@Yohanna de Oliveira Cavalcanti
Thanks for the question and using MS Q&A platform
A combination of Azure Data Factory, Azure Event Hubs, and Azure Databricks can efficiently meet these requirements.
- Azure Data Factory:
- Configure pipeline runs to generate logs in a specific format.
- Trigger an event to Azure Event Hubs on pipeline completion.
- Azure Event Hubs:
- Capture events from Azure Data Factory.
- Provide a scalable, fault-tolerant, and high-throughput platform for ingesting data.
- Azure Databricks:
- Consume events from Event Hubs using a Databricks notebook or job.
- Transform and enrich data according to the pre-established schema.
- Insert data into the Delta Table.
key Considerations
Event Hubs:
- Use a capture endpoint to store events for replay or later consumption.
- Configure retention policy to balance cost and data availability.
- Implement error handling and retry logic for reliable data ingestion.
Databricks:
- Create a Delta Table with the required schema.
- Develop a Spark job or notebook to consume events from Event Hubs.
- Use structured streaming to handle continuous data ingestion.
- Implement deduplication logic based on a unique identifier in the log data.
- Leverage Delta Lake's ACID properties for data consistency and reliability.
Triggering:
- Utilize Azure Data Factory's event-based triggers to initiate pipeline runs.
- Consider using Azure Logic Apps for more complex orchestration scenarios.
Here’s a more detailed breakdown:
No Duplicate Logs: To ensure no duplicate logs, you can make use of the RunId
of the ADF pipeline run. This RunId
is unique for each pipeline run and can be used as a primary key in your Delta table.
Pre-Established Schema: The logs from ADF will follow a pre-defined schema. You can define the schema for the Delta table to match this log schema.
Trigger-Based Ingestion: ADF can be configured to send events to Azure Event Hubs whenever a pipeline run occurs. This event can be used to trigger a Databricks job that will ingest the log into a Delta table.
Mandatory Storage in Databricks: The Databricks job will ingest the log data into a Delta table. Delta tables in Databricks provide ACID transactions, scalable metadata handling, and unifies streaming and batch data processing.
Cost Optimization:
- Adjust Event Hubs throughput units based on expected event volume.
- Optimize Databricks cluster size and auto-scaling settings.
- Explore cost-effective storage options for Delta Lake data.
Hope this helps. Do let us know if you any further queries.
If this answers your query, do click Accept Answer
and Yes
for was this answer helpful. And, if you have any further query do let us know.