A Fine Slice Of SQL Server
In this video, I show how we can integrate with the Azure AI Services Speech service, using two different methods to capture speech from the microphone via our Streamlit application and submit that to Azure OpenAI.
Check out the video and final set of code. There’s an intermediate set of code for detecting a single utterance. But I think the final product works out pretty well.
Comments closedSteven Sanderson compares two functions:
Both grep() and grepl() are functions in R that help us search for patterns in text. Think of them as detectives looking for clues in a big pile of words!
grep(): This function is like a pointer. It tells you where it found the pattern you’re looking for.grepl(): This one is more like a yes/no checker. It tells you if the pattern exists or not.
Read on for examples of each.
Comments closedKevin Wilkie gets down to tuning:
In our last post, we talked about some of my favorite queries I use in Snowflake to see various items of interest – such as finding the worst-performing queries. For today’s post, though, I want to talk about performance tuning.
Yes, you read that right. We’re going to use query history to do some fun performance tuning.
Click through for two queries that can help you find what you may need to tune.
Comments closedFor quite a while, I have been extremely interested in data visualization. Over the last few years, I have been focused on ways to visualize graph databases (regardless of where the data comes from Using force directed graphs to highlight the similarities or “connected communities” in data is incredibly powerful. The purpose of this post is to highlight the recent work that the Kusto.Explorer team has done to visualize graphs in Azure Data Explorer database with data coming from a Fabric KQL Database.
Note: The Kusto.Explorer application used to visualize the graph is currently only supported on Windows.
I’ll just sit here in the corner with my Linux laptop, not jealous or anything.
Comments closedGilbert Quevauvilliers creates a notebook:
I was recently working with a customer where they had more then 100 app workspaces and I was running into some challenges when using the Scanner API in Power Automate.
I then discovered this blog post where they detailed how to download the Scanner API data (DataXbi – admin-scan.py), it was not quite in the format that I needed, so below is my modified code.
The reason that I am downloading the Scanner API into a JSON file is that I find it easier to extract the data that I need using Power BI Desktop.
Click through for the code and how it all works.
Comments closedChad Callihan gets things moving after a few 1s without enough 0s clog up the pipe:
Keeping an Always On Availability Group healthy is crucial, and seeing a non-synchronizing database in an Always On High Availability Group can give you a sinking feeling (pardon the pun). Disregarding the reason for the syncing issue, there are a few ways to resume syncing and get your setup back in the green.
Let’s look at resuming using the SSMS GUI and running a SQL statement.
Read on for the process. I appreciate that Chad also includes the T-SQL operation to do this.
Comments closedKendra Little troubleshoots an error:
At times when shrinking a data file in a SQL Server or Azure SQL Managed Instance/Database, shrink operations may persistently fail with the error:
Msg 1119, Level 16, State 1, Line 11 Removing IAM page ([filenumber]:[pagenumber]]) failed because someone else is using the object that this IAM page belongs to. DBCC execution completed. If DBCC printed error messages, contact your system administrator.
There’s not much documented on this error anywhere that I can find, so I’m sharing my experience with this error.
Click through to see how Kendra was able to get around this issue.
Comments closedSemab Tariq continues a series on internals by covering write-ahead logging:
Write-Ahead Logging (WAL) is a critical feature in PostgreSQL that ensures data integrity and durability. It records changes to the database before they are applied, allowing PostgreSQL to recover data and restore the database to its most recent state in case of a crash or failure.
Before the introduction of Write-Ahead Logging (WAL) in PostgreSQL, PostgreSQL relied on a simpler mechanism for ensuring data integrity, which was less robust and did not support advanced features like point-in-time recovery and replication.
Read on for a bit of history, how write-ahead logging has changed over the years, and the basics of how it works. Write-ahead logging is a common feature in major relational databases and serves a critical purpose for database consistency. There are also some sneaky performance gains you can get out of it.
Comments closed