Azure Data Factory: Generate Pipeline from the new Template Gallery

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Collection of all ADF Mapping Data Flow videos

Data Saturday Croatia

ADF Mapping Flow

Did you use the Dataflow preview functionality in Azure Data Factory? This has recently be renamed to Mapping Data Flows.
 

All video’s which the ADF team has created, are collected.
Start Here:

ADF Data Flow: Overview
ADF Data Flow: Data Flow Scenarios
ADF Data Flows: Getting started
Getting started with ADF Data Flows from Altius
Getting started with Mapping Data Flows by Adam from Azure 4 Everyone

Debug and Prep:

ADF Data Flow: Debug Session, Pt 1
ADF Data Flow: Debug Session, Pt 2 Data Prep
ADF Data Flow: Debug and Test Lifecycle
Mapping and Wrangling: Data Exploration
Debug and testing End-to-End in Mapping Data Flows
Data Masking for Sensitive Data
Benchmark Timings
Dynamically optimize data flow cluster size at runtime

Transformations:

ADF Data Flows: Joins
ADF Data Flows: Pivot Transformation
ADF Data Flows: Window Transformation
ADF Data Flows: Alter Row Transformation
ADF Data Flows: Surrogate Key Transformation
ADF Data Flows: Lookup Transformation
ADF Data Flows: Aggregate Transformation
ADF Data Flows: Derived Column Transformation
ADF Data Flows: Union Transformation
ADF Data Flows: Unpivot Transformation
ADF Data Flows: Exists Transformation
ADF Data Flows: Filter Transformation
ADF Data Flows: Conditional Split Transformation
ADF Data Flows: Select and New Branch
ADF Data Flows: Flatten Transformation
ADF Data Flow Sources: CosmosDB
Fuzzy Lookups
Quick Transformations
Drifted Columns using Pivot
Source & Sink Transformations: Partitioned Files
ADF Data Flows: New Datasets Parquet and Delimited Text
JSON Transformations
Infer Data Types
Dynamic Joins and Lookups

Optimize:

ADF Data Flow: Monitoring UX
ADF Data Flow: Data Lineage
ADF Data Flow: Optimize Data Flows
ADF Data Flow: Iterate files with parameters
ADF Data Flow: Decrease start-up times
ADF Data Flow Perf: SQL DB
Optimize compute environment

Patterns:

ADF Data Flow: Staging Data Pattern
ADF Data Flow: Clean Addresses Pattern
ADF Data Flow: ETL DW Load Pattern
ADF Data Flow: Deduplication of your Data
ADF Data Flow: Merge Files
ADF Data Flow: SCD Type 1 Overwrite
ADF Data Flow: SCD Type 2 History
ADF Data Flow: Fact Table Loading
ADF Data Flow: Logical Models
ADF Data Flow: Detect source data changes
ADF Data Flow: Schema Drift Handling
Flexible Schema Handling with Schema Drift
ADF Data Flow: Parameters
Transform SQL Server on-prem with delta data loading pattern
SCD Type 1 and Type 2 demo by Bob Rubocki
Rule-based Mapping
Distinct Row & Row Counts
Handling truncation errors
Partition your files in the data lake
Intelligent data routing
Transform and Create Multiple Tables
Data Quality Patterns
Self-join patterns
Data lake file output options
Evolving database schema handling

Expressions:

Date/Time expressions
Split Arrays and Case Statement
Fun with string interpolation and parameters
Data Flow Script Intro: Copy, Paste, Snippets
Data Quality Expressions
Collect aggregate function

 
Do you want to know more or you want to try out some examples, you can find them here

Feel free to leave a comment

Data Factory Pricing

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Azure DevOps and Azure Feature Pack for Integration Services

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Azure Feature Pack for Integration Services

Azure Blob Storage
A great addition for SSIS is using extra connectors like  Azure Blob Storage or Azure Data Lake Store which are added by the Azure Feature Pack. This Pack needs to be installed on your local machine. Are you running your SSIS packages in Azure?  You don’t have to install anything, this pack is installed by default.

 

 

Building your SSIS Packages in Azure DevOps
After I started to use Azure Dev Ops to build my SSIS packages on a hosted VS2017, I got some strange error messages running these packages.

Microsoft Support
After contacting support we found out that the Azure Feature Pack is not installed on a Hosted VS2017 instance and that you need to add this installation to your build processes.
 
