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The best part about databricks is that is unifies batch and streaming workloads. Also provides a single source for structured, semi structured and unstructured data.
I've used both. Snowflake is toast. Serverless Clusters will take away the pain of managing clusters and they are making really fast improvement on delta lake which will reduce a lot of the common pains of filter, joins, and updates in spark.
@@SeattleDataGuy this is all foreign language but I am an investor who’s been very closely watching this company and it sounds top notch from what you say
Awesome breakdown Ben. I'm looking forward to a comparison with Foundry. My personal opinion is Databricks has the edge when it comes to their pay version of Spark and Delta. It's hard to do a direct head-to-head though because many of Foundry's overlapping capabilities are not documented. IMO Foundry has much better e2e capabilities with a built-in version control system, online IDE, CI/CD, and Data Apps/ML/AI tools. I'm a huge fan of both companies and really think these are the two players that will be left standing at the end of the Big Data OS wars.
@@SeattleDataGuy Please do help us understand better from a technical/professional perspective on how Palantir solutions stack up against others in the market (Databricks, Snowflake, AWS, Google, etc.).
great starter !! it'd be interesting to dive a little deeper into delta lake file format and also compare it with iceberg or hudi formats i.e. where they are similar, different, which situations suit one best over the other.
🎯 Key Takeaways for quick navigation: 01:36 *🚀 Databricks offers managed Spark services along with other tools like Delta Lake and MLflow, providing options for data processing and model deployment.* 03:56 *🏠 Databricks and Snowflake both promote the concept of data lake houses, combining data warehouse and data lake functionalities, but with different emphases on use cases.* 05:37 *🛠️ Key components of Databricks include workspaces, notebooks, tables, clusters, jobs, and libraries, providing an integrated environment for data processing and analysis.* 09:33 *📊 Databricks simplifies the transition from notebooks to production by allowing users to create jobs directly from their notebooks, enabling seamless integration and scheduling.* 11:13 *🌟 Databricks facilitates easier productionization of data science workflows compared to alternatives like Snowflake, with integrated features like job creation and version control.* Made with HARPA AI
Subscribed. When you say productionize your job, I thought I was going to see API endpoints to hit to get results for example a regression price prediction model that gives you a number after inputting variables
Upon watching , I am still no clearer with how this software is helpful to me, a data analyst. What is the benefit of using this instead of running python scripts on my local computer to ETL my data into powerbi? Am I missing something?
Enjoyed our video, thx Do you know how to install databricks &PySpark LOCALLY( on laptop ) & code & test locally Perhaps a video will be appreciated by the community WITHOUT depending on AWS/Azure
How would you compare this to SAS Viya? It seems like this is more for building a data lakehouse whereas SAS is primarily for data analysis and analytics (so it might connect to data bricka to get the data). Could you also do data analysis and analyrics well in data bricks?
To me this just looks like a great way to encourage developers to deploy untested code. Are there any testing pipelines built in that prevent job deployment prior to passing tests?
@@SeattleDataGuy a couple reasons: 1. A lot of times when I’m building a data pipeline there’s a lot of SQL queries I need to write to just analyze the data before I start creating certain metrics and to just start understanding the data. Because of Databricks 1000 row limit this is harder to do. If those tables were in a RDBMS or Snowflake I wouldn’t feel as hindered with regards to this very common task. I know it may seem weird to some people but sometimes just being able to see more of your data and scroll through just helps; maybe this is a Junior Data Engineer thing idk. 2. Idk if this is easier in Snowflake but passing a Parameter from a widget into SparkSQL was a pain in the ass in Databricks, the only reason I figured it out was because a notebook written by someone else did the same thing. We use Azure Synapse notebooks and I like that much more than Databricks as well; it was easier to do some of the same things.
@@JimRohn-u8c I understand data discovery is key aspect before modeling, but that can be achieved by using groups by, limit, distinct, windowing etc, don't need to print million rows and export to excel for that.
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
The best part about databricks is that is unifies batch and streaming workloads.
Also provides a single source for structured, semi structured and unstructured data.
Yes those are all a lot of reasons why I do like databricks. Especially their streaming functionality.
Would be cool to see a mini project in databricks where you could compare and highlight why you don't see it as a data engineering / BI first tool
Yeah I will likely do a comparison between this and foundry
I've used both. Snowflake is toast. Serverless Clusters will take away the pain of managing clusters and they are making really fast improvement on delta lake which will reduce a lot of the common pains of filter, joins, and updates in spark.
Oh boy! I want the competition though. The users win in that world
@@SeattleDataGuy this is all foreign language but I am an investor who’s been very closely watching this company and it sounds top notch from what you say
This didn't age well.
Transitioning into using Databricks and this is a great introduction!
Wanted to watch but can't filter that goofy background noise.... Everyone does it and it adds negative value to the listener.
after reading this i can't listen to his voice
Super helpful - have been using databricks in another system without ever really understanding how much of that other system was simply databricks.
Glad it helped!
Awesome breakdown Ben. I'm looking forward to a comparison with Foundry. My personal opinion is Databricks has the edge when it comes to their pay version of Spark and Delta. It's hard to do a direct head-to-head though because many of Foundry's overlapping capabilities are not documented. IMO Foundry has much better e2e capabilities with a built-in version control system, online IDE, CI/CD, and Data Apps/ML/AI tools. I'm a huge fan of both companies and really think these are the two players that will be left standing at the end of the Big Data OS wars.
Glad you enjoyed it! I think I might finally be able to start filming next week for pltr
@@SeattleDataGuy Please do help us understand better from a technical/professional perspective on how Palantir solutions stack up against others in the market (Databricks, Snowflake, AWS, Google, etc.).
