Data types that we might encounter in Azure Data Factory pipelines are: String, Int, Float, Bool, Array, Object, SecureString. One thing to mention is the available data types, this will be useful in the following sections. The documentation about expressions and functions of the pipeline expression language can be found here. Examples of activities include Copy, Azure Function, If Condition, and many more. The most common use is to configure different pipeline activities and work with parameters/variables. Pipeline expression language, or Data Factory expression language, is used within ADF pipelines. In this post, I’ll use word syntax as a synonym for expression language. When talking about Azure Data Factory expression language, it is important to understand that there are actually two types of expressions used in ADF: Pipeline and Data Flow expression languages. Expression Languages: Pipeline vs Data Flow
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The list is definitely not exhaustive, but should give a good understanding of how to use these functions in practice.
This section covers important aspects such as relevant data types, date formats, time zones, and functions that can be used to manipulate dates and timestamps.įinally, we wrap up the conversation with a number of practical use cases which you might encounter while working with date and time in Azure Data Factory. Second, we zoom in and focus on date and time in the context of Azure Data Factory. It might be confusing but ADF pipelines and data flows use different expression languages with different data types and functions.
#FLOW CONVERT STRING TO NUMBER CODE#
There’s no need to write code since everything is managed through UI.įirst, we start with a discussion about what is usually meant when people talk about data factory expression language. It integrates with many Azure and third-party services via built-in connectors. Appendix: List Of Data Flow’s TimeZone IDsĪzure Data Factory, also known as ADF, is a cloud service that provides capability to perform data integration and data transformation tasks.Convert Date Or Timestamp To String + Custom Formatting.Examples Of Working With Date And Time In Azure Data Factory.Dates And Timestamps In Azure Data Factory.Expression Languages: Pipeline vs Data Flow.All explanations are accompanied with code examples to make things easier to understand.
In this post, we will cover details of working with date and time in Azure Data Factory and also present solutions to some common problems you might encounter. As with any platform, Azure Data Factory provides support for working with dates based on its own rules and limitations. Unsurprisingly, dealing with date and time comes with its own set of challenges when developing pipelines and transformations using Azure Data Factory.ĭates/timestamps often need to be parsed, formatted, compared, or converted between time zones while working with data. Date and time information is a vital piece of any dataset, for example, when a purchase was made, when is the last time user logged in, how long a request took.
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