How to Build an ETL Process?

How to Build an ETL Process?

Extract, transform, load. Discover the vital steps and methods of building an ETL process for your business.

J
Julia Wrzosińska

What is the ETL process?

Extract. Transform. Load. It’s as simple as that. ETL processing may be viewed as a processing framework for managing all the data within an organization and arranging it into a structured form. This type of control system involves obtaining and securing data from numerous sources (extract), followed by integrating and cleansing (transform), and finally storing the data in its final form and place, where it can be efficiently accessed and analyzed (load). 

In shorter words, ETL is the process of converting information from disparate sources and in a variety of formats into a unified whole. As a result, the data can be used to analyze trends, anticipate performance, and predict future outcomes. Companies nowadays are drowning in raw data that must be sorted through. Well-organized data supports smarter decisions, and managing the ETL process with the appropriate tools is a great approach to achieve that. 

We’re not going to emphasize further how crucial it is for businesses to understand their data; if you don’t know yet why getting to know your numbers truly matters, feel free to read our piece on the importance of data orchestration. Spoiler: data is business gold.

ETL process steps

What exactly are the steps of the ETL process? As we mentioned before, the answer is already encoded in the name:

  1. Extract and copy the data.
  2. Filter and transform it.
  3. Load the obtained data into the data warehouse.

In a nutshell, it is how the ETL process is carried out. When digging into the process, though, you will discover there’s more to ETL than just extracting, transforming, and loading data. More specific steps include:

Step 1. Copy raw data 

Having the original raw data at your disposal can help you discover and solve problems faster. Data scientists and analysts like to get back to ‘ground truth’. And debugging gets considerably more manageable if you follow this step. Also, modern ‘big data’ systems make it easier and cheaper to keep more of the source data around.

Step 2. Establish connectors and extract

The connectors or defined tools that construct the connection are required for extracting data from the source. API, XML, JSON, CSV, and other file formats can be used to store data. To standardize processing, you must extract it all and convert it to a single format.

Step 3. Validate and filter

Is your data in the expected range? For example, you may need data collected only within the past 12 hours and reject entries older than 12 hours. That’s what validation is for. By filtering, you can ignore null columns, inaccurate records, etc.

Step 4. Transform

The most vital and challenging part of the ETL process, especially if it’s done manually. The goal of transformations is to translate data into a given form, depending on the use case. Transformation may include cleansing, de-duplication, standardization, aggregation, data integrity check, and more. This step can really be anything that has to be done with the data in order to get it from "raw" to “ready for use”. It will look very different in each case depending on what you are going to do with the resulting data.

Step 5. Store the transformed data

Loading altered data straight into target systems is not recommended, and this step enables you to roll back the data if something goes wrong. The staging layer is where audit reports are generated, too. 

Step 6. Load

Now your data is ready, so in this step, it is pushed to target data warehouses. It can either overwrite current information in the warehouse or get appended whenever the ETL pipeline loads a batch. 

Step 7. Schedule

Probably the most critical part of the ETL pipeline automation: scheduling the range of data loading. Daily, weekly, monthly - whatever you choose, the loaded batch will have a timestamp to identify the loading date.

Methods of building an ETL process

The number of data processing and transformation algorithms determines the complexity of creating an ETL platform from scratch. Since it might take years to establish a flexible system for companies with many processing choices, it is generally worthwhile to use ready-made tools while developing an ETL solution. ETL development tools substantially simplify the development process and save you time and money.  When it comes to ready-made tools, the choice is between open-source and licensed platforms. The monthly fee for a licensed tool may vary from several hundred to thousands of dollars. Open-source tools are free, but they require more development effort. 

Methods of building ETL pipelines can be broadly classified into two categories: batch processing and real-time processing.

  1. Batch processing

In a conventional ETL pipeline, data is processed in batches from the sources to the destination data warehouses.  Batch processing is generally more efficient and scalable, but can lead to higher latency (processed data is on average less “fresh”). 

  1. Real-time stream processing

Streaming makes it possible to modify data while it’s on the go. Many sources, such as social media and e-commerce websites, provide real-time data that needs constant changes as it is received. ETL cannot be performed in batches on this data; instead, it must be performed on the data streams by cleansing and transforming the data as it is in transit to the destination.

Why is ETL important?

ETL cleanses and organizes data to meet particular business intelligence objectives, such as monthly reporting, but it can also handle more complex analytics or machine learning models to improve back-end operations or end-user experiences.

Using ETL is vital because information sources, whether structured SQL databases or unstructured NoSQL databases, rarely utilize the same or comparable formats. ETL exists to make the data an analyzable whole, but it’s more than that. Developing an automated ETL process provides an answer to a couple of fundamental business challenges. It enables you to process data without manual effort, which saves time and resources. It provides the control, monitoring, and scheduling of tasks. Most importantly, ETL pipelines bring data to a single standard into one centralized place, where the data is ready for high-quality business analytics. All these benefits deliver immediate business value by:

  1. Breaking down data silos.
  2. Making it easy to access and analyze data.
  3. Turning data into business intelligence.
  4. Providing a global view that helps to make data-driven decisions in less time.
  5. Achieving greater ROI (ETL implementations can result in a 5-year median ROI of 112%). 

All of the above are definitely worth introducing within any organization.

ETL vs. ELT

Of course, both ETL and ELT involve data extraction, loading, and transformation. However, as previously stated, they carry out these procedures in a different order - ELT transforms the data only after loading it to the destination (typically a data lake).

ELT is the most modern approach to data circulation, and it works best with big data organizations. It is a novel technology, enabled by high-speed, cloud-based servers. Cloud-based data warehouses provide near-limitless storage and scalable processing capacity, which makes it easy to transform data after loading. However, maintaining analytical cloud capacity demands substantial resources. One of the benefits of ELT is that all of the information is in one location, allowing for fast access to it, while ETL is said to have better information privacy and compliance, as data is cleansed prior to loading.

So the question is whether the data should be transformed before or after it is loaded into the data repository? When is it preferable to utilize ETL, and when is it better to use ELT processes? There is no simple answer to this, as it all depends on specific preferences. ETL vs. ELT is all about what tools you have available, and the scale of your data.

ETL pipelines with Airflow

ETL pipelines are one of the most widely utilized process workflows in businesses today, enabling them to benefit from deeper analytics and complete business information. By adopting Apache Airflow, companies may create, scale, and manage ETL pipelines more effectively. This workflow management software enables organizations to control their tasks in one location, monitor statuses, and maximize available resources, providing the most user-friendly UI.

Most importantly, having your data pipelines in code gives you great flexibility, and Airflow allows you to orchestrate in Python. At the same time, using provider packages and external Airflow services, you can implement a complex pipeline in Airflow without writing a lot of code. 

Astronomer - making Airflow even better

Astronomer allows you to centrally develop, orchestrate, and monitor your data workflows with best-in-class open source technology. With multiple provider packages in our registry, it’s intuitive and straightforward to take off with your first ETL pipelines. If you’d like to give it a shot, here is a short intro to building ETL pipelines with Snowflake

Interested in taking your business to the next level and building the best ETL process? 

Reach out to one of our experts to learn more about how Airflow and Astronomer can help. 

Never miss an update from us.

Do Airflow the easy way.

Run production-grade Airflow out-of-the-box with Astronomer.