the why and the how of the things we have to do, when we do manual back
Read more