An introduction to ETL and Azure Data Factory

Whether you are seeking to understand the basics concepts about ETL or learn which Azure service might be used to handle this process, you are reading the right post!

And if you’ve never heard of ETL or ADF, no worries, it is a good opportunity to enhance your knowledge. I will explain what ETL is and what Azure Data Factory is.

It is important to know that this blog post gives an overview of ETL and Azure Data Factory and their proposals. I’m afraid to say there are not any hands-on, expert scenarios. However, you will get a simple scenario as an example of how to work with ETL and ADF.

ETL refers to a three-step process: extracting data from various sources, transforming it into a consistent format, and loading it into a target destination. This process ensures that data is properly prepared for analysis, reporting, or other downstream operations.

The ETL process serves as the foundation for data integration, ensuring that data is accurate, consistent, and usable across different systems and applications. It involves several key steps that work together to shape raw data into valuable insights. Let’s take a closer look at each phase of the ETL process:

The extraction phase involves gathering data from simple or multiple sources, which can include databases, files, web services, APIs, cloud storage, and more. This step typically requires understanding the structure, format, and accessibility of the source data. Depending on the source, extraction methods can range from simple data exports to complex queries or data replication techniques.

After data is extracted, it often requires cleansing, filtering, reformatting, and combining with other datasets to ensure consistency and quality. This phase involves applying various data transformation operations, such as data cleaning, normalisation, validation, enrichment, and aggregation. Transformations may also involve complex business rules, calculations, or data validation against reference data.

Once the data is transformed and prepared, it is loaded into a target system or destination. The target can be a data warehouse, a database, a data lake, or any other storage infrastructure optimised for analytics or reporting. Loading can be done in different ways, including full loads (replacing all existing data), incremental loads (updating or appending only new or changed records), or a combination of both.

ETL pipelines can have different loading strategies, such as batch processing (scheduled at specific intervals) or real-time processing (triggered by events or changes in the data source). The choice of loading strategy depends on the data requirements, latency needs, and overall system architecture.

Throughout the ETL process, data quality and integrity are of utmost importance. It is crucial to perform data validation checks, ensuring that the transformed data meet predefined quality standards. Validation includes verifying data completeness, accuracy, consistency, conformity to business rules, and adherence to data governance policies. Any data that fails validation can be flagged, logged, or even rejected for further investigation.

As data volumes and complexity continue to increase, modern data integration platforms, such as Azure Data Factory, have emerged to streamline and automate the ETL process. These platforms offer visual interfaces, pre-built connectors, and scalable infrastructure to facilitate data extraction, transformation, and loading tasks. They also integrate with other tools and technologies, such as data lakes, big data processing frameworks, and cloud services, to enable advanced analytics and insights.

By using the ETL process, you can achieve successful data integration. This enables organisations to extract data from various sources, transform it into a consistent format, and load it into a target system for analysis and reporting. ETL plays a critical role in ensuring data quality, consistency, and reliability. With the advent of modern data integration platforms like Azure Data Factory, organisations can leverage advanced capabilities and automation to streamline their ETL workflows, unlocking the true value of their data assets.

Below, we’ve highlighted the most important features and benefits of ADF.

ADF supports a wide range of data sources and platforms, including on-premises databases, cloud-based services (such as Azure SQL Database, Azure Blob Storage, and Azure Data Lake Storage), Software-as-a-Service (SaaS) applications (like Salesforce and Dynamics 365), and popular big data frameworks (such as Hadoop and Spark). This versatility allows organisations to integrate data from multiple systems seamlessly.

ADF provides a user-friendly graphical interface, known as the Azure Data Factory portal, which enables users to design, monitor, and manage their data integration pipelines. The portal offers drag-and-drop functionality, allowing users to easily create workflows, define dependencies between activities, and set up scheduling and triggering mechanisms.

ADF includes a rich set of data transformation activities to clean, transform, and enrich data during the ETL process. These activities can be used to perform operations such as data filtering, mapping, aggregation, sorting, and joining. ADF also supports custom data transformations using Azure Functions or Azure Databricks, enabling advanced data processing scenarios.

Azure Data Factory leverages the scalability and elasticity of the Azure cloud infrastructure. It can dynamically scale resources based on workload demands, allowing for efficient processing of large volumes of data. ADF can also parallelise data movement and transformation activities, enabling faster execution and improved performance.

ADF seamlessly integrates with other Azure services, providing extended capabilities and flexibility. For example, you can incorporate Azure Machine Learning for advanced analytics, Azure Logic Apps for event-based triggers, Azure Databricks for big data processing, and Azure Synapse Analytics for data warehousing and analytics.

ADF offers comprehensive monitoring and logging capabilities to track pipeline execution, diagnose issues, and analyse performance. It provides built-in dashboards, metrics, and logs for monitoring data movement, activity execution, and pipeline health. Additionally, ADF supports alerts and notifications, allowing users to set up email or webhook notifications based on predefined conditions or errors.

Workflow orchestration and scheduling

Data Factory provides a visual interface for designing and orchestrating complex ETL workflows. It allows you to define dependencies, execute activities in parallel or sequence, and schedule data integration tasks based on your requirements.

When Azure Data Factory is created it’s exposed to the internet, but there is the possibility to configure it to be private. In terms of the “public” and “private” aspects of Azure Data Factory, here’s what they generally refer to:

Public Data Factory

A public data factory refers to an Azure Data Factory instance that is accessible over the public internet. It means that the Data Factory can be accessed and used by users or applications outside of your private network. Public Data Factories are typically used when you need to integrate data across different cloud platforms, on-premises systems, or when you want to expose your data integration pipelines as a service to external consumers.

Private Data Factory

A Private Data Factory refers to an Azure Data Factory instance that is deployed within your private network or virtual network (VNet). It is isolated and not directly accessible over the public internet. Private Data Factories are used when you need to securely integrate data within your private network or when you want to restrict access to your data integration pipelines within your organisation.