Ever since the dawn of the internet and modern technology, consumer data and data, in general, has become invaluable to organizations worldwide. The importance of maintaining accurate, usable, and accessible datasets in all sorts of organizations can’t be overstated. In the age of business intelligence, the need to maintain functional datasets is as urgent as ever.
But when a business decides to automate its operations, the first issue they face is that they don't have the data for it, at least not in a way that can be used for automation, training AI algorithms, or anything of the sort. The solution to this issue comes from establishing ETL pipelines and data warehouses.
ETL stands for extract, transform, load, and an ETL pipeline is a process through which all sorts of datasets go through to be sorted before they can be stored in a data warehouse. Once there, this data can be used to train and retrain algorithms, aid in executive decision-making, predictive analyses, and much more.
But how do you establish an ETL pipeline, the process behind it, and most importantly, what does it take?
What is ETL?
ETL stands for extract, transform, load and is the foundation of all machine learning and data analytics workstreams. In its raw, unorganized form, data cannot be used to train any AI, or for any business automation processes. Therefore, even after your initial automation goals are met, you will need to keep retraining your AI programs with new data and analytics for better insight and performance.
Any data that your business deems relevant for your automated processes to perform smoothly needs to be passed through an ETL pipeline before it is of any use to your software, which is why an ETL pipeline is not just something you need to achieve automation, but a continuous necessity throughout your use of advanced business intelligence mechanisms.
ETL pipelines will extract all relevant data from their sources, organize and transform it into formats understandable by your algorithms, and load all the data in data warehouses for your analytical and machine learning tools to access. The extractions are periodic, and users can customize all aspects of this pipeline, from what data they extract to the formats it needs to be transformed into, as well as what to load into the final data warehouse.
All relevant teams inside an organization need to be trained to be able to handle ETL pipelines correctly, as well as to make sure the pipeline has access to all necessary datasets.
ETL Pipelines in Action
Here at Data Pilot, we were approached by a Growth Marketing Agency. The company had acquired several e-commerce brands and faced issues with scalability due to its data stack not being robust enough. Where they previously had an established system to run things, they now needed something to consolidate data from various sources like Shopify, Google Analytics, Google Ads, and Facebook Ads.
Data Pilot revamped Growth Marketing Agency’s architecture by building an ETL pipeline on Google Cloud platform. A top-notch visualization tool accompanied this pipeline to support each e-commerce brand’s digital marketing and business teams.
As a result, their ETL pipeline’s costs were cut to 1/3 of the original, with a 99.9% data accuracy rate.
Manual or No-Code ETL?
While an ETL pipeline might sound simple in theory, these pipelines are often tasked with handling unimaginable amounts of data. Hundreds of variables need to be considered for the pipeline to function. This step is especially essential since any AI-based mechanism cannot function without the appropriate amount of high-quality data.
So then, how do you go about setting up an ETL pipeline for your business information systems? Currently, you have two options, establishing a no-code ETL pipeline using an already available platform like Talend or Informatica, or creating your own by collaborating with data engineers and ETL specialists through code. In the next section, we will weigh the pros and cons of both options to help you make the best decision possible for your organization.
Code or Manual ETL
Coding your ETL pipeline might seem like it's tricky business, but with the right approach and tools, the benefits are endless. For example, ETL pipelines can be built to be extremely scalable when using Python. Building your own ETL pipeline from scratch enables your business to create something bespoke for your organization, as opposed to using no-code ETL solutions that are not designed with your tech stack, current data setup, and other requirements in mind.
However, it certainly has its benefits if you do it right.
No-code solutions can facilitate your ETL pipeline. They are simple to use and easy to navigate. Using a no-code platform takes all the hassle of setting up an ETL solution from scratch away from a business but has its complications.
Tools for Manual and No-Code ETL
Code-based ETL can be done through python scripts, spark scripts, etc.
Manual ETL is regarded as the “traditional” way to do ETL, and most of it is done through directly writing code and developing the pipeline from scratch.
This is typically done through Python scripts, which is a file containing code written in Python. This file is meant to run like a program. Many other programming languages are also used to write code for manual ETL, and sometimes a combination of different languages might be required.
No-code ETL tools are designed to be easy, SaaS-based solutions to an organization’s ETL needs. Organizations worldwide provide no-code ETL solutions, like Talend, Stitch, Fivetran, Keboola, and Hevo.
Even within no-code ETL options, you can choose between multiple kinds. These include enterprise ETL tools created and managed by corporations, open-source ETL tools, and cloud-based options.
Notable no-code ETL tools include Talend, Stitch, Fivetran, Keboola, and Hevo.
Best Practices for Implementing ETL
Establishing an ETL pipeline is no simple task. As with all automation processes, there is a learning curve involving trial and error, continuous maintenance, and risk management. Some best practices for ETL pipeline implementation can help you diagnose and solve any errors that occur, from ensuring the availability of high-quality data for the pipelines to modulating your ETL code.
1. Input Data Incrementally
If you input too much data into your ETL pipeline, you run the risk of overloading it. The results will be subpar, and your pipeline will be slow to produce them. Therefore, the best way to process data through an ETL pipeline is to do it incrementally.
The key here is to split the data into parts and input them one by one. By doing this, you can ensure that results are produced quickly and any issues in the pipeline are caught in time.
Setting up checkpoints throughout your ETL pipeline is another helpful step you can take to ensure everything is running smoothly. Errors are not uncommon, especially at the initial stages of implementation, and checkpoints for errors can make it easier to catch where the error occurred.
Steps like this can save a lot of time and energy. Without setting up checkpoints in your pipeline, you might be forced to restart the process from step one.
3. Use a Data Observability Platform
Using data observability platforms like Monte Carlo is another great way to go above and beyond when it comes to error prevention and ensuring data security throughout ETL pipelines. Most observability platforms don’t just observe the data; they track data movement across various servers, tools, and platforms.
This allows concerned parties to ensure data security and ease in diagnosing issues within the pipeline, should they occur.
4. Maximize Data Quality
The quality of data you get at the end of the pipeline is severely affected by input quality, which is why ensuring the availability of high-quality data is essential. Any data being processed through an ETL pipeline needs to be free of repetitions, not mismatched, and free of any inaccuracies.
5. Code Modularization
Modularizing your code means structuring your ETL code into singular, reusable modules. This allows for the code to be reused in multiple processes.
Some benefits include easier unit testing, avoiding duplication in the code, and standardized processes throughout the pipeline.
Before implementing any kind of ETL solution, you need to have a very clear idea of what you want before the developmental stage. Building effective data pipelines is key to having a robust data architecture powering analytics across your organization.
Not only do you need to know what your goals for your ETL pipeline are at the beginning, but you also need to be able to anticipate any internal changes you will need to make in your operations while the ETL pipeline is being set up and be clear on what your budget is before you make your final decision.
Our expertise in data engineering sets us apart. We have a lot of experience in building robust data pipelines to consolidate data from different sources into a data warehouse. After building data pipelines, we spend a considerable amount of time in data validation to ensure there are no data accuracy issues. For data engineering, we have expertise in custom Python scripting, Keboola, Fivetran, Skyvia, Airbyte, Stitch, DBT and Dataform.