The Bit Bucket

Our free SDU Tools for developers and DBAs, now includes a very large number of tools, with procedures, functions, and views. The NormalizeForSearch function normalizes a string to make it ready for search operations.

It makes strings comparable by stripping away differences that are usually meaningless for search or matching.

It helps to answer the question: If two strings refer to the same thing, what differences should I ignore before I even start comparing?

2026-04-02

When we work with streaming data, one of the first transformations we often apply is filtering. The reality is that not every event is useful. In fact, in many scenarios, the vast majority of events are just background noise. Filtering gives us a way to narrow the stream down to only the events that actually matter for the business problem we’re solving.

Take IoT telemetry as an example. A device might send thousands of readings every hour, but if 99% of them show perfectly normal operating conditions, storing and analyzing them all just adds cost and complexity. By applying a filter, we could say: only keep the events where the temperature rises above 80 degrees Celsius, or only process readings where a vibration level exceeds a set threshold. This way, we’re focusing on signals of interest instead of wasting resources on noise.

2026-04-01

Our free SDU Tools for developers and DBAs, now includes a very large number of tools, with procedures, functions, and views. The LevenshteinDistance function calculates the Levenshtein distance between two strings.

It essentially answers the question How far apart are these two strings in terms of character edits?, where edits are inserting, deleting, or substituting a character. In this calculation, each edit has a cost of 1.

Empty and NULL values on input return NULL.

2026-03-31

When we talk about ingestion modes in Fabric Real-Time Intelligence, we’re really thinking about how events move from their source into a destination, such as an Eventhouse table or another downstream system.

The pattern you choose affects latency, cost, data quality, and the flexibility of your real-time architecture. There are two broad models that students will encounter: direct ingestion and processing before ingestion.

Direct ingestion is the simplest path. Events arrive from a source such as IoT devices, applications, or an event broker, and they are immediately written into the target system without any intermediate shaping. This mode gives the lowest latency because nothing happens in between. It is most useful when you want to preserve raw events for later analysis, replay, or transformations that happen downstream. It is also the right choice when your first priority is freshness and the consumers are able to handle any necessary cleaning or shaping themselves.

2026-03-30

Our free SDU Tools for developers and DBAs, now includes a very large number of tools, with procedures, functions, and views. The JaroWinklerSimilarity function that we have added calculates the Jaro Winkler similarity for two strings.

It essentially answers the question Do these two short strings probably refer to the same thing, even if they aren’t exactly the same?.

It can be used where typos are common, or characters are transposed, and where prefixes matter more than suffixes. Empty and NULL values on input return NULL.

2026-03-29

Event Schema Sets in Fabric Real-Time Intelligence are essentially a way to standardize the shape of the data coming into your real-time environment. When events are flowing in from a variety of sources—such as IoT devices, applications, APIs, or logs—you typically see a lot of variation: different fields, inconsistent casing, unexpected nested structures, or additional attributes that drift over time. Schema Sets give you a central place to define the expected structure for those events.

2026-03-28

Our free SDU Tools for developers and DBAs, now includes a very large number of tools, with procedures, functions, and views. The FormatBytes function can now be used to take a number of bytes, and to format it as a string, with appropriate units.

The calculation can be done using SI units (where 1000 bytes is one kB, or binary units where 1024 bytes is one KB. It can also output IEC based units like the kibibyte.

2026-03-27

One of the biggest differences between batch ETL pipelines and real-time pipelines is how you manage data quality. In a batch world, you often have long, multi-step processes that validate, clean, and enrich data before it ever reaches your reports. You can afford those extra passes because the data isn’t needed instantly.

In a real-time system, you don’t have that luxury. Events arrive continuously, and you need to deal with problems on the fly. That means data quality checks have to be fast, lightweight, and built directly into your ingestion or processing stage.

2026-03-26

Whenever we’re talking about an amount of data, it’s important to understand the units that are used. In all the early days of computing, it all seemed pretty simple. We had KB for 1024 bytes, MB for 1024 * 1024 bytes, etc.

The first people I saw messing that up were the hard drive manufacturers. Originally, they followed the standard units that we had been using in computing. But somewhere along the way, they changed how this worked. The vendors decided that if they had 10,000,000 bytes of storage, they would call that a 10MB hard drive, but of course it wasn’t, at least not in how we used to measure them. Some of the vendors even started talking about hard drive megabytes like it was some other new unit. That meant that suddenly a 128MB hard drive (128 * 1024 * 1024 * 1024 or 137,438,953,472 bytes) became a 137MB hard drive.

2026-03-25

Show 3 with Guest Dr Neil Roodyn

2026-03-24