What To Consider When Building Data Pipelines - Intro To Data Infrastructure Part 2

When Building Pipelines What Should You Consider
Tools and technology are just that.
🛠️ Tools.
They won’t actually drive any form of impact on their own.
They won’t develop processes that are connected to dashboards that in turn drive actions without people. Nor are the numbers they are creating going to magically jump off the screen and fix a business.
So before building any data pipeline it’s important to consider a few things.
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About me:
I have spent my career focused on all forms of data. I have focused on developing algorithms to detect fraud, reduce patient readmission and redesign insurance provider policy to help reduce the overall cost of healthcare. I have also helped develop analytics for marketing and IT operations in order to optimize limited resources such as employees and budget. I privately consult on data science and engineering problems both solo as well as with a company called Acheron Analytics. I have experience both working hands-on with technical problems as well as helping leadership teams develop strategies to maximize their data.
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[@SeattleDataGuy]

If you guys want to learn more about data engineering, then sign up for my newsletter here seattledataguy.substack.com/ or join the discord here discord.gg/2yRJq7Eg3k

[@DarshilParmar]

Thanks for this video, always learning something new

[@SeattleDataGuy]

Thank you! And I am always learning from you as well!

[@peteintania]

Very informative, as always. Thank you!

[@SeattleDataGuy]

Glad it was helpful!

[@makster92]

Great content! Looking forward to upcoming videos

[@SeattleDataGuy]

Glad you enjoyed it! Plenty more videos coming.

[@pengamax6500]

Really useful! Thank you x

[@SeattleDataGuy]

Glad you found this helpful!

[@vedanthasm2659]

Granular and informative video. Thanks Bro!

[@SeattleDataGuy]

Glad you liked it!

[@lecryptojames]

Great video! Thanks.

[@SeattleDataGuy]

Thank you so much!

[@adalke2]

Terrific overview of key, universal tenets!

[@SeattleDataGuy]

Glad you found it helfpul!

[@harsha2375]

Thanks. After watching ur videos I decided my career to be a data engineer. 💯💯

[@SeattleDataGuy]

Wow! I hope you find a job you enjoy!

[@AbhishekUpperwal]

Hey! Amazing video. Very informative and useful. Which tool is it in the video at 9:00 with all the stats for data quality?

[@SeattleDataGuy]

The tool is called bigeye

[@ralphpatrice1676]

I never been the first in the comments section before 🤪. Thank you for the great information

[@SeattleDataGuy]

Thanks for being the first commenter!!

[@kreedur]

What QCs are you performing? Is there a generally-accepted list?

[@TA-vf8yi]

Hi Seattle Data Guy, since you've worked at Meta, I was thinking if you could do a video on Data Modeling? I keep reading all these blog articles that say that 'dimensional modeling is dead' and so on, so I'm wondering how it is done in top tech companies? I'm trying to learn more about data engineering, so thinking if Kimballs books and STAR schemas are still relevant or is there a new paradigm shift and if so what books would you recommend?

[@SeattleDataGuy]

There are aspects of star schema and data warehousing that still exists at some companies and some companies still strongly rely on it. I think its important to have as a base because as you go forward you will see a lot of different ways people have morphed how to model data. In addition, not everyone can be Google and Meta. These places have a lot of other tools in place that allow them to break some of the rules of data modeling.

[@flyffreak93]

Any good tool for schema analysis for rdms?

[@filibertogarced]

Great video!
E is for extract lol 🍪

[@SeattleDataGuy]

Thanks! Just got to bring it back to the ABCs

[@vinanguyen2583]

Thanks! Can you link to the I T K Funde video?

[@SeattleDataGuy]

Whoops I meant to do that. Thanks for the reminder. Here is the link btw. th-cam.com/video/VtzvF17ysbc/w-d-xo.html

[@hamsansari2111]

Hey Ben
In which env are working mostly with ETL or ELT.

[@SeattleDataGuy]

I have worked in both. Currently I would say its a 40/60 split between ETL and ELT work.

[@JoeG2324]

SSIS handles all our ETL/ELT

[@SeattleDataGuy]

It's a classic. It's where I started

[@navarrodba]

From 20 years beign a DBA to DE