For the better part of a few decades, businesses have increasingly used information about customers to predict future behaviors, customize their offerings, drive higher profits, and improve user experiences.
But doing this effectively requires large volumes of high quality trusted data, which has given rise to one of the cornerstones of modern data engineering, ETL (extract, transform, load) pipelines. After building an ETL pipeline, enterprises can extract data from disparate sources, transform it in various ways, and load it into systems for downstream consumption, where it can be used to drive tremendous value for processes like analytics and business intelligence.
The AI data pipeline, a critical framework consisting of various tools and processes for efficiently managing data used in AI applications, further enhances this by structuring data handling from ingestion to training, impacting the performance and scalability of AI systems.
Of course, this has its own challenges. Building an ETL pipeline can be complex, costly, and reliant upon highly skilled data engineering talent. This may be why so many are excited by the emerging role played by generative AI in ETL. Generative AI makes it easier to automate major parts of the ETL pipeline development process, increases data engineers' efficiency, and enables less technical data integration users to build ETL pipelines without handcoding.
This article will cover the benefits of building ETL pipelines with AI, explain how this process works, and offer guidance on leveraging AI for ETL workflows effectively.
The Core Concepts of ETL
As already mentioned, “ETL” stands for “extract, transform, load,” and by implication, an ETL pipeline is just a set of processes meant to handle each of these steps. An ETL pipeline typically pulls data from a data warehouse, a data lake, social media sites, or an API (“extract”); changes it by filtering, aggregation, data cleansing, data preprocessing or changing its format (“transform”); and pushes it into models, dashboards, or models (“load”).
Given how much data exists in the world now and how powerful it can be when used correctly, the ability to build an ETL pipeline has become an important competitive factor for businesses in many domains.
To further clarify the role of ETL pipelines, it’s helpful to distinguish them from ETL processes. As the word “process” is more generic, it can refer to any effort to move, change, and improve data. For its part, the word “pipeline” is stricter and refers specifically to a coordinated series of processes designed to extract, transform, and load data in a structured and repeatable manner.
The Traditional Challenges in Building an ETL Pipeline
An ETL pipeline makes it far easier to work with large amounts of data spread out over many different sources, but that doesn’t mean it’s without its challenges. Dealing with multiple data sources and raw data can add complexity to the process. Depending on the circumstances, building an ETL pipeline may require:
Manual writing of thousands of lines of code, sometimes in several different programming languages
Relying on costly, specialized data engineers with expertise in writing this code, which could lead to a knowledge gap if that expertise isn't shared across the team
Grappling with time-consuming inefficiencies and an inability to scale quickly when required
For these reasons, it’s always exciting when major new tools emerge that can reduce the burdens associated with the ETL process.
Building an ETL Pipeline With AI
That brings us to AI for ETL. “Artificial intelligence is among the most exciting developments in large-scale data management in a long time,” says Preetam Kumar, Director of Product Marketing at Informatica. “The current crop of generative AI tools for ETL can take tasks that once required weeks and make them doable in just a few hours. Some of them require little to no code, and many offer the ability to monitor data dynamically, so that there’s full visibility into what’s happening and it’s possible to make rapid adjustments if needed.”
Let’s explore these capabilities of AI further, specifically in how they simplify ETL.
Automation
AI for ETL can substantially reduce the burden required to build ETL data pipelines. Whether a developer is attempting to migrate data from a particular database, develop connectors for different data stores, or programmatically execute business-critical data transformations, current-generation AI tools can automate these tasks, making the entire process faster and more efficient. ETL pipelines transform data by ensuring data cleanliness and reliability, which is essential for meaningful decision-making.
Recommendations for Problem-Solving Next Best Transformation
A subtler use case of AI for ETL is guiding data engineers and programmers rather than simply doing a task directly. To illustrate, such a tool might notice that a particular data set has a problem that needs to be addressed, suggest ways of combining an existing data set with another, or point out that a pipeline is breaking at a particular point because of a tricky transformation.
No-Code and Low-code Solutions Using Natural Language Processing
One of the major innovations from recent years has been the development of low-code and no-code platforms, which are essential for preparing data for training and deploying machine learning models. With the rise of large language models in 2023, AI-driven ETL tools now offer similar capabilities. It’s possible to have ChatGPT create a program to move data from the Oracle cloud to Snowflake, for example, or write RegEx to execute difficult data transformations — all without requiring the user to write a single line of code.
Data observability
Of course, building an ETL pipeline with AI is just one part of the story; it’s also necessary to monitor the pipeline to check for breakages or changes to the underlying data.
With the right data monitoring procedures in place, it becomes easier to detect these situations and take the appropriate action. Some AI for ETL tools, Kumar points out, will even allow developers to detect schema changes in the development environment and replicate them automatically in the production environment, reducing engineering overhead as well as the potential for errors to make it out into the world.
All told, AI for ETL can result in substantial savings in time and effort. As Kumar puts it, “We’ve seen stories of companies replacing 50 engineers with two or three for the same volume of work, just by using AI for ETL effectively.”
Kumar suggests, “One way to think about this phenomenon is as an inversion of the familiar 80/20 rule. Whereas data engineers used to spend 80% of their time preparing or integrating data, now that’s been reduced closer to 20%. This means the bulk of everyone’s focus can now go to doing highly productive strategic work for the company.”
What to Look for When Using AI for ETL
Not all AI tools are created equal. These are the “five E’s” to seek in an AI for ETL solution:
Easy: An AI for ETL pipeline should be exceptionally easy to use. The best are user-friendly, GUI-based offerings with drag-and-drop functionality to build data pipelines. It’s also a good idea to look for pre-built templates to jumpstart data engineering projects and rich metadata of data pipelines for quick and efficient setup.
Efficient: Any option should be highly efficient. Look for a platform that features intelligent recommendations to guide data engineers through the design process. Superior still would be a general-purpose data management assistant designed to simplify many data-related tasks by providing an LLM-based natural language interface that allows users to interact with data conversationally.
Cost-Effective: Budget is usually top of mind, and it will be here too. The AI-powered ETL tool of choice should have real-time cost monitoring and optimization, “metered” pricing based on consumption (so a team only pays for what it uses), and detailed insights into job-specific resource consumption.
Everywhere: Another thing to check for is that a platform is cloud agnostic, and can work on cloud platforms like AWS, Azure, Google Cloud, and Oracle. This will allow a team to seamlessly integrate with diverse cloud infrastructures for maximum flexibility.
Everyone: Finally, the right tool should be inclusive for all skill levels. For coders, it should offer open, embeddable, and extendable tools to optimize their workflows; for non-coders, it should offer the ability to create data pipelines without handcoding expertise.
AI: The Next Frontier in Data Management
ETL pipelines are a crucial part of the machinery used by data-driven organizations for a reason: they make managing the huge quantities of data modern enterprises rely on much simpler than it otherwise would be. AI data pipelines and AI pipelines play a critical role in automating data handling, real-time processing, and collaboration, thus enhancing the overall effectiveness of AI workflows.
Integrating an AI solution into a broader Master Data Management (MDM) strategy has the power to revolutionize business processes by improving data accuracy, enhancing operational efficiency, and enabling more intelligent customer experiences, digital commerce, supply chain management, and more.
Informatica’s AI-powered tool for building ETL pipelines was built around the 5 “E”s mentioned above. At the core of this solution is CLAIRE, Informatica’s AI engine, which leverages advanced artificial intelligence to instantly automate thousands of data management tasks, freeing teams up to scale without limit.
