#Azure #DataFactory #mappingdataflows Training slide deck v005

1. Azure Data Factory
Mapping Data Flows
https://aka.ms/dflinks
MicrosoftTrainingv005

3. Mapping Data Flow is a new feature of Azure Data Factory that allows you to build data transformations
code-free in a visual user interface
• Transform at scale, in
the cloud
• Code-free pipelines​ do NOT
require understanding of
Spark / Scala / Python / Java​
• Serverless scale-out
transformation execution
engine​
• Resilient data transformation
Flows​ built for big data
scenarios with unstructured
data requirements​
• Operationalized with Data
Factory scheduling, control
flow and monitoring​

7. Authoring and designing Mapping Data Flows

11. Mapping Data Flow Transformations

32. Mapping Data Flows Expressions

34. List of columns
being modified
All available
functions, fields,
parameters …
Build expressions
here with full
auto-complete
and syntax
checking
View results of your
expression in the data
preview pane with
live, interactive
results

35. here

48. Operationalizing Mapping Data Flows

53. Continuous Integration and
Continuous Development

55. Mapping Data Flow common scenarios

62. Mapping Data Flows Best Practices

63. https://aka.ms/dfperf

65. https://mssqldude.wordpress.com/2018/12/20/adf-data-flows-self-join/

66. Optimizing Data Flows

73. Data Flow Script

74. https://docs.microsoft.com/en-us/azure/data-factory/data-flow-
script

75. Source projection (1)
Source properties
Unpivot transformation (3)
Aggregate Transformation (2)
Sort (4)
Sink (5)
1 2 3 4 5
• Syntax: input_name transform_type(properties)
~> stream_name

76. 1 2
3
4
5
6
Source projection (1)
Source properties
Distinct Aggregate (3)
Row Count Agg (5)
Select transformation mappings (2)
Select properties (2)
Row Count Agg (4)
Sink transformation (6)
• ~> name_of_transform
• New branch does not require any script
element