Event-Driven Data Ingestion with Microsoft Fabric & dltHub — No More Scheduling Hassles!

Data teams often face the challenge of not being able to directly connect to data sources, forcing raw data to be pushed to landing zones in formats like CSV or Parquet.

In these scenarios, Microsoft Fabric & dltHub in a Python environment provide an effective solution for event-driven ingestion, ensuring data is processed as soon as it arrives — no need for scheduled jobs.

Key Benefits:

1) No Fixed Schedules — Jobs are triggered automatically when new files arrive.

2) SLA Compliance — Ingest data as it lands, removing unnecessary runs.

3) No Spark Needed — A huge opportunity for handling moderate-sized data efficiently in a Python environment.

4) Simplified Complexity — dltHub takes care of many intricate processes, making ingestion smoother.

Check out this architecture to see how you can implement this in Microsoft Fabric with dltHub — without Spark!

How are you tackling event-based ingestion in your pipelines? I’d love to hear your insights!