Had this on while I was working and it just clicked to me, thank you!
Glad to hear that!
Your approach in this particular video, is simple and precise, not deep, just right enough. Thanks! Devendra
Thank you! You're too kind
Great video! Would love to see another one on Databricks; moving raw data from blob storage, transforming and storing in databricks tables
That would be a great video!
🔥 video! Learning so much from your vids man. I’m not really getting the effective difference between DataBricks and Snowflake.
Filming that video now :)
@@SeattleDataGuy woohooo looking forward to it!
You earned my sub. I am all in. Thank you for this, it was a great help!
Glad you found this helpful!
great starter !! it'd be interesting to dive a little deeper into delta lake file format and also compare it with iceberg or hudi formats i.e. where they are similar, different, which situations suit one best over the other.
Sounds like a great video. Let me see if I can get Ryan Blue in the video.
🎯 Key Takeaways for quick navigation:
01:36 *🚀 Databricks offers managed Spark services along with other tools like Delta Lake and MLflow, providing options for data processing and model deployment.*
03:56 *🏠 Databricks and Snowflake both promote the concept of data lake houses, combining data warehouse and data lake functionalities, but with different emphases on use cases.*
05:37 *🛠️ Key components of Databricks include workspaces, notebooks, tables, clusters, jobs, and libraries, providing an integrated environment for data processing and analysis.*
09:33 *📊 Databricks simplifies the transition from notebooks to production by allowing users to create jobs directly from their notebooks, enabling seamless integration and scheduling.*
11:13 *🌟 Databricks facilitates easier productionization of data science workflows compared to alternatives like Snowflake, with integrated features like job creation and version control.*
Made with HARPA AI
thanks for this!
Super! Thank you so much for the video!
You’re welcome!
really cool all around video, would love to see more videos on databricks where a more deep dive analysis would be given on each topic ;)
I will add it to the list of future data engineering videos
This channel is great, but you may also check mine. Cheers.
Databricks is awesome. Its so easy to work with
It really is!
I love this an would love to learn more and understand what’s needed as a prerequisite before I can get a job
I'm new on the subject, well explained!
glad you found it helpful!
a really cool introduction, thanks a lot!
Thank you! Glad you liked it!
Thank you for sharing this!
My pleasure!
Love Databricks!
Seems to be a decent amount of love for it!
Very clear -- very nice presentation
brilliant intro - well done and and surprisingly i understand a lot of this. (im not a dev nor a data engineer)😄
Excellent content. Exactly what I needed to get started. Thanks
Subscribed. When you say productionize your job, I thought I was going to see API endpoints to hit to get results for example a regression price prediction model that gives you a number after inputting variables
Thanks for this informative video Ben 🙌
Glad you found it informative!
Great overview. Thanks!
You're welcome!
Thanks for your job Ben ;)
Video didn't explain what databrick does. When was table show being built ?
Great content as always.
Thanks for sharing.
that's high quality vid, thx :]
Glad you liked it!
Upon watching , I am still no clearer with how this software is helpful to me, a data analyst. What is the benefit of using this instead of running python scripts on my local computer to ETL my data into powerbi? Am I missing something?
Very useful. Thanks.
You're welcome!
Is Databricks different from Azure Databricks?
Enjoyed our video, thx
Do you know how to install databricks &PySpark LOCALLY( on laptop ) & code & test locally
Perhaps a video will be appreciated by the community
WITHOUT depending on AWS/Azure
Great vid Ben. Like the way, you give tips with commercial thinking behind it. Thanks Mike
Thank you
You're welcome!
We need to have you do a video about SAS Viya too!
How would you compare this to SAS Viya? It seems like this is more for building a data lakehouse whereas SAS is primarily for data analysis and analytics (so it might connect to data bricka to get the data). Could you also do data analysis and analyrics well in data bricks?
Great product overall, but sucks that you can’t use Airflow on it.
I could have sworn I saw a partnership between them and astronomer
Their Workflows orchestrator got super powerful and is easier than airflow imo
To me this just looks like a great way to encourage developers to deploy untested code. Are there any testing pipelines built in that prevent job deployment prior to passing tests?
Testing in data field is completely not like typical software engineering cicd unit testing
Good work
Great video ruined by the annoying background music. The background music was really distracting and annoying. But very good information. Thank you
Whenever it's Databricks there a Romanian around :)
Its the same as Domino
there are some similar features but it is kind of an apples and oranges comparison
I hate Databricks, would rather use Snowflake.
Why?
I would also love to know why!
Agreed. Snowflake + Prefect = 😇
@@SeattleDataGuy a couple reasons:
1. A lot of times when I’m building a data pipeline there’s a lot of SQL queries I need to write to just analyze the data before I start creating certain metrics and to just start understanding the data. Because of Databricks 1000 row limit this is harder to do.
If those tables were in a RDBMS or Snowflake I wouldn’t feel as hindered with regards to this very common task. I know it may seem weird to some people but sometimes just being able to see more of your data and scroll through just helps; maybe this is a Junior Data Engineer thing idk.
2. Idk if this is easier in Snowflake but passing a Parameter from a widget into SparkSQL was a pain in the ass in Databricks, the only reason I figured it out was because a notebook written by someone else did the same thing. We use Azure Synapse notebooks and I like that much more than Databricks as well; it was easier to do some of the same things.
@@JimRohn-u8c I understand data discovery is key aspect before modeling, but that can be achieved by using groups by, limit, distinct, windowing etc, don't need to print million rows and export to excel for that.