Video

Nice video. You showcased an example of Duck DB integration with Unity Catalog. Can you please help me validate if my understanding captured in below points, about the behaviour of "open sourced Unity Catalog" (UC)? 1. Within UC, we can apply the Data Security changes like data masking. When this UC is accessed from Duck DB, the columns will appear as masked there too. 2. Similarly, other Security config such as row level Security, column level Security will also be visible in Duck DB. 3. Similar to "attach accounts_prod" command in Duck DB, we can integrate UC with other lakehouse implementations such as Microsoft Fabric and even on-prem Delta Lake too (or at least such integration is in roadmap). 4. Such tables are hosted/managed Within Databricks, but are accessed from Duck DB too, which is a reverse of what is done in case of "external table".

Thank you for your great presentation. Could you please provide the source code or share the link to the Git repository

Epic Brit accent

This sounds backwards

system.billing.usage does not contain a cluster_id columns

Would it be possible to generate this on DLT Streaming table ?

Lovely! .... more detailed demo please 🤓 many thanks

No module named 'databricks.vector_search'

I cannt find the "launch genie" button , Do i need to enable anything else ?

The video is playing back strangely interlaced, like a column was out in the stream or what not.

How to install private package in serverless compute? I used init_script with normal compute to add private package access details in /etc/pip.conf BUT with serverless, initi scripts can't be used

oi got stuck because I'm only receiving 3 rows of responses. but my table have 800

Thanks for sharing - great content Naveen

are you hiring?

Epic

Databricks AI is great but a show stopper for adoption in many big organisations is data privacy and residency. My understanding is the data leaves the organisations tenant.

Brilliant 👌🏽

Awesome!

When I choose Timeseries as the profile type and set all the required fields it seems to be working fine, but when I open the dashboard it throws an error like "Table or View {catalog}.{schema}.my_table_profile_metrics Can not be found". should I create the my_table_profile_metrics myself or it is a part of the process?

Awasome. Can I use German to ask these questions?

Did I miss something or did you not mention on how to activate the AI features? Because when I try to use ai_analyze_sentiment() within my serverless Europe West env, I get an error saying AI_FUNCTION_HTTP_REQUEST_ERROR

Wow this is amazing. I wanted to understand how variant data type is different from Struct type? Also second question. How does it work with array of json?

People who don't undertand the video at all (possibly not even finish watching) comment Feifei Li "creepy". That's the time you know most of the world goes crazy.

Love this shorts

I ended up writing a custom function to handle data in batches and recursively exploding lists and normalizing dictionaries. Not having a schema or frontend developers saving elemnts as lists, then dictiomaries and then as bananas was tricky. I will give this one a try 😅

Hello for the video, it could't follow it up, because of the juniper notebook, what do you recommend me to follow in order to replicate what you did in this vidoe. Thank you.

Well to be honest, it is not really free. You still need to pay for the AWS resources set up through CloudFormation. That stack cost me ~7 USD per day.

Awesome!

oh great.

Great video Jason, Thanks for putting together. Would you be able to share the notebooks as well.

Unfortunately, can't understand shit of what he's saying!

Getting an issue when trying to create the serving endpoint. It's saying Served entity creation aborted for served entity `audio_transcription_chatbot_model-1`, config version 1, since the update timed out. Have not been able to figure out why.

懂了，赛博号脉！

Very inspiring! My mind is going att 1000 miles an hour with ideas for our startup and clients from this!

Where can access your code or workbook? Would be nie to run your code.

Holden and team are incredibly engaging and very easy to understand!

Great feature, please also include low code features in order to be more beneficial as Data factory also has for ETL

awesome

Excellent presentation, beginning 3.5-4.0 Billion years ago and explaining all the way to now (AI, non-physical-spatial). Excellent. Thank you. 👏

Who's the speaker?

AI can do everything you need to do in times of studying and understanding AI.

Awesome 👏🏾

🎉

How did parse_json handle schema evolution and from my kowledge, prod table do not recommend parse schema on the fly, it's more safer to define schema first

this is clearly copied from snowflake