Install Azure Feature Pack on your Hosted VS2017 machine
Follow the steps to download and install the Azure Feature Pack:

Open  your dev.azure.com/instance.
Create a new Build Pipeline or use an existing one.
Select the correct Sources and after that you can add a new build task.
Add a Powershell Task.

This task needs to be added before the build process of your SSIS project.

Define the Display name “Install Azure Feature Pack”.
Type => Inline.
Add the script which you can find below.
Save and Queue the Pipeline.
Check the Results.

 

Powershell script
The script will take care of downloading and installing the Azure Feature Pack for SSIS2017 on your hosted 2017 machine.
The File SsisAzureFeaturePack_2017_x64.msi will be downloaded to the system variable Build.StagingDirectory.
Inline script:
[code lang="ps"]
# Erwin de Kreuk
# February 2019
# PURPOSE: Install Azure Feature pack on Hosted VS2017 machine in Azure DevOps

Write-Information 'Starting ADF ARM Transform'

#Define Filename
$Filename = 'SsisAzureFeaturePack_2017_x64.msi'
$Arguments=' /qn'
Write-Host 'Downloading...$Filename'
#Define download link including filename and output directory with filename
Invoke-WebRequest -Uri 'https://download.microsoft.com/download/E/E/0/EE0CB6A0-4105-466D-A7CA-5E39FA9AB128/SsisAzureFeaturePack_2017_x64.msi' -OutFile '$(Build.StagingDirectory)\$Filename'

Write-Host 'Installing...$Filename'
Invoke-Expression -Command '$(Build.StagingDirectory)\$Filename $Arguments'
Write-Host 'Finished Installing...$Filename'

[/code]

The next time you build your SSIS Packages with the Azure Components, these packages are build correctly. Create a Release Pipeline to Deploy the SSIS Packages to the SSIS server and to test your Package.
Thanks for reading today and if there’re some questions left do not hesitate to ask them.

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Are you using Azure DevOps?

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Azure DevOps

More and more users are starting with Azure DevOps. Azure DevOps can be used for up to 5 users free of charge and is therefore a great start to start a project.
 
 

Build and Release
I will not describe what Azure DevOps can do, because that is too much to mention. In my future articles I want to take you into the challenges that I had in my projects during building and releasing of f.e. DataFactory, SSIS Packages, KeyVault, a number of recognizable Azure Services that are regularly used within Business Analytics projects.
If you want to use Azure Feature Pack components like Azure Blob Storage or Azure Data Lake Store  in your SSIS Packages, I would definitely read the following article:
Azure DevOps and Azure Feature Pack for Integration Services

Feel free to leave a comment

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Rerun Pipeline activities in Azure Data Factory

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Rerun Pipeline activities in ADF!

As of today you can rerun or partially, yes you’re reading it correct partially, rerun you Azure Data Factory pipeline.Where you previously had to run the entire Pipeline again, you can now run a part of the Pipeline. This can save a lot of time if many different activities are created within one pipeline. Another nice step forward, I’m curious what else is coming in the next months.
 

Visualized
Besides that you can rerun your Pipeline in Azure Data Factory in a easy way, you also have the possibilities to see your run,  visualized in the Azure Data Factory Monitoring. This is a big improvement in my opinion.

Rerun a Pipeline
If you want to partially rerun a Pipeline, follow the steps below:Select the Pipeline which has failed, go to the view activity runs and select the activity which failed.
Click on the Rerun Icon

 
 
 
 
You need to confirm that you want to rerun this activity.

The Pipeline will start and will first skip all the activities(the grey new icons in the upper right corner of each activity) in the Pipeline before your selected Activity.Your Pipeline will now finalize all the activities from your newly defined starting point.

 
What else is new?
Monitor Rerun History
You can now view all the history reruns by clicking on the toggle to ‘View All Rerun History’.

By clicking on the red marked action, you can see all the History from an particular Pipeline run.

 
Thanks for reading.
 
 
Updated 10th of March:
 
Found a video on Channel9 which explains how to  “Rerun activities inside your Azure Data Factory pipelines”
https://channel9.msdn.com/Shows/Azure-Friday/Rerun-activities-inside-your-Azure-Data-Factory-pipelines?ocid=player

Feel free to leave a comment

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