Handle schema drift

Schema drift refers to the inconsistent structure or format of data over time, which can pose significant challenges in the field of data engineering. In the context of Microsoft Azure, schema drift can occur when the schema of data sources or data streams change without any prior notice or synchronization. This can lead to data processing and integration issues, affecting analytics, machine learning, and other data-driven operations. In this article, we will explore ways to handle schema drift in the context of exam Data Engineering on Microsoft Azure.

Understanding Schema Drift

The first step in handling schema drift is to understand the potential sources and types of schema changes. These changes can include the addition or removal of fields, changes in field data types, alterations in field names, or changes in the overall structure of the data. It is important to have a thorough understanding of the data sources and their schemas, as well as the possible changes that may occur.

Using Azure Data Factory for Dynamic Mapping

One way to handle schema drift is by using Azure Data Factory, which provides a set of tools and services for data integration and orchestration. Data Factory allows you to build pipelines that can handle different types of data with varying schemas. By using data flows within Data Factory, you can build data transformation logic that adapts to changes in the schema.

Data Factory supports dynamic mapping, which enables you to handle dynamic schema changes during data ingestion or data transformation processes. Dynamic mapping involves using wildcard characters, expressions, or external metadata to dynamically map the fields of incoming data with the target schema. This ensures that even if the schema changes, the data can still be processed correctly.

Let’s consider an example of dynamic mapping using Data Factory. Suppose you have a data source that provides customer information, including fields such as “CustomerId,” “FirstName,” and “LastName.” However, due to schema drift, the data source starts including an additional field called “Email.” Using Data Factory, you can handle this schema drift by creating a dynamic mapping in your pipeline. Below is an example of how you can achieve this with Data Factory’s mapping data flows:

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In the mapping data flow, you can define the source and target schemas. When configuring the mapping, you can use dynamic expressions to handle the schema drift. For example, you can use the “Coalesce” function in a derived column transformation to handle the absence of the “Email” field in the original schema, like this:

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This way, the pipeline can handle both the original schema and the updated schema with the additional “Email” field.

Using Azure Databricks for Schema Enforcement

Another approach to handling schema drift is to use Azure Databricks, an Apache Spark-based analytics platform that can handle large-scale data processing and machine learning workloads. Databricks provides robust capabilities to handle schema drift through its Spark programming model.

With Databricks, you can define and apply schema enforcement rules to ensure that incoming data adheres to a specific schema. Schema enforcement allows you to validate and reject data based on predefined schema rules, mitigating the impact of schema drift. You can define the schema using the Databricks DataFrame API and apply it during the data ingestion or transformation process.

Here’s an example of using Databricks to handle schema drift through schema enforcement:

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In the example, you define a schema using the Databricks DataFrame API and apply it to a streaming DataFrame. By setting the `failFast` option to `true`, Databricks will reject any incoming data that does not conform to the defined schema, thus handling schema drift effectively.

Conclusion

Schema drift can be a common challenge in data engineering on Microsoft Azure. By leveraging tools and services such as Azure Data Factory and Azure Databricks, you can handle schema drift through dynamic mapping and schema enforcement techniques. These approaches enable you to adapt to changing schemas, ensuring the integrity and usability of your data for analytics, machine learning, and other data-driven operations.