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I recently received a review copy of Agentic Coding with Claude Code by Eden Marco from my friends at PackT.

Author

Eden Marco is an LLM specialist at Google Cloud and a LangChain Ambassador with years of experience in software engineering and cloud architecture. Over many years, he has created practical courses based on his real-world experience.

Content

This is a timely and practical guide for developers who are ready to move beyond casual AI-assisted coding and start treating coding agents as serious development tools. Eden’s main argument is that effective agentic coding is not just about writing better prompts. It is about engineering context: deciding what the agent should know, what it should ignore, what tools it should use, and how its work should be structured so that results are repeatable, controlled, and maintainable.

2026-07-03

Warehouses in Microsoft Fabric are designed for structured, relational analytics — and they’re a perfect destination for streaming data that fits those patterns.

In many organizations, data warehouses have traditionally been refreshed through nightly or hourly batch loads. That meant Power BI dashboards and reports were always slightly behind — reflecting yesterday’s or last hour’s data.

With Fabric Eventstreams, we can now stream data directly into Warehouse tables, especially into fact tables that support BI models. This means data becomes queryable within seconds of arrival, rather than waiting for the next batch window.

2026-07-02

Note: This is part of a series of posts where I’m improving some of my older blog posts.

Recently, I wrote about one of the issues with the Invoke-SqlCmd commandlet where it sets the ApplicationName when you use the parameter that should set the host.

Fellow MVP Ben Miller sent me a copy of the Invoke-SqlCmd3 that they were using. It was much better.

I then realized that there were many other options missing from these commandlets (such as options for Database Mirroring and Availablity Groups) and so I set about improving it.

2026-07-01

One of the most powerful features of Microsoft Fabric is the ability to stream data directly into a Lakehouse.

A Lakehouse is a unified storage system that brings together both structured and unstructured data. It can store raw files, like CSV or Parquet, as well as structured tables in Delta format. That means it’s equally comfortable handling real-time events and batch-loaded historical data.

When we connect Eventstreams to a Lakehouse, the incoming data is written directly into Delta tables. This format supports transactional consistency, so you can query the data immediately while new events continue to arrive.

2026-06-30

Day by day, we’re getting further away from needing to use 32-bit DLLs. One place where I keep coming across them, is when doing integration work. The problem is that there are many 32-bit drivers for older systems, and no-one is going to ever bother to write 64-bit drivers for them. So, if you have to use those drivers, you need to run the 32-bit versions. And SSIS has options that allow you to run 32-bit drivers.

2026-06-29

While Power BI remains a fantastic visualization tool for business reporting, and we’ll talk more about it later, Fabric Real-Time Intelligence also includes its own built-in real-time dashboards — designed specifically for operational monitoring and live analytics.

These dashboards are native to the Real-Time Intelligence workspace. They connect directly to KQL Databases or Eventhouses, and they display query results that automatically refresh — often in near real time, with sub-second latency.

2026-06-28

Two of the parameters in SQL Server connections are the Application Name and the Host Name. You can see these in SQL Server if you execute the following command:

SELECT HOST_NAME() AS HostName, APP_NAME() AS ApplicationName;

On my system, that returns:

I’ve always been a fan of having applications identify themselves in their connection strings. It makes tasks like tracing much easier. The tools supplied with SQL Server do a reasonable job of that as you can see above. But many other tools don’t do such a good job.

2026-06-27

Trend analysis is one of the most powerful applications of KQL — it lets you move beyond simple metrics and start understanding how your data evolves over time.

Most trend analysis begins with the make-series operator. This operator constructs a time-series dataset by grouping values into evenly spaced time intervals. You might, for instance, calculate the average response time or error count every minute, hour, or day.

Here’s a simple example:

2026-06-26

One of the common challenges that arises in many applications is around how to optimize the SQL Server performance of dynamic search queries. For example, we’ve all seen applications with screens like this:

Often, there will be many, many more fields to be searched.

Perceived wisdom

What I often hear from developers is that there is no way to optimize the query because you don’t know in advance which options the user will choose. While this is partly true, it assumes that human behaviour is much more random than it really is.

2026-06-25

Anomaly detection is a key use case for real-time analytics — it’s about automatically identifying when something unusual or unexpected happens in your data.

In most systems, normal behavior forms a fairly predictable pattern. For example, transactions follow a steady daily rhythm, CPU usage fluctuates within a typical range, and sensor readings stay within expected bounds.

Anomalies are the data points that break from those patterns — a sudden spike in temperature, a large payment outside normal limits, or a sudden burst of failed login attempts.

2026-06-24