Microsoft-Fabric

KQL offers a wide range of specialized functions designed to handle advanced analytical tasks — particularly useful in real-time and large-scale telemetry environments.

Some of the most important belong to three broad groups:

• Anomaly and pattern detection: Functions like series_decompose_anomalies() and series_outliers() identify unusual trends or values within time series data. These are often used in monitoring, fraud detection, or IoT scenarios where unexpected changes may signal issues.
• Time series and multivalue operations: Functions such as make-series and mv-expand let you group data into time bins and expand array-like columns for further analysis. These help with trend visualization, forecasting, and interval-based reporting.
• Geospatial and temporal enrichment: The geo_* family (e.g., geo_distance_2points(), geo_point_to_h3cell()) allows spatial analysis, such as tracking asset locations or mapping activity by region.

2026-09-18

In KQL, the union operator can combine results from multiple tables, even when those tables follow a naming pattern. This is particularly useful in environments where data is split across many similar tables — for example, logs_2024_01, logs_2024_02, and so on.

You can use wildcards with the union operator to simplify these queries. Instead of listing each table individually, you can write:

union withsource=TableName logs_*

This automatically brings together all tables whose names begin with logs_.

2026-09-16

In Fabric, KQL supports querying across multiple databases or even across different clusters.

This feature is especially valuable when data is distributed across teams, regions, or functional domains — for example, separate clusters for telemetry, transactions, and logs.

A cross-database query allows you to access tables in more than one KQL database within the same cluster. This is often used when analytical data and operational data are stored separately but need to be correlated — such as linking user activity logs with transaction histories.

2026-09-14

Partitioning is one of the most effective ways to improve scalability and performance in event-driven systems. It works by dividing a single event stream into multiple independent subsets, or partitions, that can be processed in parallel.

Each partition operates independently, which allows multiple consumers or processing nodes to work simultaneously without interfering with one another. This parallelism increases throughput and helps maintain low latency even as data volumes grow.

The partition key determines how events are distributed among partitions. Common choices include device ID, user ID, or region, depending on the data model and access patterns. The key should be selected carefully:

2026-09-12

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

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

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

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

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

IoT environments typically involve large numbers of devices — sensors, controllers, and machinery — each continuously generating telemetry data. Common examples include temperature, vibration, pressure, voltage, and power consumption readings. These data streams provide valuable insight into device health and operating conditions.

Problem to solve

When a component starts to degrade or operate outside normal ranges, early signs often appear in the telemetry before a full failure occurs.

The challenge is that traditional reporting systems tend to work on batch cycles — for example, generating summaries every hour or day. That delay means early warning signs can be missed, especially in high-frequency data streams where conditions can change in seconds.

2026-08-31