Fabric or ADX? Key Differences and Use Case Guidance

[MMacarie]

Hi everyone,

I'm trying to better understand the differences between Microsoft Fabric and Azure Data Explorer (ADX), especially in terms of architecture, performance, pricing, and typical use cases. I’ve read some documentation, but I’d love to hear from experienced users or Microsoft engineers who’ve used them in production environments.

Here’s some context:

• Data Type & Volume: I'm working with high-volume tabular data and telemetry with real-time or near real-time processing requirements.

• Latency Requirements: I handle a mix of batch analytics for structured data and real-time querying for streaming telemetry/log data.

• Cost Sensitivity: I have a limited budget and need cost-effective ingestion/querying.

• Throughput Issue: The current analytics system can handle a maximum of 20,000 messages per second which is near saturation.

What I’d like to know:

1. In which scenarios is ADX a better fit over Fabric, and vice versa?

2. What are the differences in performance and scalability for large datasets?

3. Are there any major cost considerations I should be aware of?

4. Which one is better for real-time analytics, data modeling, or BI dashboards?

5. Details on Python support (e.g., notebook use, UDFs, inline Python, SDKs, runtime limits).

Any guidance, decision frameworks, or real-world experiences would be greatly appreciated!

[FerencKovacs]

Another important aspect is observability. Fabric has this PBI dashboard or matrix app to see what "jobs" consumed capacity. If you are running mostly the Real Time DB's for continuous ingestion, daily update policies for aggregation, occasional PBI KQL Direct Query report - that jobs type view might be less useful.

In ADX, there are ready made dashboards showing ingestion latency, table size, hot cache occupation percentage, cluster query execution utilisation % per user. I am not sure if Fabric provides anything similar.

It was mentioned Fabric potentially being more expensive in a comment, although I do not understand why. The ADX cluster VM SKU kind of maps onto the Fabric SKU's: dual-core/quad-core etc. Although in ADX you can choose the much preferred (by me) large SSD SKU for a large hot cache over CPU optimized SKU. I am not sure what VM type is used in Fabric under the hood. In the pricing calculator "OneLake cache" is priced differently and it is stated it is specifically for Kusto hot cache. Documentation states 1 Fabric CU is equal to 2 Spark vCores (double when blasting), but we do not know how many Kusto vCores (management node + execution nodes).

In ADX table extents are stored in zipped internal blobs. In Fabric I don't really know, they might be hidden files with standard OneLake pricing.

I also found the MultiJson ingestion mapping to work slightly differently (IoTHub), when the message body stream is not an object {...} but a json array [...]. I think it was designed for JSONL but "tolerates" a few "malformed" body steams.

I have not tried the python plugin in Fabric, but did try to activate %kqlmagic in Fabric notebook since it does not support KQL (yet?) in the UI. This operation crashed my F2 CU which was down for a week or more, then it came back. This might work on a larger SKU's.

In terms of version control, they are equally bad. You'd want to store in git the policies, functions, table schemas ideally and keep everything under tight control especially in production. Ideally... There could be some OSS tooling around but I have not found any.

Hopefully the community can fill the gaps, my own experience so far is inconclusive.

[v-lgarikapat]

Hi @MMacarie ,

Thanks for reaching out to the Microsoft fabric community forum.

@burakkaragoz , 

Thanks for your prompt response

• Fabric Real-Time Hub: While Fabric integrates with Azure Data Explorer (ADX) for real-time intelligence, the Kusto Python plugin is not yet fully supported in the same way as ADX. Fabric’s Eventhouse is built on ADX technology, but Python execution within Fabric’s Real-Time Hub may have different constraints.

• ADX: ADX natively supports Python UDFs, inline execution, and SDKs for advanced analytics. You can enable the Kusto Python plugin directly in ADX for machine learning, anomaly detection, and custom computations.

I have included some learning documents here that may help you understand and resolve the issue

Python plugin packages - Kusto | Microsoft Learn

Differences between Real-Time Intelligence and comparable Azure solutions - Microsoft Fabric | Micro...

Enable Python plugin in Real-Time Intelligence - Microsoft Fabric | Microsoft Learn

If this post helped resolve your issue, please consider the Accepted Solution. This not only acknowledges the support provided but also helps other community members find relevant solutions more easily.

We appreciate your engagement and thank you for being an active part of the community.

Best regards,
LakshmiNarayana.

[v-lgarikapat]

Hi @MMacarie ,

I wanted to check if you had the opportunity to review the information provided. Please feel free to contact us if you have any further questions. If my response has addressed your query, please Accept as Solution so other members can easily find it.

Best Regards,

Lakshmi Narayana

[MMacarie]

Hello, 
Thank you for your response. I reviewed what you sent, but regarding the other aspects of Fabric vs ADX, would you be able to provide some information as well? I am still not convinced which one to choose, how are they related or what parts of one are found in the other. 
I want to store data in  KQL database and while Fabric has a lot of functionalities I am not sure if I should go with it or if I should choose ADX. Which one would be a better solution for the long term regarding the details in my initial message?

