Data Flow In Azure Data Factory
What is Data Flow In Azure Data Factory?
Data Flow is a fundamental part of Azure Data Factory that allows you to cleanse, enrich and transform data that you want to move between your cloud and on-premises environments. Data Flows offer the ability to define reusable data transformation logic that can be applied at multiple points in a pipeline or across all your pipelines. Data Flows also make it easy to set up advanced functionality like scripting, access control, authentication, monitoring, etc.
In Azure Data Factory, data flow is the core construct for defining jobs. An Azure Data Factory pipeline is a sequence of data movement activities that transform rows from one or more sources into rows in one or more targets. Each activity affects the data; this impact on data is called transformation. Some transformations update the schema of the output table. Other transformations copy or move data to another location without changing the schema.
The data flow lets you orchestrate data movement, transformation, and compliance tasks. Data flows can be chained together to transform data in parallel as it moves toward its destination. The following topics are introduced in this video that illustrates the data flow in Azure Data Factory:
It is a cloud data integration and data transformation service. Data Factory ingests and transforms data from any source and any format, applies business rules to the data, and stores the transformed data in any sink.
Types of Data Flow in Azure Data Factory
There are two types of data flow in Azure Data Factory:
Azure Machine Learning to Hive data flow: This type of data flow is used to transform your Azure ML model into a SQL statement that can be run on a hive server. You can use this type of data flow when you want to integrate your machine-learning models with existing systems or databases.
Azure Machine Learning to Spark data flow: This type of data flow is used to transform your Azure ML model into a Scala code snippet that can be run on a spark server. You can use this type of data flow when you want to integrate your machine-learning models with existing systems or databases.
Data Flow Transformations in Azure Data Factory
Azure Data Factory supports a variety of transformations that you can perform on data as it flows between pipelines in your data factory.
SPLIT: This transformation splits the input data into multiple output files based on a pattern that you specify. You can use this transformation to split your data into multiple files based on a specific pattern.
For example, you may want to split your input data into one file per customer, or one file per order ID.
EXISTS: This transformation checks whether a file exists in the specified folder and returns either a true or false value. For example, you may want to use this transformation to check if files exist in a particular directory before trying to process them.
UNION: This transformation combines multiple files into a single output file. You can use this transformation to combine multiple files into a single output file. For example, you may want to combine all of the sales orders for a particular customer into one file.
LOOKUP: This transformation looks up a value in a file and then replaces the reference with the value that was found. For example, you may want to use this transformation to look up contact information for each customer or supplier and replace those references with actual contact details.
DERIVED COLUMN: This transformation is used to create a new column in a data flow that contains values calculated using expressions. The expression can be based on columns in the data flow or on other external sources, such as databases and web services.
SELECT: This transformation is used to create a new data flow that contains only the rows you specify. You can use this transformation to filter out unwanted rows, such as those with missing values or incorrect data types.
AGGREGATE: This transformation is used to create a new column in the data flow that contains aggregated values from other columns. The aggregation can be based on row counts, averages, minimums, or maximums. You can also use this transformation to create calculations that are applied across all rows of a dataset.
SURROGATE KEY: This transformation is used to create a surrogate key, which is a unique identifier that can be used to identify each row in a dataset. The surrogate key can help you join datasets together and make the data more manageable.
PIVOT: This transformation is used to rearrange the data in a dataset. You can pivot the data by using either columns or rows; you can also create multiple pivots on one dataset.
UNPIVOT: This transformation is used to rearrange the data in a dataset. You can unpivot the data by using either columns or rows; you can also create multiple pivots on one dataset.
How to Create Data Flow In Azure Data Factory?
Step 1: Mapping Data Flow – The first step is to create a simple data flow with one source, one destination, and one activity.
Step 2: Create a DAG – The next step is to create a Directed Acyclic Graph (DAG), which is a set of activities that are connected in order.
Step 4: Configure the DAG – The last step is to configure the DAG. You can do this by either clicking on each activity in order or selecting all of them at once and then clicking on Edit DAG.
Step 3: Create a Pipeline – To create a pipeline, you have to add one or more activities in the DAG and connect them.
Step 4: Configure the Pipeline – The last step is to configure the pipeline. You can do this by either clicking on each activity in order or selecting all of them at once and then clicking on Edit Pipeline.
Step 5: Save and Deploy – The last step is to save and deploy your pipeline. You can do this by clicking on the Save button at the top right corner of the page.
What is a Power Query?
Power Query is a powerful tool that allows you to explore data, discover new relationships, and build custom connections with just a few clicks.
With Power Query, you can import a wide range of data sources into Excel and build connections between them. You can also use the tool to create a custom dataset from scratch. The data is stored in the form of tables and it’s easy to manipulate this information using various functions.
How to Connect Power Query?
- Open Power Query and click on Get Data on the left side of the window.
- Select any data source from the list, such as an Excel workbook or a website that allows you to query its data using an API (Application Programming Interface).
- Once the data is imported, it will appear as a table in Power Query. You can use this information to build your dataset using the tools on the left side of the window.
- You can then use the data to create a custom dataset, which can be used in other tools such as Power BI or Tableau.
Advantages of Data Flow in Azure Data Factory
Disadvantages of Data Flow in Azure Data Factory
The main disadvantage of Data Flow in Azure Data Factory is its inability to deal with concurrency and parallelism. The designers have not been able to give a better solution for this problem so far.
The Data Factory service allows you to stage data in various formats, manage the transformations and connections between different systems, and then process the data through a pipeline.
If you’re looking for a tool that makes it easy to create data pipelines in Azure Data Factory, then Data Flow is the best option. It offers a visual interface that allows you to create and manage data pipelines without writing any code.
Azure Data Factory provides the ability to perform high-level transformations on multiple datasets while maintaining the fidelity of their source. You can implement Data Flows with a wide range of tools and technologies, including REST APIs, Azure Blob Store, and File Share, Amazon S3, and Redshift.
However, if you’re looking for a tool that makes it easy to create data pipelines in Azure Data Factory, then Data Flow is the best option. It offers a visual interface that allows you to create and manage data pipelines without writing any code.
In summary, Data Flow is a powerful feature that provides a robust integration capability to Azure Data Factory. These pipelines can be designed to run on a schedule or triggered by an event, but also dynamically updated when changes are made to the source data.