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
Coming from someone switching from teaching physics at the moment (quantum physics / data science background) back into data aspiring to work as a data engineer, thanks for the clear overview (having now watched a few videos on the history).
Wonderful summary. I really like your analysis and point of view! I agree with most of the stuff discussed here, and, to add my 2 cents, talking from a decade of experience plus plus... I think what we are seeing is more of the same... I even with the rise of snowflake and databricks, can't spot any real innovation or change. It is all just building on the same old tech. But perhaps the innovation should come from the actual organizations and their data strategies (which most of them simply don't have!)
Your videos are really on point. The data industry is indeed always changing and it is hard for customers to keep with this pace of new releases. It is even harder to see the value you could get from implenting one of the new kid on the block in the data industry as many tools appear to fix issues of a former one, which is not really easy to sell to customers with tight budgets. Now there is all the fuze around data observability, but this adds more licences to the whole landscape without giving a clear ROI to customer who want to implement it. Where do you feel the industry will go to? More and more to a constellation of scattered solutions we need to inegrate and make work together or to the data platforms/fabrics that try to be the one-stop-shops of the data world? I personnaly think the latter will be predominant in the large enterprises but your input interest me a lot. :)
Hey Ben, I started learning data engineering some weeks ago. I'm loving everything and your channel is helping me the most. My only problem right now is the fear of using AWS and explode bills cost because of mistakes. I know it's possible set alerts or even scripts that are triggered by alerts to kill all resources but the fear don't go away. I'm learning and I know I will make mistakes, but what I can do to minimize the risks? Thank you!
Just forget about data engineering. It takes years to learn. I have been on thos roads for 1.5 year and still have no clue whats going on. There are too many technologies
Hello, my major is MIS, I want to become data engineer, I thought to start first as data analyst and work my way up to data engineer. What do you think?
presently there are loads of (> 80%) doesn't know how to write or extrapolate Hadoop, MapR and Hadoop ecosystem code, consider themselves to above "Engineers" or "Science" , "ML", "DevOp" or even "Developers" position, drawing more than whatever figure remuneration packages.......that's what's modern cloud companies have brought this sector, is it sad or progress, sorry the latest being ChatGTP's in the "ML" space......
There are 2 types of data engineers: businessey data engineers and big data wngineers. In this video he mostly focuses on big data engineers requiring big data technologies like spark and hadoop. Anyhow I am with you
@@DroisKargva This is an interesting distinction that I do not usually see in the market but would love to push forward. Do you think this vision is still valid with the Cloud Data Platforms that sell us the point that they can handle big data without Hadoop? Like Databricks, Snowflake, BigQuery or the new fabrics from Microsoft, SAP and Orcale?
![Blox Fruits Dragon Rework Update [Full Stream]](http://i.ytimg.com/vi/EqDAp8udhm0/mqdefault.jpg)
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
Thanks for the video! Love your cats as well.
Thank you! she loves jumping in
Coming from someone switching from teaching physics at the moment (quantum physics / data science background) back into data aspiring to work as a data engineer, thanks for the clear overview (having now watched a few videos on the history).
Holy shit this videos are really on point.
thank you! personally one of my favorites but i know its not for everyone.
This was very good content! Thank you
Glad it was helpful!
Wonderful summary. I really like your analysis and point of view!
I agree with most of the stuff discussed here, and, to add my 2 cents, talking from a decade of experience plus plus... I think what we are seeing is more of the same... I even with the rise of snowflake and databricks, can't spot any real innovation or change. It is all just building on the same old tech.
But perhaps the innovation should come from the actual organizations and their data strategies (which most of them simply don't have!)
Happy 10th dataversary Ben 🏆📆💪
thank you!
Your videos are really on point.
The data industry is indeed always changing and it is hard for customers to keep with this pace of new releases. It is even harder to see the value you could get from implenting one of the new kid on the block in the data industry as many tools appear to fix issues of a former one, which is not really easy to sell to customers with tight budgets.
Now there is all the fuze around data observability, but this adds more licences to the whole landscape without giving a clear ROI to customer who want to implement it.
Where do you feel the industry will go to?
More and more to a constellation of scattered solutions we need to inegrate and make work together or to the data platforms/fabrics that try to be the one-stop-shops of the data world?
I personnaly think the latter will be predominant in the large enterprises but your input interest me a lot. :)
Thanks for the great comment, I probably need to make a video on this whole subject.
thank you for your content!
My pleasure!
please provide full course of data engineer basic o advanced.,
thanks you
Hey Ben, I started learning data engineering some weeks ago. I'm loving everything and your channel is helping me the most. My only problem right now is the fear of using AWS and explode bills cost because of mistakes. I know it's possible set alerts or even scripts that are triggered by alerts to kill all resources but the fear don't go away. I'm learning and I know I will make mistakes, but what I can do to minimize the risks? Thank you!
Just forget about data engineering. It takes years to learn. I have been on thos roads for 1.5 year and still have no clue whats going on. There are too many technologies
always migrating data since the stack continually changes
next year we'll all just be migrating data back on prem
hey any palantir update? do you use the software or whats going on?
Can you do a vid about migrating data from a traditional db to cloud (preferably snowflake)
Create a backup then take that and load it into cloud
What is the summary?
Hello, my major is MIS, I want to become data engineer, I thought to start first as data analyst and work my way up to data engineer. What do you think?
Thats easy and right way to do it. Its super complicated to jump right into data engineering.
presently there are loads of (> 80%) doesn't know how to write or extrapolate Hadoop, MapR and Hadoop ecosystem code, consider themselves to above "Engineers" or "Science" , "ML", "DevOp" or even "Developers" position, drawing more than whatever figure remuneration packages.......that's what's modern cloud companies have brought this sector, is it sad or progress, sorry the latest being ChatGTP's in the "ML" space......
What happened to color of your video? LOL
good question, maybe it was lighting....hmm
yep i don’t know how to do any of those and i am a data engineer. i’ll probably never need to know any of that information.
There are 2 types of data engineers: businessey data engineers and big data wngineers. In this video he mostly focuses on big data engineers requiring big data technologies like spark and hadoop.
Anyhow I am with you
@@DroisKargva This is an interesting distinction that I do not usually see in the market but would love to push forward.
Do you think this vision is still valid with the Cloud Data Platforms that sell us the point that they can handle big data without Hadoop? Like Databricks, Snowflake, BigQuery or the new fabrics from Microsoft, SAP and Orcale?