The Bit Bucket

I have previously posted information about how to get local date and time happening in Azure SQL Database. Many people have been frustrated about the inability to set any date and time to work with, rather than just UTC.

I posted here about calculating it, and here about how to set it for your session.

While the workarounds were helpful, there were many situations where they just weren’t enough. One simple example is where you’re offering a Software as a Service (SaaS) application and want each tenant database to have its own date and time configuration, but many other people just wanted their database to reflect their own local time.

2026-10-09

An Eventhouse-originally designed for realtime event data-can also serve as a vector database in Fabric RTI, supporting semantic/embedding-based retrieval workflows.

The key enabler is the dynamic column type, which allows you to store unstructured data such as arrays or JSON blobs. This lets your embedding vectors live alongside your ordinary event metadata.

Microsoft recommends using Vector16 encoding (i.e. 16-bit floating representation) rather than full 64-bit. This dramatically reduces storage footprint and boosts the performance of vector operations like dot product or cosine similarity.

2026-10-08

AI development is another really timely subject. I recently read Hands-On AI Development with Python: Build and Deploy Real-World AI, Machine Learning, Deep Learning, and NLP Applications as a review copy I received from my friends at PackT.

Author

Vivian Aranha is described as an AI educator, technology leader, and founder of School of AI. He was worked with Fortune 500 organizations including The Washington Post, Delta Air Lines, and IBM. He currently teaches globally through School of AI, Udemy, Skool, and Maven.

2026-10-07

Retrieval-Augmented Generation, or RAG, is a technique where a large language model is given external context before it generates an answer. In Fabric, Eventhouse can play a central role in supporting real-time RAG solutions.

Because Eventhouse stores both structured and unstructured streaming data, it can serve as a continuously updated source of facts and events. Using built-in vectorization capabilities, recent data can be converted into embeddings and stored for semantic search.

2026-10-06

More SQL and AI love !

We’ve been working on this for a while. PostgreSQL is a great vector database that can support your AI-based applications. And HorizonDB is an awesome managed version of PostgreSQL. Better yet, we’ve just completed a course that can show you how to use it. It’s low-cost ($195 USD) and offers our lifetime access.

Check it out and enrol now here: Using AI Features in HorizonDB

Course Summary

Do you want to start working with AI-related features in Azure HorizonDB? Or are you going to be asked about using databases this way? Either way, you need to be prepared!

2026-10-05

The Model Context Protocol, or MCP, provides a standard way for AI agents to interact with real-world systems and data sources.

RTI can expose real-time data and events through MCP-compatible endpoints.

When integrated with Fabric Real-Time Intelligence, MCP-based agents can consume real-time signals directly from Eventstreams, Eventhouses, or Activator outputs.

For example, an agent could subscribe to anomaly detection events and automatically create an incident ticket, adjust system parameters, or notify a human operator.

2026-10-04

More SQL and AI love !

We’ve been working on this for a while. PostgreSQL is a great vector database that can support your AI-based applications. And we’ve just completed a course that can show you how to use it. It’s low-cost ($195 USD) and offers our lifetime access.

Check it out and enrol now here: Using AI Features in PostgreSQL

Course Summary

Do you want to start working with AI-related features in PostgreSQL? Or are you going to be asked about using databases this way? Either way, you need to be prepared!

2026-10-03

As with any preview feature, there are some practical considerations.

The first is that this is still evolving. Not every scenario or data type is supported yet. Expect some limits on dataset size, refresh frequency, and concurrent analyses.

You must enable the Python plugin in the Eventhouse. This is what provides the model runtime, and enabling it isn’t instant. If you attempt analysis before it’s ready, the system will return an error.

2026-10-02

I’ve done a lot of Microsoft exams over the years, mostly SQL Server ones but plenty of others too. And one thing that I really don’t like is when the questions are:

• Purely academic (ie: would never happen)
• Memory-based (ie: who cares what a particular limit is for a particular SKU today?)
• Clearly not written by someone who actually uses the product

Today, I want to mention an item in the last category.

2026-10-01

Let’s look at where this feature fits in practice.

The first major category is IoT telemetry - monitoring physical equipment. Think of a manufacturing plant where each machine sends readings for vibration, current draw, and temperature. Sudden spikes or drifts in these metrics may indicate wear, imbalance, or failure. The anomaly detector can continuously flag these irregularities before they cause downtime.

Another use case is application or system performance. If you’re monitoring throughput, error rates, or latency across multiple services, anomalies may indicate deployment issues, scaling problems, or outages. Because detection runs continuously and in near real time, operations teams can be notified within seconds.

2026-09-30