Upsert data

The article will provide an overview of upsert data in the context of Data Engineering on Microsoft Azure. It will discuss what upsert means, its importance in data engineering, and how to perform upsert operations using relevant Azure services.

What is Upsert?

Upserting data refers to the combined operation of updating existing data and inserting new data into a target storage system. This operation is particularly useful in scenarios where you need to synchronize data from multiple sources or maintain a consistent dataset within a storage system. In data engineering, upsert operations are often performed on large datasets to ensure data accuracy and integrity.

Azure Services for Upsert Operations

Microsoft Azure provides several services that can be leveraged to implement upsert functionality in data engineering pipelines. Let’s explore some common approaches and tools for achieving upsert operations in Azure.

Azure Data Factory (ADF): ADF is a fully managed data integration service that allows you to orchestrate and automate the movement and transformation of data. ADF provides multiple ways to upsert data into different target storage systems. One approach is to use the “Copy data” activity along with the “Upsert” as the write behavior. By configuring the source, destination, and mapping settings, ADF can efficiently perform upsert operations.

Here’s an example of how to configure an “Upsert” operation using ADF with Azure SQL Database as the destination:



Upsert




Azure Databricks: Azure Databricks is a powerful tool for data engineering and analytics on Azure. It provides a collaborative and interactive workspace for processing and manipulating data. In Databricks, you can perform upsert operations using the Delta Lake library, an open-source data lake technology optimized for handling big data workloads. Delta Lake supports ACID (Atomicity, Consistency, Isolation, Durability) operations, including upserts.

To upsert data using Delta Lake in Azure Databricks, you can use the merge operation. Here’s an example using Scala:

import io.delta.tables._

deltaTable.as("existingData")
.merge(
newData.as("newData"),
"existingData.key = newData.key"
)
.whenMatched()
.updateAll()
.whenNotMatched()
.insertAll()
.execute()

Azure Cosmos DB: Azure Cosmos DB is a globally distributed, multi-model database service that supports document, key-value, graph, and columnar data models. It also provides upsert functionality through its APIs. In Cosmos DB, you can use the CreateDocumentAsync method with the Upsert option to perform upsert operations in your code.

Here’s an example using the .NET SDK with C#:

await client.UpsertDocumentAsync(collectionLink, document);

These are just a few examples of how to perform upsert operations in Data Engineering on Microsoft Azure. Depending on your specific use case and data storage requirements, there are many more services and techniques available in the Azure ecosystem to accomplish upsert functionality.

Remember to consult the official Microsoft Azure documentation for detailed guidance on using specific services and libraries, as well as to explore further examples and best practices for upserting data in Azure.

Happy upserting!