Thomas Petter’s Post

Databricks' AI Assistant went GA today. But is it truly GA ready? I took it for a SQL spin (see video), and I am happy to say that not only is it ready, but it has come a long way to become an "A" grade feature in Databricks. Here are some thoughts: Pros: +Leveraged metadata from the UC catalog to not only understand which fields to choose, but also to locate the table I was looking for without specifiying the table (see "Test 1" as an example). +While I didn't need to fix a broken query, it consistently gave functional SQL output and was able to adjust an existing query (second part of Test 6) successfully. +It was able to formulate a complex SQL query extremely well. I was 100% expecting "Test 4" to fail, but at a quick glance of the SQL query, it looked right and about what I would expect the query to look like for this finance/accounting exercise. +Fairly nit-picky prompts (Test 5 and 6) returned solid SQL code. +It followed my instructions very well without adding unnecessary details that I did not ask for. Cons: -While I didn't encounter errors during my testing, diagnosing broken code still is hit or miss from time to time. Closing Thoughts: Benjamin Rogojan (Seattle Data Guy) and I were chatting today about Text-2-SQL experiences from the past as we were recording a video, and after seeing Databricks AI Assistant go GA, I knew I had to put it through a test like this. Based on my experience with it, I would definitely pick the Databricks AI Assistant to write SQL code for me any day vs leveraging another tool such as ChatGPT, specially with its connection to your Unity Catalog metadata. Thank you for watching, and thank you to SunnyData for letting me borrow the compute!

Databricks' AI Assistant went GA today. But is it truly GA ready? I took it for a SQL spin (see video), and I am happy to say that not only is it ready, but it has come a long way to become an "A" grade feature in Databricks. Here are some thoughts: Pros: +Leveraged metadata from the UC catalog to not only understand which fields to choose, but also to locate the table I was looking for without specifiying the table (see "Test 1" as an example). +While I didn't need to fix a broken query, it consistently gave functional SQL output and was able to adjust an existing query (second part of Test 6) successfully. +It was able to formulate a complex SQL query extremely well. I was 100% expecting "Test 4" to fail, but at a quick glance of the SQL query, it looked right and about what I would expect the query to look like for this finance/accounting exercise. +Fairly nit-picky prompts (Test 5 and 6) returned solid SQL code. +It followed my instructions very well without adding unnecessary details that I did not ask for. Cons: -While I didn't encounter errors during my testing, diagnosing broken code still is hit or miss from time to time. Closing Thoughts: Benjamin Rogojan (Seattle Data Guy) and I were chatting today about Text-2-SQL experiences from the past as we were recording a video, and after seeing Databricks AI Assistant go GA, I knew I had to put it through a test like this. Based on my experience with it, I would definitely pick the Databricks AI Assistant to write SQL code for me any day vs leveraging another tool such as ChatGPT, specially with its connection to your Unity Catalog metadata. Thank you for watching, and thank you to SunnyData for letting me borrow the compute!

Databricks' AI Assistant went GA today. But is it truly GA ready? I took it for a SQL spin (see video), and I am happy to say that not only is it ready, but it has come a long way to become an "A" grade feature in Databricks. Here are some thoughts: Pros: +Leveraged metadata from the UC catalog to not only understand which fields to choose, but also to locate the table I was looking for without specifiying the table (see "Test 1" as an example). +While I didn't need to fix a broken query, it consistently gave functional SQL output and was able to adjust an existing query (second part of Test 6) successfully. +It was able to formulate a complex SQL query extremely well. I was 100% expecting "Test 4" to fail, but at a quick glance of the SQL query, it looked right and about what I would expect the query to look like for this finance/accounting exercise. +Fairly nit-picky prompts (Test 5 and 6) returned solid SQL code. +It followed my instructions very well without adding unnecessary details that I did not ask for. Cons: -While I didn't encounter errors during my testing, diagnosing broken code still is hit or miss from time to time. Closing Thoughts: Benjamin Rogojan (Seattle Data Guy) and I were chatting today about Text-2-SQL experiences from the past as we were recording a video, and after seeing Databricks AI Assistant go GA, I knew I had to put it through a test like this. Based on my experience with it, I would definitely pick the Databricks AI Assistant to write SQL code for me any day vs leveraging another tool such as ChatGPT, specially with its connection to your Unity Catalog metadata. Thank you for watching, and thank you to SunnyData for letting me borrow the compute!

