DP-700: optimize Kusto Query Language (KQL) queries in Fabric

João Barros 26 de August de 2026 5 min read

In this lesson I focus on a practical skill for the DP-700: optimizing queries with Kusto Query Language (KQL) in Fabric environments. Knowing how to optimize KQL is crucial for fast responses, cost reduction, and a good experience for report, dashboard, and ingestion pipeline users. Inefficient queries can turn a simple analysis into tasks that take minutes or hours and generate high I/O and compute costs.

What you need to know

Kusto Query Language (KQL) is the language used to query tables in several Fabric components (for example, Explorer and some data pools). Optimizing queries means reducing execution time, CPU/memory usage, and disk/network I/O. The key concepts you should master are:

Early filtering: apply where/filters as early as possible to reduce the number of rows processed — ideally before expensive operations like joins and aggregations.
Project: select only the necessary columns to reduce data transfer and row width; reducing from tens of columns to 3–5 can cut data volume by 10x or more.
Summarize and bin(): plan aggregations; use bin() to group timestamps which reduces complexity and allows the engine to optimize temporal aggregations.
Efficient join: prefer hash joins when the engine supports them; reduce each side of the join (filter and project) and avoid cross joins (cartesian joins).
Materialize results: use materialized views, caching, or persist intermediate results when a query runs frequently (for example, dashboards refreshed every 5 minutes).

Conceptual example: imagine a Telemetry table with 100 million rows (approx. 100 GB) and 50 columns. A query that reads all columns just to compute an average over 7 days can read 100 GB; applying where timestamp and project to 3 columns can reduce the volume read to ~2–5 GB and drop the time from seconds/minutes to a few seconds.

How it works in practice

Below I show step-by-step transformations, with real KQL snippets and explanations of why each change improves performance. I will assume a typical example: Telemetry with millions of rows per day.

// Initial query (inefficient):
Telemetry
| where Timestamp >= ago(7d)
| summarize AvgValue = avg(Value) by DeviceId, bin(Timestamp, 1h)
| order by Timestamp desc

Problems: although the where is present, if the table has many columns and the engine is forced to read entire blocks, it may still transfer a lot of information. Also, ordering unnecessarily can force a global sort step.

// Improved: filter and project early, reduce data before summarize
Telemetry
| where Timestamp >= ago(7d)
| project Timestamp, DeviceId, Value
| summarize AvgValue = avg(Value) by DeviceId, bin(Timestamp, 1h)
| order by Timestamp desc

Explanation: project reduces row width — for example, from 1 KB to 80 B per row — decreasing the amount of data moved between nodes. Applying bin() in the grouping allows the engine to group by fixed windows, which typically speeds temporal aggregations and reduces group cardinality.

// If only a few devices are relevant, filter first by DeviceId
let devices = datatable(DeviceId:string)["devA","devB","devC"];  // example with 3 devices
Telemetry
| where Timestamp >= ago(7d) and DeviceId in (devices)
| project Timestamp, DeviceId, Value
| summarize AvgValue = avg(Value) by DeviceId, bin(Timestamp, 1h)

Using a small set of DeviceId reduces volume considerably: if the table has 50M rows per week and only 0.5% are for those devices, you go from 50M to 250k rows — a 200x reduction.

For joins:

// Inefficient join: joining large tables without pre-filtering
Telemetry
| where Timestamp >= ago(7d)
| join kind=inner Devices on DeviceId

// Better: reduce each side before the join
let Tsmall = Telemetry | where Timestamp >= ago(7d) | project DeviceId, Timestamp, Value;
let Dsmall = Devices | project DeviceId, Region;
Tsmall
| join kind=inner (Dsmall) on DeviceId

Pre-filtering and projecting reduces the cost of the hash join and the data movement. In practical scenarios, reducing each table to 5–10% of the original size can transform a join that consumed 30 GB of memory into an operation that comfortably runs within 2–3 GB.

Common mistakes

Keeping SELECT * (or equivalent) and not projecting unnecessary columns: this increases I/O and latency.
Applying filters late in the pipeline: placing where after joins/aggregations forces the engine to process much larger sets.
Ignoring cardinality before joins/aggregations: joining tables with high cardinality without reducing them first can cause memory spikes and out-of-resource failures.
Sorting when not necessary: order by can force shuffle/disk steps; avoid it in intermediate pipelines unless you truly need ordered data.

How to practice

To practice, use Fabric training environments and public datasets (for example, telemetry, logs, or GitHub datasets). Try measuring times before/after: for example, run an initial query and record execution time and bytes read; apply project/where early and compare. Microsoft provides an OFFICIAL free Practice Assessment for the DP-700 and a free study guide with topics and resources — consult those official materials to guide your practice. Avoid copying exam questions; the goal is to master the skills to be effective in real work.

In summary

Filter early and project only the necessary columns to significantly reduce the cost of KQL queries — often 10x–200x in processed data volume.
Plan joins: pre-filter and reduce cardinality before joining large tables to avoid memory spikes.
Use bin() and summarize appropriately for temporal aggregations; consider materializing views for repeated queries and frequently refreshed dashboards.
Practice in the Fabric environment, measure before/after, and use the official Microsoft Practice Assessment and study guide to prepare responsibly and effectively.