Ruthie  Bugala

Ruthie Bugala

1622562240

How to schedule Azure Data Factory pipeline executions using Triggers

In the previous articles, we discussed how to use the Azure Data Factory to move data between different data stores, how to transform data before writing it to a predefined sink, how to run an SSIS package using Azure Data Factory and how to use iterations and conditions activities in Azure Data Factory.

In this article, we will see how to schedule an Azure Data Factory pipeline using triggers.

Triggers Overview

Previously, we used the manual execution of the pipelines, also known as on-demand execution, to test the functionality and the results of the pipelines that we created. But it does not make sense to employ someone to execute that pipeline during the night or rely on a human to remember when to execute a specific pipeline. From that point, we can see the need to have another automated way to execute the pipeline at the correct time, which is called Triggers.

Azure Data Factory Triggers determines when the pipeline execution will be fired, based on the trigger type and criteria defined in that trigger. There are three main types of Azure Data Factory Triggers: The Schedule trigger that executes the pipeline on a wall-clock schedule, the Tumbling window trigger that executes the pipeline on a periodic interval, and retains the pipeline state, and the Event-based trigger that responds to a blob related event.

Azure Data Factory allows you to assign multiple triggers to execute a single pipeline or execute multiple pipelines using a single trigger, except for the tumbling window trigger.

Let us discuss the triggers types in detail.

Schedule Trigger

The schedule trigger is used to execute the Azure Data Factory pipelines on a wall-clock schedule. Where you need to specify the reference time zone that will be used in the trigger start and end date, when the pipeline will be executed, how frequent it will be executed and optionally the end date for that pipeline.

Azure Data Factory trigger can be created under the Manager page, by clicking on + New or Create Trigger option from the Triggers window, as shown below:

In the New Azure Data Factory Trigger window, provide a meaningful name for the trigger that reflects the trigger type and usage, the type of the trigger, which is Schedule here, the start date for the schedule trigger, the time zone that will be used in the schedule, optionally the end date of the trigger and the frequency of the trigger, with the ability to configure the trigger frequency to be called every specific number of minutes or hours, as shown below:

#azure #azure data factory

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How to schedule Azure Data Factory pipeline executions using Triggers
Siphiwe  Nair

Siphiwe Nair

1620466520

Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

Ruthie  Bugala

Ruthie Bugala

1622562240

How to schedule Azure Data Factory pipeline executions using Triggers

In the previous articles, we discussed how to use the Azure Data Factory to move data between different data stores, how to transform data before writing it to a predefined sink, how to run an SSIS package using Azure Data Factory and how to use iterations and conditions activities in Azure Data Factory.

In this article, we will see how to schedule an Azure Data Factory pipeline using triggers.

Triggers Overview

Previously, we used the manual execution of the pipelines, also known as on-demand execution, to test the functionality and the results of the pipelines that we created. But it does not make sense to employ someone to execute that pipeline during the night or rely on a human to remember when to execute a specific pipeline. From that point, we can see the need to have another automated way to execute the pipeline at the correct time, which is called Triggers.

Azure Data Factory Triggers determines when the pipeline execution will be fired, based on the trigger type and criteria defined in that trigger. There are three main types of Azure Data Factory Triggers: The Schedule trigger that executes the pipeline on a wall-clock schedule, the Tumbling window trigger that executes the pipeline on a periodic interval, and retains the pipeline state, and the Event-based trigger that responds to a blob related event.

Azure Data Factory allows you to assign multiple triggers to execute a single pipeline or execute multiple pipelines using a single trigger, except for the tumbling window trigger.

Let us discuss the triggers types in detail.

Schedule Trigger

The schedule trigger is used to execute the Azure Data Factory pipelines on a wall-clock schedule. Where you need to specify the reference time zone that will be used in the trigger start and end date, when the pipeline will be executed, how frequent it will be executed and optionally the end date for that pipeline.

Azure Data Factory trigger can be created under the Manager page, by clicking on + New or Create Trigger option from the Triggers window, as shown below:

In the New Azure Data Factory Trigger window, provide a meaningful name for the trigger that reflects the trigger type and usage, the type of the trigger, which is Schedule here, the start date for the schedule trigger, the time zone that will be used in the schedule, optionally the end date of the trigger and the frequency of the trigger, with the ability to configure the trigger frequency to be called every specific number of minutes or hours, as shown below:

#azure #azure data factory

How to Debug a Pipeline in Azure Data Factory

In the previous article, How to schedule Azure Data Factory pipeline executions using Triggers, we discussed the three main types of the Azure Data Factory triggers, how to configure it then use it to schedule a pipeline.

In this article, we will see how to use the Azure Data Factory debug feature to test the pipeline activities during the development stage.

Why debug

When developing complex and multi-stage Azure Data Factory pipelines, it becomes harder to test the functionality and the performance of the pipeline as one block. Instead, it is highly recommended to test such pipelines when you develop each stage, so that you can make sure that this stage is working as expected, returning the correct result with the best performance, before publishing the changes to the data factory.

Take into consideration that debugging any pipeline activity will execute that activity and perform the action configured in it. For example, if this activity is a copy activity from an Azure Storage Account to an Azure SQL Database, the data will be copied, but the only difference is that the pipeline execution logs in the debug mode will be written to the pipeline output tab only and will not be shown under the pipeline runs in the Monitor page.

#azure #sql azure #azure data factory #pipeline

Gerhard  Brink

Gerhard Brink

1620629020

Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.

Introduction

As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).


This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.

#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management

Kennith  Kuhic

Kennith Kuhic

1625915820

Azure Machine Learning Notebook Code and run as pipeline — Automate usingAzureData Factory

Ability to run notebook code as Pipeline

Prerequisite

  • Azure Account
  • Azure Machine learning
  • Create a compute instance
  • Create a compute cluster as cpu-cluster
  • Select Standard D series version
  • Create Train file to train the model
  • Create a pipeline file to run the as pipeline

Steps

Create Train file as train.py

  • Create a directory ./train_src
  • Create a train.py
  • Should be a python file not notebook

Create Pipeline code

  • Load the workspace config

  • Get the default store information

  • Create compute cluster

  • Load the package dependencies

  • Load the data set

  • set the dataset as input

  • Setup output optional

  • I am only creating single step

  • setup the pipeline config and assign

  • Validate the pipeline

  • Now time to submit the pipeline

  • Wait for pipeline to finish

  • Now lets publish the pipeline

  • Every publish will create a REST endpoint

  • I logged into the Azure ML Studio

  • Go to Pipeline on the left menu

  • Click on pipeline endpoint

  • should see a pipeline — Published_Titanic_Pipeline_Notebook

  • Click submit and see if the pipeline line runs

  • Now go to ADF or Synapse Integrate

  • Create a New pipeline

  • Name is AzureMLPipelinetest

  • Drag and drop Azure Machine learning services (only to run published pipeline)

  • Create a New Source for Azure Machine learning using service principal account

#data-factory #machine-learning #azure-ai #azure-machine-learning #azure data factory