Since the advent of the internet and modern technology, consumer and general data have become invaluable to organizations worldwide.
Companies today must ingest vast amounts of data from numerous sources, utilizing either no-code or manual ETL pipelines. When a business decides to automate its operations, the first issue they often face is not having data in a usable format for automation, AI training, or similar purposes. The solution lies in establishing ETL pipelines and data warehouses.
The ETL (Extract, Transform, Load) process is vital for bringing clean, usable data into operational systems, especially since 80-90% of generated data is unstructured.
But how do you establish an ETL pipeline, the process behind it, and most importantly, what does it take?
What is an ETL Pipeline?
ETL stands for extract, transform, load, and an ETL pipeline processes various datasets, sorting them before storing them in a data warehouse. Once stored, this data can be used for training and retraining algorithms, aiding executive decision-making, performing predictive analyses, and much more.
ETL is the foundation of all machine learning and data analytics workstreams. If the data is raw and unorganized, it cannot be used to train any AI, or for any business automation processes. 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 handle ETL pipelines correctly and to ensure they have access to all necessary datasets.
Should I Go for 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.
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 may seem challenging, but with the right approach and tools, the benefits are immense. For instance, using Python can make your ETL pipelines highly scalable. Building your own ETL pipeline from scratch enables your business to create something tailored 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.
It certainly has its benefits if you do it right.
Pros
- Flexibility and Performance Optimization: One of the main advantages of manual ETL is its flexibility and customization capabilities. When you’ve written your code, you can adjust it to handle any task until it meets your needs.
- Higher Data Accuracy: Code-based ETL offers higher data accuracy than no-code ETL because it is tailored to your organization’s specific needs and requirements.
- Cheaper Than No Code ETL: Hiring an in-house ETL engineer can be costly for businesses, both in terms of money and time. Here, you can have the flexibility of manual ETL without the hassle of hiring and training an in-house ETL expert by outsourcing the ETL development process to a reputable tech firm or agency. This will help you keep your costs down and give you the tailor-made results you want for your ETL pipeline if you are dealing with very niche datasets, or if you want something that already established ETL platforms can’t provide.
Cons
- Complex Development Process: The ETL development process is complex and time-consuming. Writing code from scratch is challenging and given the importance of your ETL pipeline to business automation, there is no room for error.
- Harder to Operate: Your manual ETL pipeline will likely be managed only by your in-house ETL expert, making it difficult for non-technical staff to navigate and understand it. With high resistance to change in the workplace, it becomes essential to simplify the process for all users.
- Code Maintenance: Maintaining your code and keeping it running smoothly can be a challenge, especially when issues in speed and productivity may arise.
No-Code ETL
No-code solutions can streamline your ETL pipeline, offering simplicity and ease of use. While they eliminate the hassle of building an ETL solution from scratch, no-code platforms come with their own set of complications.
Pros
- Easy to Operate: The biggest benefit of using a no-code ETL solution is that it is simple and easy to operate. You don’t need to worry about any of the coding, and your solution is immediately ready for implementation.
- Building Large Pipelines is Simple: For larger datasets, no-code ETL pipelines are the clear solution. They are simple to use and typically avoid the speed and lag issues common with manual ETL pipelines.
- User-friendly UI and Workflow: The user-friendly UI of no-code ETL pipelines makes it easier to integrate its usage into the workflow process for your teams.
Cons
- Limited Customization: No-code ETL lacks the customization of manual ETL pipelines, which is a major reason organizations still choose manual ETL despite the ease of use offered by no-code ETL.
- Scalability is a Challenge: Unlike other no-code tech solutions, the scalability of a no-code ETL pipeline depends on its platform-specific specifications.
Case Study: Data Pilot built an ETL pipeline for a marketing agency, cutting costs by two-thirds with 99.9% accuracy
Data Pilot was approached by a Growth Marketing Agency. The company had acquired several e-commerce brands and was facing issues with scalability as its data stack was not robust enough.
They needed to consolidate data from sources like Shopify, Google Analytics, Google Ads, and Facebook Ads into a single system.
To solve this, 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.
The Result: their ETL pipeline’s costs were cut to 1/3 of the original, with a 99.9% data accuracy rate.
(P.S. To learn more about data consolidation, here is a comprehensive guide.)
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.
2. Checkpointing
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.
Final Verdict
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 that is 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 data engineering expertise sets us apart. We excel in building robust data pipelines that consolidate data from various sources into a data warehouse. Moreover, we dedicate our time to data validation to ensure the data is accurate and actionable. Our data engineering skills include custom Python scripting, Keboola, Fivetran, Skyvia, Airbyte, Stitch, DBT, and Dataform.