An ETL pipeline can help your agency turn scattered data into meaningful analytics. As an agency, you are likely to have several different channels through which data comes in. Think of all the data you own and how difficult it is to take it all into consideration when making business decisions.
To make effective use of all the information you have at your disposal, building an SSOT with an ETL pipeline is the right solution.
The term ETL stands for extract, transform and load. An ETL pipeline starts by extracting data from all your different sources, transforming it into a unified format and loading it into a data warehouse. From that point on, you are ready to derive insights from your data that would be impossible to get from raw, unprocessed data. Let’s learn how.
What is an ETL Pipeline?
An ETL pipeline defines a set of processes that take data through three stages: extraction, transformation, and loading. At the end of the process, you are left with clean data that gives businesses informed insights for planning processes, analysing trends, reporting and much more.
Turning raw data into meaningful and digestible insights is key to making calculated decisions and reducing risks. ETL pipelines find use in business intelligence applications, data warehousing, and pretty much in most other cases when data needs to be transformed and moved at scale.
For example, you can build an ETL pipeline when you’re migrating data from one database to another (usually from legacy systems to upgraded ones), consolidating the data into a centralised system, and formatting it so that it complies with various regulations such as CCPA, HIPAA, or GDPR.
ETL Pipeline Architecture
The architecture of an ETL pipeline is based on the three main things it does: extracting, transforming, and loading. For example, in an ETL pipeline, data must first go through a transformation engine, which may use staging tables to temporarily store the data before loading it into the data storage you choose.
The purpose of the extraction phase is to collect data from disparate sources and store it in tables inside your chosen database. Data can come from various platforms that your agency is using.
With the popularity of mobile and web apps on the rise, data is no longer pulled from spreadsheets. Instead, APIs or web scraping tools are used. For example, if you want to find the best-selling products in a niche for your affiliate business, you can easily scrape reviews from Amazon listings and quickly find what the hottest product is and why.
The extraction phrase can be approached in three main ways:
- Full Extraction. All the data is extracted from the sources at once and imported into the data pipeline.
- Incremental Extraction. An extraction process is performed regularly, ingesting the most recent data. This process is commonly performed when extracting data using APIs.
- Source-Driven Extraction. In this case, when data changes, a signal is sent to the ETL pipeline, which triggers an extraction process for the new data in a specific source.
After the data has been extracted, it moves onto the next step, which is to transform it into a unified format.
The transformation process is crucial for cleansing and transforming your data from various sources into a unified format. Without this process, data from different sources will have different shapes and sizes, making it difficult to fit it into one database and even more difficult to gain insights from it.
Some of the steps in the transformation process are:
- Basic Cleaning. In this step, a common data format is set for similar data that’s otherwise collected in multiple formats. For instance, when users pay in multiple currencies, you might want to convert all payments into one currency that will make it possible for you to manipulate that data.
- Join Tables. Most data you’ll transform is already structured into tables, but you’ll still need to combine it in some way. If you’re extracting it from multiple SQL tables, in this step you have to merge and join them.
- Filtering. A common practice for selecting useful data is by filtering only the relevant data based on certain rules that characterise it. Then, we can discard all the remaining data and continue with the transformation.
- Aggregation. As the name suggests, applying an aggregate function to a certain group of rows based on a condition such as ranking the minimum, maximum, or finding the sum or the average value is a common practice too.
When the extracted data is transformed into a proper format, the final step is to load this data into a single database where it can be used for analytical or BI purposes.
Loading is the final step of the ETL pipeline process, where you load the data into a database or data warehouse where you can draw insights from it. There are several options for uploading the processed data afterward, depending on your needs. Here are a few:
- SQL RDBMS: A relational database that uses SQL and is quite popular for storing and querying data.
- Flat Files: The processed data can be loaded into simple formats too such as CSV, Excel spreadsheets, or TXT files, making it more accessible to non-technical users.
- Cloud Platforms: Loading data into the cloud has become one of the easiest and safest approaches. You can use platforms like Google BigQuery, Snowflake, or Amazon Redshift which provide data lakes, data warehouses, and data lakehouses for your data.
ETL Pipeline vs Data Pipeline: Key Differences
The terms ETL pipeline and data pipeline are different in terms of the processes each involves. The terms are often used interchangeably, but they have different meanings.
An ETL pipeline is a type of data pipeline that consists of three specific, consecutive processes for manipulating data. In the meantime, a data pipeline can consist of any data sorting processes that are executed in a specific order.
