How to Monitor your Azure Data Platform with Fabric
Insights from the Silicon Valley Microsoft Fabric Meetup
Now you can maintain optimal oversight of your Azure data platform with the help of Microsoft Fabric. With numerous monitoring tools and techniques available, finding the right balance is key. How can you achieve the best visibility into the health of your data systems? How can you ensure data quality and detect issues before they impact users? At the August Meetup, Azure expert Angel Abundez demonstrated a quick way to start monitoring Azure Data Factory and Azure Data Lake Storage with Fabric.
In his role as VP, Solutions Architecture at DesignMind, Angel has extensive experience helping enterprise clients envision, plan, and implement cutting edge solutions for their business data. Microsoft Fabric offers actionable strategies for keeping a vigilant eye on Azure Data Factory and Azure Data Lake Storage. Angel explained how these practical tools can enhance the visibility and performance of your data systems.
Understanding the Azure Data Platform
Azure Data Factory is pivotal in managing these data pipelines, initiating transformations, and overseeing system health.
Angel began by breaking down the structure of a standard Azure data platform. Diverse data sources such as APIs, databases, and data files converge in Azure Data Lake Storage.
Databricks is used to transform this data, which is then organized into a medallion architecture consisting of bronze, silver, and gold layers. This organized data is subsequently leveraged by BI teams for robust reporting and analytics.
Azure Data Factory is pivotal in managing these data pipelines, initiating transformations, and overseeing system health. Angel underscored the significance of Azure Data Factory's built-in monitoring features, which enable users to monitor pipeline performance and pinpoint failures.
Challenges in Data Monitoring - "What could go wrong? Everything!"
Angel shared his experiences with the challenges of monitoring complex data systems. He highlighted several potential issues, such as missing data, API failures, unannounced cloud updates, and compute failures. "What could go wrong? Everything!" he said. These challenges necessitate a robust monitoring strategy to ensure data systems operate smoothly and efficiently.
"You know, I think whether it's Amazon, whether it's Google, whether it's Microsoft, I don't care what vendor you use, there are going to be failures for whatever reason. Sometimes vendors will update their technology stacks. They'll migrate data centers, they have outages. I mean, just recently we had a very big outage that affected anybody who was on the Microsoft platform. So things fail all the time, and especially with APIs. Unannounced cloud updates - I love them!"
Developing a Monitoring Strategy using Azure Monitor
To deal with potential failures head on, Angel introduced the concept of a data operations (DataOps) team dedicated to monitoring and resolving data system issues. This team would be responsible for developing monitoring tools, triaging issues, and communicating problems effectively.
One of the key tools is Azure Monitor, which collects logs from various Azure services and provides a centralized hub for monitoring. By leveraging Log Analytics Workspace, users can query logs using Kusto Query Language (KQL) and integrate the data with Power BI for visualization.
Quick Monitoring with Microsoft Fabric
Angel showcased an efficient method for overseeing Azure Data Factory and Azure Data Lake Storage utilizing Microsoft Fabric. By integrating Log Analytics Workspace with Power BI, users can effectively visualize pipeline performance and detect bottlenecks.
Custom visuals, including box-and-whisker plots and heat maps, provide clear insights into pipeline execution times and potential conflicts. Angel further highlighted the critical importance of understanding the parent and child pipeline structure. By utilizing Power BI's path function to simplify these hierarchies, users can focus on the main pipelines, thus enhancing the efficiency of their monitoring efforts.
Enhancing Data Quality with Databricks SQL Alerts
Beyond just tracking pipeline performance, data quality is paramount. Data quality encompasses six crucial dimensions: completeness, consistency, accuracy, timeliness, validity, and uniqueness.
These dimensions can be meticulously monitored using Databricks SQL alerts, which promptly notify users of any data quality issues. This proactive approach allows for swift resolutions, ensuring that problems are addressed well before they affect end users.
Oversee Azure data platforms with Microsoft Fabric's powerful tools
Angel's presentation illuminated the complex art of overseeing Azure data platforms with the powerful tools provided by Microsoft Fabric. Leveraging the functionalities of Azure Monitor, Log Analytics Workspace, and Power BI, you can gain comprehensive insights into your data ecosystems, quickly identify issues, and assure exceptional data quality.
To learn more about monitoring with Microsoft Fabric, you can watch Angel's entire presentation on YouTube: Monitoring your Azure Data Platform with Fabric.
DesignMind is an Azure Data and AI Partner. Our clients often come to us with needs related to AI Strategy and Consulting and Azure AI Development and Implementation.