Storing events is easy. Turning them into actionable insights in real-time? That’s where ADX shines. Follow along with a simple demo to ingest data in real-time ready for querying.
#lowlatencyanalytics #azuredataexplorer #adx #eventanalytics
https://snape.me/2025/12/event-streams-need-analytics/

Discover why Azure Data Explorer (ADX) outperforms Cosmos DB for event stream analytics. Learn why ADX should be your default choice for real-time insights.
https://snape.me/2025/11/adx-hidden-gem/
#lowlatencyanalytics #azuredataexplorer #eventanalytics

The "Test" column is a remnant of traditional waterfall practices that has no place in a truly agile environment. By shifting left, using automation, and focusing on the unique skills each role brings to the project, you can create a more efficient and effective workflow that delivers high-quality software.
https://snape.me/2025/04/who-tests-features/
#devops #testing #opinion

Even with the best intentions you can find yourself jumping from meeting to meeting without spending any time on sprint tasks. Creating a shared and consistent sprint calendar can help carve out time to focus.
#agile #meetings #process
https://snape.me/2024/11/sprint-calendar/

Address matching. Using all the code from before we can now determine if the entered address is actually a match.
#example #addresses #postal #csharp
https://buff.ly/3MfvJL0

For the latest post in the address matching series. There are generally no field separators between building number and street so how can they be split and then matched?
#example #addresses #postal #csharp
https://buff.ly/3yQe3CP

Using a Levenshtein distance algorithm to find the best match for each field.
#example #addresses #postal #csharp
https://buff.ly/46ZntIL

Given the various ways of formatting and address how can we split it into fields? This is a shorter post on data preparation before we get to the main problem of matching.
#example #addresses #postal #csharp
https://buff.ly/3yEOCEe