Databricks' AI Assistant went GA today. But is it truly GA ready? I took it for a SQL spin (see video), and I am happy to say that not only is it ready, but it has come a long way to become an "A" grade feature in Databricks. Here are some thoughts: Pros: +Leveraged metadata from the UC catalog to not only understand which fields to choose, but also to locate the table I was looking for without specifiying the table (see "Test 1" as an example). +While I didn't need to fix a broken query, it consistently gave functional SQL output and was able to adjust an existing query (second part of Test 6) successfully. +It was able to formulate a complex SQL query extremely well. I was 100% expecting "Test 4" to fail, but at a quick glance of the SQL query, it looked right and about what I would expect the query to look like for this finance/accounting exercise. +Fairly nit-picky prompts (Test 5 and 6) returned solid SQL code. +It followed my instructions very well without adding unnecessary details that I did not ask for. Cons: -While I didn't encounter errors during my testing, diagnosing broken code still is hit or miss from time to time. Closing Thoughts: Benjamin Rogojan (Seattle Data Guy) and I were chatting today about Text-2-SQL experiences from the past as we were recording a video, and after seeing Databricks AI Assistant go GA, I knew I had to put it through a test like this. Based on my experience with it, I would definitely pick the Databricks AI Assistant to write SQL code for me any day vs leveraging another tool such as ChatGPT, specially with its connection to your Unity Catalog metadata. Thank you for watching, and thank you to SunnyData for letting me borrow the compute!

Databricks' AI Assistant went GA today. But is it truly GA ready? I took it for a SQL spin (see video), and I am happy to say that not only is it ready, but it has come a long way to become an "A" grade feature in Databricks. Here are some thoughts: Pros: +Leveraged metadata from the UC catalog to not only understand which fields to choose, but also to locate the table I was looking for without specifiying the table (see "Test 1" as an example). +While I didn't need to fix a broken query, it consistently gave functional SQL output and was able to adjust an existing query (second part of Test 6) successfully. +It was able to formulate a complex SQL query extremely well. I was 100% expecting "Test 4" to fail, but at a quick glance of the SQL query, it looked right and about what I would expect the query to look like for this finance/accounting exercise. +Fairly nit-picky prompts (Test 5 and 6) returned solid SQL code. +It followed my instructions very well without adding unnecessary details that I did not ask for. Cons: -While I didn't encounter errors during my testing, diagnosing broken code still is hit or miss from time to time. Closing Thoughts: Benjamin Rogojan (Seattle Data Guy) and I were chatting today about Text-2-SQL experiences from the past as we were recording a video, and after seeing Databricks AI Assistant go GA, I knew I had to put it through a test like this. Based on my experience with it, I would definitely pick the Databricks AI Assistant to write SQL code for me any day vs leveraging another tool such as ChatGPT, specially with its connection to your Unity Catalog metadata. Thank you for watching, and thank you to SunnyData for letting me borrow the compute!

Databricks' AI Assistant went GA today. But is it truly GA ready? I took it for a SQL spin (see video), and I am happy to say that not only is it ready, but it has come a long way to become an "A" grade feature in Databricks. Here are some thoughts: Pros: +Leveraged metadata from the UC catalog to not only understand which fields to choose, but also to locate the table I was looking for without specifiying the table (see "Test 1" as an example). +While I didn't need to fix a broken query, it consistently gave functional SQL output and was able to adjust an existing query (second part of Test 6) successfully. +It was able to formulate a complex SQL query extremely well. I was 100% expecting "Test 4" to fail, but at a quick glance of the SQL query, it looked right and about what I would expect the query to look like for this finance/accounting exercise. +Fairly nit-picky prompts (Test 5 and 6) returned solid SQL code. +It followed my instructions very well without adding unnecessary details that I did not ask for. Cons: -While I didn't encounter errors during my testing, diagnosing broken code still is hit or miss from time to time. Closing Thoughts: Benjamin Rogojan (Seattle Data Guy) and I were chatting today about Text-2-SQL experiences from the past as we were recording a video, and after seeing Databricks AI Assistant go GA, I knew I had to put it through a test like this. Based on my experience with it, I would definitely pick the Databricks AI Assistant to write SQL code for me any day vs leveraging another tool such as ChatGPT, specially with its connection to your Unity Catalog metadata. Thank you for watching, and thank you to SunnyData for letting me borrow the compute!

