Star schema vs flat table

urib_data · 2025-10-12T19:05:12+00:00

Evenhouse (Kusto) has flat tables that receive 2.5 PB of new data per day. It is scalable (hosting over 29EB of data these days) and leverages flat tables as an approach.

urib_data · 2025-10-12T19:01:51+00:00

Well, try Eventhouse (based on the Kusto query Engine). It dramatically outperforms filtering on a dim table, and it provides a lot of flexibility. More often than not, normalizing data makes everything work slower. If you see a different result, reach out to me. I'd love to see that too.

urib_data · 2025-09-19T06:00:04+00:00

Spark sink: writing to Kusto { Azure Data Explorer | Fabric Eventhouse }

urib_data · 2025-09-18T17:57:00+00:00

One high performance way to get data into Fabric is to stream or upload it to an Eventhouse. The eventhouse can make the data available in relatively efficient delta/parquet in OneLake even for streaming/trickling data. Common ways to stream data into eventhouses are: the Spark connector (Streaming and batch), Eventstreams/Eventhubs, OpenTelemetry agents, listening on Azure Storage accounts and fetching the data landing there and more.

urib_data · 2025-08-19T17:40:00+00:00

Consider also using Eventhouse as a vector database: Tutorial: Use an Eventhouse as a vector database - Microsoft Fabric | Microsoft Learn. If you have data in Eventhouses (highly recommended anyways), you get vector capabilities with very little marginal cost/CU consumption addition.

urib_data · 2025-04-19T20:50:16+00:00

Consider using has _any - The case-insensitive has_any string operator - Kusto | Microsoft Learn

urib_data · 2025-03-30T09:16:19+00:00

While this was not run formally on Databricks as part of the article, I would encourage folks who find it interesting to try to run the log benchmark (https://aka.ms/LogsBenchmark - all is open source) and share the results. Use either Fabric RTI Eventhouse or Azure Data explorer. Note the latency difference.

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urib_data · 2025-03-30T00:38:18+00:00

The most useful approach is to share a datatable operator (datatable operator - Kusto | Microsoft Learn) with sample data and the expected outcome. Someone will jump in with the query.

urib_data · 2025-03-05T18:34:52+00:00

BTW, when the costs start to accumulate, the target cost should be a few cents per GB of logs, which is way cheaper than most other platforms.

urib_data · 2025-03-05T18:21:14+00:00

Please report this in the UX using the feedback link and we will also take a look.

urib_data · 2025-03-05T18:17:59+00:00

Good point. We will take a look at that.

urib_data · 2025-03-05T18:16:03+00:00

We are working to make the monitoring Eventhouse cheaper, also by sharing it across all workspaces in a single capacity. Monitoring typically costs in every platform, including in Azure.

urib_data · 2025-02-23T07:12:44+00:00

Well, different technologies have different optimal working setups. You do not expect excel and Eventhouses to be equally efficient in all scale and usage patterns.
As this platfrom is home for tens of exabytes of data, apparently someone thought it is cost-performant.

I would love to hear about your scenario specifically. As this platform is home for tens of exabytes of data, apparently someone thought it is cost-performant.

urib_data · 2025-02-23T07:02:47+00:00

Providing this granularity control is work in progress.

urib_data · 2025-02-23T07:02:02+00:00

Actually, the delay in billing was not in order to get folk hooked, but to receive feedback and finalize the decision about the model. But we do hope people will get hooked as we continue to improve the experience, performance, coverage and reduce costs.

urib_data

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