The Bit Bucket

In Kusto Query Language (KQL), materialized views are a key optimization feature that improve the performance of repeated or computationally expensive queries on streaming data. Normally, when you query a real-time data stream, the system must process the incoming events and apply all calculations each time the query runs. As data volumes increase, this can become inefficient and introduce latency.

A materialized view solves this by precomputing and storing the results of a defined query in a physical table. This table is automatically updated as new events arrive.

2026-09-10

Was 2018 a leap year?

What about the year 2000?

Will 2100 be a leap year?

When I was a young student at school, we learned that leap years were every four years. Then as I got older, I learned that there was more to it than that.

It’s important to understand that any calendar is just an approximation, and there is a lot more to calendars than most people realize.

2026-09-09

Scaling an Eventstream involves more than simply increasing capacity — it’s about ensuring that each stage of the data pipeline can handle higher volumes efficiently and reliably.

The first approach to scaling is by adding more inputs and outputs. As the number of data sources or destinations grows, Eventstream can be configured to handle multiple concurrent data feeds, such as several IoT hubs, Kafka topics, or application logs.

You can also tune the Eventstream’s capacity to handle greater throughput. This involves adjusting the compute and memory resources allocated to the Eventstream so that it can process more events per second without increasing latency.

2026-09-08

There’s a UK game show that I like to watch. It’s called Lingo and it’s the standard word game turned into a full TV show. It’s based on a US game show of the same name.

While watching it, I realized that I’ve come to form an immediate opinion on the education level of the contestants, not based on their appearance, not based on their accents, not based on their assortment of tattoos, but on how they pronounce the letter H.

2026-09-07

Streaming systems produce large and continuous data flows, so it’s essential to have a clear data retention strategy. Without one, storage costs and data management complexity can increase very quickly.

The first decision is what type of data to keep. In many cases, raw event data is valuable for troubleshooting or forensic analysis but may not need to be retained indefinitely. Aggregated or summarized data (for example, hourly or daily averages) is often sufficient for long-term trend analysis.

2026-09-06

I recently read the 2nd edition of a book on serverless ETL. Serverless ETL and Analytics with AWS Glue (2nd Edition) . I hadn’t had the chance to read the first edition of this book, but my friends at PackT sent me the 2nd edition to check out.

Authors

Noritaka Sekiyama is a big data engineer working at a data and AI company.

Albert Quiroga is a senior solutions architect at Amazon.

2026-09-05

In practice, most real-time systems use a combination of streaming and batch processing rather than relying entirely on one or the other. This approach is often described as a hybrid architecture, where each path serves a different purpose.

The hot path (also called the streaming path) handles data as it arrives. It’s designed for immediacy, enabling rapid decisions such as triggering alerts or applying automated actions. The trade-off is that it usually works with limited context and may rely on approximations or rolling summaries.

2026-09-04

I’ve never liked how the LEN function works in T-SQL for SQL Server.

To test out what others thought, I recently posted a short quiz on my social networks. Here’s the question:

What would you expect the output from this query to be?

DECLARE @Value1 varchar(10) = 'Hello   ';
DECLARE @Value2 varchar(10) = 'There ';

SELECT LEN(@Value1), LEN(@Value2), LEN(@Value1 + @Value2);

Nothing tricky. The first value has 3 trailing spaces. The second value has one.

Then I gave the following answer options:

2026-09-03

Monitoring brand reputation has become a continuous challenge in the age of social media. Platforms like X, Facebook, and Reddit can amplify sentiment rapidly, and a single negative trend can reach large audiences within minutes.

Problem to solve

Traditional sentiment analysis systems are often designed for batch processing — for example, analyzing posts every few hours or once per day. While this can capture overall trends, it fails to provide early warnings. By the time a report shows a spike in negative sentiment, the situation may already be public and difficult to control.

2026-09-02

I recently listened (via Audibla) to Timothy Snyder’s book On Tyranny: Twenty Lessons from the Twentieth Century . I wasn’t sure what I’d make of it but I’d heard it was interesting, and I noticed that on a show led by Rachel Maddow.

Author

Timothy Snyder is a well-known author, historian and intellectual. He’s an expert on eastern Europe and on the Second World War.

On Tyranny

I decided to go through this book for two reasons. I was fascinated by his thoughts on tyranny, and I liked the fact that it was a short book. I’m also a fan of books that are narrated by their authors, and Snyder is the narrator for this book.

2026-09-01