The main difference lies in the processes involved in the pipeline. Other key differences between an ETL pipeline and a data pipeline are:
- Data transformation. A data pipeline might or might not include the process of data transformation. Meanwhile, data transformation is an essential element of an ETL pipeline.
- Data moving frequency. In data pipelines, data can move between systems either in real time or in scheduled batches. Meanwhile, in an ETL pipeline, data is exclusively moved in scheduled batches.
- Data output state. When it comes to data pipelines, they don’t always have to end with loading data as the last process, while an ETL pipeline ends by loading data into a storage system.
What is a Staging Area?
A staging area holds data that has just been transformed before it’s loaded into the storage. That’s done in order to protect data if something goes wrong during transformation and you’d like to reverse the changes.
Having a staging area can act as a safety layer. While the data is in the staging area, you can produce reports or perform analysis on the data. And the data can be manipulated before finally being loaded into storage.
A staging area consists of basic storage as well as data staging server software. The software enables all the functionality for working with the data that’s being stored before moving it to the target repository.
How to Build an ETL Pipeline Using Batch Processing: Step-by-Step Guide
Building an ETL pipeline is possible using either the stream processing or batch processing methods. The second one is more common, so we’ll focus on it in this article.
In ETL pipelines, data is commonly moved in batches. These batches have a reference code that is linked to common data details, such as inventory numbers or location codes.
Stream processing mainly works with new data. But in batch processing, the movement of data is slightly delayed, which makes management much easier.
Don’t be worried, as building an ETL pipeline with batch processing is doable in seven simple steps. Here’s how you can do it:
Step 1. Create a Reference Dataset
The first step is to make a dataset that will be a big part of figuring out which data is qualified to move through batch processing. The reference data set will consist of which data is allowed, and any new data that comes in will be compared to it.
Only data that passes the validation against the reference dataset will be able to pass. Keep in mind that this step is optional.
Step 2. Connect Sources to Extract Data
Now that the rules for extracting data have been defined, it’s time for the extraction process to start. This is the step where you connect all the data sources your agency owns and that are relevant to the project.
After connecting the different data sources and converting the data into a unified format, you’re ready to start validating it.
Step 3. Validate the Data
Once the data has been extracted from the sources, you have to verify if it fits within the range of the data type you’ve defined and approve or disapprove it as necessary. Later on, you might return to the rejected data and correct or further analyse it.
Step 4. Transform the Data
Data transformation is the essence of an ETL pipeline. In this phase, the data is cleaned from duplicate records, verified for its integrity (whether any data is corrupted or lost), and sorted using functions (like aggregate or window functions).
Step 5. Stage the Data Before Loading
Right after the data is transformed, it’s best to not rush loading it into storage right away. Instead, having a staging area where you can load the data first can serve as an extra layer of protection.
Once your data has been transformed, staging it gives you a chance to look at it, make sure every process was done correctly, and even analyse it before putting it in your final database. And if something went wrong during the process, you can easily revert it at this stage.
Step 6. Load the Data into Storage
Finally, your data is ready to be stored in your database or data warehouse of choice. And, since the ETL process occurs regularly, you can choose to either overwrite the existing data or add the new data every time the process runs again. Keep in mind the volume of your storage when deciding on this factor.
Step 7. Automate the ETL Pipeline
The beauty of ETL pipelines lies in automation. You’ll spend less time moving your data once you’ve automated and scheduled the process. According to your needs, define how often you want the ETL process to run. This can be a weekly, monthly, quarterly, or custom schedule. To keep your data storage organised, always include a timestamp when loading data.
Consolidating Your Data in a BigQuery Data Warehouse
If the process of building an ETL pipeline and consolidating your data seems daunting, let data experts handle it for you.
At Acuto, we build customised data warehouses that are unique to your business. We’ll listen to your requirements and bake them into your own data warehouse. The best part is that you’ll finally get to own all your data across every platform.
Why work with us? We’re a team of developers and cloud engineers with an extensive background in PPC. On top of building a data warehouse for you, we can even create automation scripts that will further save your team time and money on menial tasks.
Get in touch with us today and schedule a call to learn more!
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With the ever-growing amount of data in the industry, there’s also a growing need for data processing solutions that are both affordable and effective. An ETL pipeline solution helps you work with data significantly faster and load it into your data warehouse with ease. The concept of an ETL pipeline is simple because data passes through three simple steps:
One of the best ways to get data from different sources into your data storage is to build an ETL pipeline. Once you’ve built and automated it, the process is a smooth ride.