Azure Data Pipeline: A Complete Guide

The process of integrating data from various sources and loading the consolidated data into a data store or analytics environment is essential for business operations. Whether you’re looking for on-premises to cloud platform migration or data integration for analytical purposes, an Azure data pipeline is a good solution.

With Azure Data Factory pipelines, you can automate the movement and transformation of data. This will help enhance your operational efficiencies by allowing you to manage data integration projects from a single environment.

Let’s look into the practical use cases of building Azure Data pipelines and the steps to validate, debug, and publish it. We will also delve into the details of creating an Azure data pipeline with an efficient integration tool.

What is Azure Data Factory?

Azure Data Factory (ADF) is a fully managed, serverless data integration service offered by the Microsoft Azure cloud computing platform. ADF allows you to create data-driven pipelines in the cloud to automate data movement and transformation at scale.

You can use Azure data pipelines to combine data from multiple sources, transform the data, and load it into varied destinations. Some of the common destinations include Azure Data Warehouse, Azure SQL Database, Azure Cosmos DB, and other analytics engines.

While ADF doesn’t store data itself, you can use it to move data between supported data stores. Then, you can utilize computing services in an on-premise environment or in other regions to process this data.

How to Validate the Azure Data Factory Pipeline

Azure Data Factory offers robust data validation capabilities, helping your organization ensure data integrity, accuracy, and compliance throughout the data pipelines.

You can use validation in Azure Data Factory pipelines to ensure the pipelines only continue execution under certain conditions. The pipeline must validate that the attached dataset reference exists, meeting the specified criteria or timing out.

Let’s assume that during a scheduled data load, the source data may not be available. When the pipeline is triggered, the files in the source folder may not have been generated. By default, this causes the pipeline to fail. However, with ADF, you can use Validation Activity to halt the pipeline execution and retry later.

Here are the steps to validate an ADF pipeline:

• In the pipeline Activities pane, search for Validation, and drag a Validation activity to the pipeline canvas.
• If not already selected, then select the new Validation activity on the canvas, followed by its Settings tab.

• Either select a dataset or click the + New button to define a new one. For file-based datasets, you can select a specific file or a folder.
• The other options under the Settings tab include:‍

Timeout: This is the time duration for the validation activity to be active. It has a default value of seven days.‍

Sleep: This is the time (in seconds) between retry attempts.‍

Child items: The Child items setting is available only when you select a folder for a dataset. It allows you to control the pipeline execution based on some folder checks. Ignore involves checking only the existence of the folder, regardless of any folder objects. The True option involves checking the existence of the folder and objects in the folder. False involves checking if the folder exists and if it is empty.

You can use the output of the Validation activity as input for other activities.

Steps to Debug and Publish the Azure Data Factory Pipeline

When you develop complex, multi-stage Azure Data Factory pipelines, it may be difficult to test the pipeline functionality and performance as one block. By debugging your Azure Data pipeline, you can easily test the functionality or performance of the pipeline activities gradually in the development phase. This is especially beneficial before you publish the changes to the data factory.

Here are the steps to debug your ADF pipeline:

• Use your internet browser to open the Azure portal and search for your Azure Data Factory service.
• Click Launch studio in the Azure Data Factory UI. This will open the Azure Data Factory Studio.

• In the ADF Studio, click the pencil icon on the left-side pane to start debugging the pipeline.
• You will be redirected to the Author window, where you can click on the pipeline that you want to start debugging.
• Click the Debug button on the pipeline design window. It is important to note that when you debug a pipeline, it will be executed completely, and the ETL process will be performed.

• The debug process will start; you can use the Output window to monitor the execution progress.

• A pipeline Run ID in the output window distinguishes the pipeline execution from other executions, whether for the same pipeline or other pipelines.
• When the pipeline execution is complete, you can see the Input and Output information for that activity. The full details about the copy activity will also be available.

• If there is any issue during the pipeline activity execution, you will receive a detailed error message. This will help you troubleshoot the cause of the execution failure.

After you’ve debugged your Azure Data pipeline, you can click Publish all in the top toolbar. This will publish all items you’ve created or updated to the Data Factory service.

Creating Azure Data Factory Pipeline Using Airbyte

While Azure data pipelines can help streamline data movement between multiple sources and destinations, there are some associated limitations:

• ADF supports only over 90 prebuilt connectors. You may not be able to find all the data sources and destinations you need for your organization’s data pipelines.
• There is a steep learning curve, sometimes requiring additional training, due to ADF’s complex features and capabilities. Anyone who is unfamiliar with cloud data integration tools will find it challenging to start using ADF.
• ADF doesn’t have extensive community support or contributions. This may complicate your troubleshooting efforts.

Airbyte, a unified data integration platform, enables you to overcome these limitations. With its comprehensive library of 550+ pre-built connectors, Airbyte allows you to efficiently move data from varied sources to destinations. You can migrate data from databases, SaaS applications, or APIs to data lakes, warehouses, or vector databases.

If you’re unable to find the connector of your choice, you can utilize Airbyte’s personalized connector development options: a no-code Connector Builder, a low-code Connector Development Kit (CDK), and language-specific CDKs.

Airbyte also has an active and growing community comprising 20,000+ users and 900+ contributors. With access to community-driven connectors, plugins, and support resources, this is a significant advantage over ADF.

