Our free SDU Tools for developers and DBAs, now includes a very large number of tools, with procedures, functions, and views. Version 27 adds the first set of views and functions for working with Nepali dates. These are useful in Nepal and in a number of Buddhist-related areas.
The first tool added is a view called NepaliMonths. It returns the Nepali names for months. You can see it in the main image above.
2026-04-06
When we talk about aggregation, we’re really talking about taking huge volumes of raw events and rolling them up into something that’s usable, measurable, and actionable. A raw stream might be thousands of individual events per second — each transaction, each sensor ping, each click. By themselves, they’re useful for tracing details, but they don’t tell the bigger story.
Aggregation lets us step back and say: Instead of looking at every single reading, let’s look at the total, the average, or the maximum over a period of time. Common aggregation functions are ones you already know — COUNT, SUM, AVG, MIN, MAX.
2026-04-05
Our free SDU Tools for developers and DBAs, now includes a very large number of tools, with procedures, functions, and views. The TokenSetSimilarity function calculates token set similarity for two strings.
It answers the question: Do these two strings contain mostly the same words, even if the order, spacing, or repetition differs?
It is useful where word order varies, or extra or missing words are common. It can also help where character-level typos are less important than the presence of words.
2026-04-04
When working with real-time data, it’s easy for your streams to become cluttered — especially as events come from multiple sources with different structures. That’s where the Manage Fields option becomes essential.
It gives you control over the shape of your data stream. You can choose which fields to keep and drop those you don’t need, which helps reduce noise and improves performance. For example, you might remove diagnostic fields or metadata columns that aren’t needed for your analytics.
2026-04-03
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