[v-lgarikapat · Accepted solution]

The differences between Microsoft Fabric and Azure Data Explorer (ADX) especially since we’re focused on KQL databases and thinking long-term.

To start with: KQL databases in Fabric are powered by the same engine as ADX. So, from a query perspective (Kusto), they behave very similarly. But where they diverge is in purpose and integration.

If your focus is mainly on real-time data ingestion, high-throughput telemetry, or log analytics then ADX is still the more specialized tool. It's built for scale and performance in those areas. Also, it currently offers broader support for advanced features like Python UDFs, which might be important depending on your stack.

On the flip side, if we’re looking for a more holistic data platform something that lets us ingest, store, transform, and visualize data all in one place Fabric has a lot to offer. It integrates tightly with Power BI, Lakehouse, and other Microsoft 365 tools, which could simplify our data architecture and improve collaboration across teams.

Microsoft has published some great official docs that helped clarify this:

• Fabric vs Azure Solutions – Real-Time Intelligence comparison
• Fabric documentation hub
• ADX documentation

Note: if we're prioritizing raw real-time analytics and data ingestion performance, I’d lean toward ADX. But if we want an end-to-end platform that's more versatile and future-looking, Fabric could be the better long-term bet.

Best,Regards,
Lakshmi Narayana.

[burakkaragoz]

Hi @MMacarie ,

Great question! Here’s a direct, scenario-driven comparison based on your needs:

---

1. In which scenarios is ADX a better fit over Fabric, and vice versa?

ADX is better if:

• You need ultra-fast ingestion and analytics for high-volume, time-series, telemetry, or log data (IoT, app logs, monitoring).
• Real-time or near real-time querying is a must (20,000+ messages/sec is well within ADX’s sweet spot).
• Your primary focus is on low-latency, ad hoc analytics, anomaly detection, or advanced time-series functions using KQL.
• Cost efficiency is critical for continuous, high-throughput ingestion.

Fabric is better if:

• You want end-to-end analytics (ETL, warehousing, semantic modeling, and BI) in a single SaaS platform.
• Your workloads are a mix of batch analytics, data integration, and business intelligence reporting.
• You need deep integration with Power BI, Lakehouse, and dataflows.
• Structured/tabular data is the main use case and real-time needs are moderate.

---

2. Performance and Scalability for Large Datasets

• ADX: Built for massive scale and speed. Handles TBs–PBs of time-series or log data with sub-second query latencies. Scales horizontally and can ingest tens of thousands of events per second.
• Fabric: Excellent for structured and batch analytics, but for ultra-high velocity telemetry/log ingestion, ADX is typically more performant and cost-effective.

---

3. Major Cost Considerations

• ADX: Pricing is based on ingestion, retention, and query compute. Highly cost-effective for high-throughput streaming data thanks to storage/compute separation and compression.
• Fabric: Capacity-based (per vCore) SaaS pricing. Great for unified analytics, but high-frequency streaming can become expensive compared to ADX.

---

4. Real-Time Analytics, Data Modeling, BI Dashboards

• ADX: Best for real-time analytics, streaming data, and ad hoc analysis.
• Fabric: Best for data modeling (semantic models), BI dashboards, and integrating multiple data sources.
• Hybrid: Many organizations ingest and process real-time data in ADX, then move curated/aggregated results into Fabric for downstream analytics and BI.

---

5. Python Support

• ADX:
  • Native support for inline Python, Jupyter notebooks, UDFs, and Python SDKs.
  • Great for embedded analytics, ML, and custom logic at query time (note resource/runtime limits for UDFs).
• Fabric:
  • Supports Python via Notebooks/Data Science experiences.
  • Excellent for prep, ML, and transformation, but for real-time inline analytics on streaming data, ADX’s Python support is more advanced.

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Summary Table

Feature ADX Fabric

Real-time Ingestion	✔✔✔ (Purpose-built)	✔ (Good for batch)
Query Language	KQL (time-series, log analytics)	SQL, DAX, Power Query
End-to-End Analytics	❌	✔✔✔
BI/Reporting Integration	Power BI connector	Deep, native integration
Cost for Streaming	✔✔✔ (Optimized, lower)	✔ (Can be higher)
Python Support	Native UDFs, Jupyter, SDK	Notebooks, ML, scripts

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Decision tip:

• For real-time analytics and telemetry at scale, start with ADX.
• For unified analytics, BI, and data modeling, Fabric is the go-to.
• For hybrid needs, use both: ingest/process in ADX, then move data to Fabric for BI.

If you have a specific scenario or need architectural advice, I’m happy to dive deeper!

[MMacarie]

Thanks for the detailed comparison!

I would like a bit more clarity regarding the Python part, specifically the UDF.

When you referred to "scrips", did you mean I can enable the Kusto python plugin in Fabric Real-Time Hub just like in ADX? If yes, how can I use the plugin? And are there any differences between the two, such as supported versions or cost implications?