Here are some other impressive features of Airbyte that make it a good choice for building integration pipelines:

• Effective Schema Management: You can mention how you want Airbyte to handle schema changes in the source for each connection. While you have the flexibility to manually refresh the schema at any time, Airbyte also provides auto-schema change management. For cloud users, it checks for schema changes in the source data every 15 minutes, and for self-hosted every 24 hours. This guarantees efficient and accurate data syncs.
• ‍AI Assist: Airbyte’s AI Assist feature in Connector Builder enables you to streamline the process of building custom connectors. You need to provide the API doc links and specify the required streams. The AI assistant auto-populates the fields for connector configuration that you can modify and deploy directly.
• ‍Streamline Gen AI Workflows: With Airbyte, you can load semi-structured and unstructured data directly into vector store destinations. With Airbyte’s automatic chunking and indexing options, you can transform the raw data and store it in vector databases like Milvus, Qdrant, Weaviate, and Pinecone. This facilitates simplified AI workflows.
• ‍PyAirbyte: This is an open-source library that packages Airbyte connectors into a Python library. PyAirbyte allows you to extract data from Airbyte sources into a local cache. Then, you can merge or transform the data using Python libraries and store it in your destination database.

Now that you’ve seen the many offerings of Airbyte, let’s look into how you can create an Azure Data pipeline using Airbyte.

Step 1: Set up the Source Connector

• Sign in to your Airbyte account.
• Click the Sources option on the left-side pane of the dashboard.
• On the Sources page, click the + New source button to set up a new source.

• You can either scroll through the available connector options or use the Search Airbyte Connectors box to find the required connector. There are multiple connectors available in Airbyte Connectors and the Marketplace.

• When you see the source connector of your choice, click on it to proceed to the connector configuration page.
• Enter all the necessary information, including a Source name and Authentication details.
• Click the Set up source button at the bottom of the page to complete the source configuration.

Step 2: Set up the Destination Connector

• Select the Destinations option on the left-side pane of the UI.
• On the Destinations page, click the + New destination button to set up a new destination.

• To set up an Azure Data pipeline, you can opt for Azure Blob Storage as the destination. Either scroll through the available connector options or use the Search Airbyte Connectors box to find the connector.

If you require a custom Azure connector apart from Azure Blob Storage, you can use Airbyte’s CDK or Connector Builder with AI Assist features to create one.

• Enter the required information, such as the Azure Blob Storage account key and account name. Select the Output Format as JSON or CSV.

• When you’re done configuring the connector, click Set up destination at the bottom of the page.

Step 3: Set up a Connection

Following the configuration of your source and destination connectors, here are the next set of steps to execute:

• Select the Connections option from the left-side pane of the UI to set up a connection.
• Choose the source and destination (Azure Blob Storage) to use for this connection.
• Set the destination namespace, sync mode, and destination stream prefix if needed. From selecting which streams you want to replicate to individual fields to sync, Airbyte offers granularity for the connection setup.
• After completing the connection settings, click Set up connection.

These steps will set up the connection between your source and destination platforms and start the data movement process.

Practical Use Cases for Building Azure Data Factory Pipeline

ADF pipelines offer a range of use cases, contributing to how your organization can handle data workflows. Here are some of the popular use cases:

• Real-Time Data Processing and Event Streaming: You can create Azure Data pipelines to integrate your data with Azure Event Hubs or other real-time data streaming services. This enables you to handle real-time data streams generated by social media feeds, sensors, financial transactions, and application logs. You can gather operational insights and perform customer behavior analysis with the streaming data.
• ‍Data Lake Management and Analytics: With an ADF pipeline, you can ingest data from multiple sources and integrate it with Azure Data Lake Storage. ADF pipelines also facilitate data cleansing, filtering, and transformation before loading your data into big data analytics tools like Spark or ML models.
• ‍Data Warehousing: You can use ADF pipelines to ingest data from disparate sources. Following this, you can transform and load the data into a central data warehouse like Azure Synapse Analytics. This is helpful for data preparation for BI tools that provide you with insightful reports and dashboards.
• ‍Integrate Data from Different ERPs into Azure Synapse: With ADF pipelines, you can integrate data from multiple ERP systems into Azure Synapse Analytics. By consolidating and harmonizing the data, ADF provides you with a unified analytics and data management approach.
• ‍Cloud Migration: ADF pipelines allow you to migrate your data from on-premises data stores to cloud-based data stores like Azure Blob Storage or Azure SQL Database. This is particularly useful for migrating your data from legacy systems to modern data warehouses.

Summing It Up

Azure Data pipelines facilitate extracting data from multiple sources, transforming it, and loading it into Azure- or non-Azure destinations. The validation capabilities of Azure Data Factory help ensure data accuracy, integrity, and compliance within your ADF pipelines.

You can also debug your Azure Data Factory pipeline in the development phase. Debugging will execute the pipeline operations, perform the ETL process, and provide you with details about the activity. For any issues during debugging, you can troubleshoot the cause with the received details.

Building an Azure data pipeline has several use cases, including data warehousing, cloud migration, data lake management, and real-time data processing.

If you want an almost effortless process to creating an Azure Data pipeline, you can use Airbyte, an efficient integration solution. With over 550+ connectors, effective schema management, and automated data pipeline setup capabilities, Airbyte simplifies data integration with Azure destinations.