Databricks' AI Assistant went GA today. But is it truly GA ready? I took it for a SQL spin (see video), and I am happy to say that not only is it ready, but it has come a long way to become an "A" grade feature in Databricks. Here are some thoughts: Pros: +Leveraged metadata from the UC catalog to not only understand which fields to choose, but also to locate the table I was looking for without specifiying the table (see "Test 1" as an example). +While I didn't need to fix a broken query, it consistently gave functional SQL output and was able to adjust an existing query (second part of Test 6) successfully. +It was able to formulate a complex SQL query extremely well. I was 100% expecting "Test 4" to fail, but at a quick glance of the SQL query, it looked right and about what I would expect the query to look like for this finance/accounting exercise. +Fairly nit-picky prompts (Test 5 and 6) returned solid SQL code. +It followed my instructions very well without adding unnecessary details that I did not ask for. Cons: -While I didn't encounter errors during my testing, diagnosing broken code still is hit or miss from time to time. Closing Thoughts: Benjamin Rogojan (Seattle Data Guy) and I were chatting today about Text-2-SQL experiences from the past as we were recording a video, and after seeing Databricks AI Assistant go GA, I knew I had to put it through a test like this. Based on my experience with it, I would definitely pick the Databricks AI Assistant to write SQL code for me any day vs leveraging another tool such as ChatGPT, specially with its connection to your Unity Catalog metadata. Thank you for watching, and thank you to SunnyData for letting me borrow the compute!

Databricks' AI Assistant went GA today. But is it truly GA ready? I took it for a SQL spin (see video), and I am happy to say that not only is it ready, but it has come a long way to become an "A" grade feature in Databricks. Here are some thoughts: Pros: +Leveraged metadata from the UC catalog to not only understand which fields to choose, but also to locate the table I was looking for without specifiying the table (see "Test 1" as an example). +While I didn't need to fix a broken query, it consistently gave functional SQL output and was able to adjust an existing query (second part of Test 6) successfully. +It was able to formulate a complex SQL query extremely well. I was 100% expecting "Test 4" to fail, but at a quick glance of the SQL query, it looked right and about what I would expect the query to look like for this finance/accounting exercise. +Fairly nit-picky prompts (Test 5 and 6) returned solid SQL code. +It followed my instructions very well without adding unnecessary details that I did not ask for. Cons: -While I didn't encounter errors during my testing, diagnosing broken code still is hit or miss from time to time. Closing Thoughts: Benjamin Rogojan (Seattle Data Guy) and I were chatting today about Text-2-SQL experiences from the past as we were recording a video, and after seeing Databricks AI Assistant go GA, I knew I had to put it through a test like this. Based on my experience with it, I would definitely pick the Databricks AI Assistant to write SQL code for me any day vs leveraging another tool such as ChatGPT, specially with its connection to your Unity Catalog metadata. Thank you for watching, and thank you to SunnyData for letting me borrow the compute!

Databricks' AI Assistant went GA today. But is it truly GA ready? I took it for a SQL spin (see video), and I am happy to say that not only is it ready, but it has come a long way to become an "A" grade feature in Databricks. Here are some thoughts: Pros: +Leveraged metadata from the UC catalog to not only understand which fields to choose, but also to locate the table I was looking for without specifiying the table (see "Test 1" as an example). +While I didn't need to fix a broken query, it consistently gave functional SQL output and was able to adjust an existing query (second part of Test 6) successfully. +It was able to formulate a complex SQL query extremely well. I was 100% expecting "Test 4" to fail, but at a quick glance of the SQL query, it looked right and about what I would expect the query to look like for this finance/accounting exercise. +Fairly nit-picky prompts (Test 5 and 6) returned solid SQL code. +It followed my instructions very well without adding unnecessary details that I did not ask for. Cons: -While I didn't encounter errors during my testing, diagnosing broken code still is hit or miss from time to time. Closing Thoughts: Benjamin Rogojan (Seattle Data Guy) and I were chatting today about Text-2-SQL experiences from the past as we were recording a video, and after seeing Databricks AI Assistant go GA, I knew I had to put it through a test like this. Based on my experience with it, I would definitely pick the Databricks AI Assistant to write SQL code for me any day vs leveraging another tool such as ChatGPT, specially with its connection to your Unity Catalog metadata. Thank you for watching, and thank you to SunnyData for letting me borrow the compute!