Deciphering ETL: Understanding the Essence Beyond Azure’s Data Tools
Understanding ETL (Extract, Transform, Load)
ETL, short for Extract, Transform, Load, is a crucial process used by data-driven organizations to gather information from diverse sources and amalgamate it, enabling streamlined discovery, reporting, analysis, and decision-making.
This process was formed because data sources can be very diverse in type, format, volume and reliability, so it is necessary to process the data to make it useful when combined for more advanced processing. The target data stores can be databases, data warehouses or data lakes, depending on the objectives and technical implementation.
To comprehend this concept better, let’s delve into each stage of the ETL process by addressing three fundamental questions:
- Extraction: Where does the data come from?
During extraction, the ETL process identifies the data and copies it from its sources so that the data can be transported to the target data warehouse. Data can come from structured and unstructured sources, including documents, e-mails, business applications, databases, equipment, sensors, and third parties, among others. - Transformation: How is the data refined?
Since the extracted data is not processed in its original format, it must be mapped and transformed in order to prepare it for the final data warehouse. In the transformation process, ETL validates, authenticates, de-duplicates or aggregates the data so that the resulting data is reliable and searchable, i.e., it is processed so that it is ready for the next stage. - Loading: Where does the transformed data go?
Once transformed, ETL moves the data to its intended destination. This step involves the initial loading of all source data or the incremental loading of changes. Loading can occur in real-time or scheduled batches, depending on the operational needs.
The Significance of ETL Processes
Ever wondered why ETL processes are pivotal? Well, it turns out that they are the key to intelligently merging data, which is critical to making strategic decisions in the business world. Imagine being able to make informed, agile decisions that save time and money. ETL processes do exactly that: they cleanse data while extracting and loading it, allowing us to work with quality information for smart decisions in the business world.
Key Azure Tools for Intelligent Data Management
If you are looking for a more controlled way to apply these processes, here are some Azure tools specially designed for this purpose:
Extraction
- Azure Data Factory: Think of it as the cloud’s master of ceremonies for integrating and transforming data. With its pipelines, you can guide your data through different stages in a controlled and supervised manner.
Transformation
- Azure Databricks: This tool is the place where data analytics solutions are cooked. It provides an open, unified platform for developing and deploying large-scale analytics and artificial intelligence solutions. This is where code is created and executed in libs that can be called from Azure Data Factory pipelines.
Load
- Azure Synapse Analytics: Imagine a service that gives you total freedom to query data at large scale, either serverless or with dedicated resources. That’s Synapse, an unlimited analytics service.
- Azure SQL Database: This cloud platform offers you a secure, scalable and highly available SQL database. It’s perfect for storing and processing business and web application data.
With these Azure tools, you’ll be able to manage your data intelligently and efficiently, from extraction and transformation to loading, allowing you to unlock a range of opportunities in analytics and business decision making. Now, diving into the hands-on action, we will use Azure Data Factory to create our own pipeline and visualize the data extraction process. Ready to take the first steps?
Creating the path for data automation with Azure Data Factory
We will use Azure Data Factory to take you through this exciting step-by-step journey:
Log into your Azure Data Factory account and head to the “Author” tab to dive into the development area.
Once in the “Author” section, click on the “+” icon next to the search engine. Then select “Pipeline” and once again, choose “Pipeline”.
Now you will discover the “Activities” section which houses different types of sub-processes for your pipeline. You are in the pipeline designer! To add activities to your pipeline, simply drag and drop. Start by finding the “Copy data” activity and attach it to the pipeline. This activity allows you to copy data from the source to the target database (Sink).
Customize the name of your activity. Click on it and go to the “General” section. Type in a new name, for example, “Copia de datos”.
Now it is time to configure the activity. Go to “Source” and choose the data source from which you will extract the information (example: Azure Blob Storage, SQL Database, Oracle, etc.). Subsequently, select your source and provide details on how the information will be extracted (table, file, query, etc.). For example: DB2 Database
Configure the “Sink” section, where the data will be stored. Repeat the last process, now setting the parameters corresponding to the data destination.
Validate our pipeline doing click on “Validate” button to ensure its correct configuration. Once validated, click “Publish” button to save the changes and return to the Azure Data Factory home page.
Find your pipeline in Factory Resources, click on its name and select “Trigger” > “Trigger now” to run it immediately. Confirm the action and watch your pipeline come to life.
Go to “Monitor” > “Pipeline runs” to monitor the progress of your pipeline. Here, you can see the status, run time and other details to know when it is finished.
Check the results! Access the target database to confirm that the data has been successfully transferred.
Now that we have explored the ETL process and learned how to perform a basic extraction using Azure tools, it is exciting to consider the true magnitude of scalability that this process can achieve. From these first steps to managing complex data streams, the scalability possibilities are enormous. Imagine extending this process to include advanced transformations, integration of multiple data sources, and full automation — the potential for growth is limitless! These fundamental insights provide us with a solid foundation to explore and expand our capabilities in intelligent data management, allowing us to adapt and evolve with the changing demands of today’s business world.
