ETL in Azure Data Engineering

Origins of ETL :

ETL started in the 1970s and 1980s when businesses began using computers to save large amounts of data. At first, data was stored in different systems that could not easily share information. For example, sales data might be in one database, while customer information was in another.

Companies soon realized that they needed a way to combine all this data to answer important questions. For instance, managers wanted to see how marketing expenses affected sales or which products were most profitable. But pulling data straight from operational systems was difficult and slow.

This need led to the idea of creating a separate storage area just for analysis. Data could be copied from day-to-day systems into this central place. To transfer the data, organizations had to take it out of each system, change it into a uniform format, and place it into the new storage system.

That process became known as ETL: Extract, Transform, Load.

In the early days, companies wrote their own programs and scripts to perform ETL. These custom solutions were often fragile and hard to maintain because every change in the source systems could break the process.

Early Data Warehousing

During the 1990s, the concept of the data warehouse gained widespread recognition. It refers to a specialized type of database designed specifically for reporting and analytical purposes.

Before data warehouses, teams often ran reports directly on transactional systems. This caused several problems:

• Reports slowed down the systems used for daily operations.
  
• Data was often incomplete or stored in inconsistent formats.
  
• It was difficult to keep a history of old records for long-term analysis.
  

With a data warehouse, companies could store cleaned, organized, and historical data in one place. This enabled analysts and business leaders to make more informed and effective decisions.

Data warehouses were usually relational databases, like Oracle, Teradata, or IBM DB2, designed for fast queries.

To fill these warehouses, businesses relied heavily on ETL. Here is how each step fit into early data warehousing:

• Extract: Data engineers took data out of source systems on a regular schedule (often nightly).
  
• Transform: They cleaned the data, fixed errors, matched formats, and sometimes aggregated it (for example, grouping daily sales by month).
  
• Load: The processed data was then saved into tables inside the warehouse.
  

At first, ETL jobs were run in batches, usually overnight or over weekends. This way, reports could be generated the next morning without slowing down operational systems.

Evolution of ETL :

As data grew in size and complexity, ETL had to change. In the late 1990s and early 2000s, businesses started using specialized ETL tools instead of writing custom scripts. Some popular tools were:

• Informatica PowerCenter
  
• IBM DataStage
  
• Microsoft SQL Server Integration Services (SSIS)
  
• Oracle Warehouse Builder
  

These tools allowed teams to create ETL workflows with visual interfaces, reducing the need to code everything by hand. They also made it easier to:

• Schedule jobs
  
• Monitor data flows
  
• Handle errors
  
• Document processes
  

Even so, these systems were mainly built for on-premises servers. Companies had to buy and maintain expensive hardware, install software, and hire specialists to keep everything running.

Another limitation was batch processing. Data loads typically happened once per day, so reports were always based on information that was at least a few hours old. Real-time analytics was rare and very complex to build.

As data volumes continued to increase, many companies found it hard to expand their ETL pipelines. Storage expenses went up, and processing big datasets became more time-consuming.

Transition to Cloud ETL :

In the 2010s, the rise of cloud computing began to reshape the ETL process once more. Platforms like Microsoft Azure introduced new ways to store and process data. Instead of buying hardware, companies could rent computing power and storage as needed.

This approach made ETL quicker, more affordable, and simpler to scale up. Cloud services provided benefits like:

• Elastic resources: You could quickly scale up to handle big workloads and scale down when not needed.
  
• Managed infrastructure: Azure and other cloud providers took care of servers, software updates, and security patches.
  
• More frequent data processing: Cloud ETL allowed near real-time updates, rather than just daily batches.
  
• Easy integration: Cloud platforms offer straightforward connections to a wide range of data sources and targets.

Within Azure, Azure Data Factory (ADF) emerged as the primary service for handling ETL tasks. ADF provided:

• A fully managed, cloud-native ETL solution.
  
• A visual designer for building pipelines.
  
• Over 90 connectors for common data sources.
  
• Support for both batch and real-time processing.
  
• Seamless connectivity with other Azure services such as Data Lake Storage, Synapse Analytics, and Power BI.

This shift meant that businesses no longer needed to spend months setting up hardware and software. They were able to begin on a small scale and expand gradually as needed. It also opened the door to modern analytics, including machine learning, big data processing, and advanced reporting.

How Cloud ETL Changed Data Engineering :

Moving ETL to the cloud made it more accessible to smaller companies, not just large enterprises. Teams could now:

• Automate complex data workflows with less effort.
  
• Pay only for the resources they used.
  
• Process and deliver data much faster.
  
• Combine structured and unstructured data more easily.
  

Over time, cloud ETL became the standard approach for most new data projects. Azure continues to add features that improve ETL performance, security, and monitoring.