With Azure Synapse, you can validate data.

With Azure Synapse, you can validate data .

 

Overview

In the realm of AI and machine learning, data quality is critical to ensuring that our models and algorithms work properly. We can do comprehensive data validation at a massive scale for your data science applications by using the power of Spark on Azure Synapse.

What is Azure Synapse, and how does it work?

Azure Synapse is a Data Analytics Service that offers end-to-end data processing within Azure. The Azure Synapse Studio is a tool for creating and implementing data extraction, transformation, and loading processes in your Azure environment. All of these procedures are based on cloud architecture that is scalable and can handle massive quantities of data if necessary. We'll use Apache Spark as the processing engine for data validation in Azure Synapse.

Pre-requisites

Without going into too much detail, the essential requirements for executing this code are an Azure Synapse Workspace and a data set loaded to Azure Storage on which you want to conduct certain validations. The approach demonstrated here may be used to do these sorts of data validations in your own use case.

I've uploaded a data set of hard disc sensor data (see below) to an Azure Storage account and linked the storage account in Synapse for demonstration purposes.

Let's get this validation process started.

You may start by computing some high-level statistics for easy validations after importing the data.

The describe() function computes simple statistics (mean, standard deviation, min, max) that may be compared across data sets to ensure that values are within an acceptable range. This is a data function that may be applied to any data. You may store these data sets to your data lake for use in downstream operations by using the following commands:

df desc.write.parquet("[path]\[file].parquet")

However, by utilising spark queries to further verify the data, we can make things a lot more complicated.

What am I going to do with this in my workflow?

Returning to the high-level architectural diagram shown at the start of this article. This approach may be used to verify if the raw files are qualified to be in the data lake during data import. We can run row-level tests quickly and effectively using Synapse Spark, and then send the results back into the data lake. The validation data may then be accessed by downstream processes such as Machine Learning Models and/or Business Applications to assess whether or not to use the raw data without having to re-validate it. You can execute sophisticated validations on very big data sources with minimum codin using Azure Synapse and Spark.