What Is the Role of LLM in Data Engineering Evolution?

Let’s face it: Data engineering is like playing Tetris, always moving objects around to fit them into the right places. The data is never static; pipelines, schemas, transformations, workflows, and flow are always puzzles that must be solved. Yes, it is an unmistakable kind of job; however, let me assure you, it is not always that you get to work in the glamour of the profession. Here’s Large Language Models (LLMs), the new hope of AI, which will help you. Of course, the LLMs have been seen creating texts, answering questions, writing codes, and so on, but how can these great models help the data engineers? 

In this blog, we will discuss the role of LLM in data engineering, along with their use cases and best practices.

Let’s dive in.

What Exactly Are LLMs?

If you are unfamiliar with the term, large language models are deep learning models that are pre-trained on huge volumes of plain text to produce natural language. Tools are GPT (hi there), Codex, etc. They are perfectly capable of writing essays, fixing code, and even generalizing simple workflows, and that’s where they can shine in data engineering.

Data Engineering Challenges We All Know

Data engineering is like constructing the framework for a huge building, like a skyscraper. It’s crucial, though; people seldom pay attention to it until an issue arises. Here are a few pain points:

1. Manual Data Cleaning: It’s the exhausting calls, late-night decoding, and encoding, and a ton of data cleansing where every process involves dealing with nulls, types, or duplicates.
2. SQL Queries: This is not an exaggeration because, unlike other programming languages, writing and optimizing SQL queries depends more on experience rather than the normative approach.
3. Schema Mismatches: I’m sure everyone who attempted to merge data from several sources experienced at least a couple of lost hours (or even days) dealing with the schema differences.
4. Monitoring Pipelines: It is quite a challenge always to ensure that ETL/ELT pipelines are in good condition.
5. Documentation: Taking time to document and assuring that the documentation is correct becomes typically the least of our priorities.

If you are nodding your head in agreement, you should continue reading because LLMs could be just what the doctor ordered.

Practical Use Cases of LLM in Data Engineering

Here’s where things get exciting. LLMs have the potential to automate, enhance, or even completely rethink the way we approach certain aspects of data engineering. Here are some practical ways LLMs can step in:

1. SQL Writing and Optimization

Let’s be honest: However, particularly when the queries have gotten longer, it becomes rather tedious to write SQL. With an LLM, you say what you want, and there it is in plain English! Based on your input, a query for you is created. 

For example:

You: “For example, somebody might come and say, ‘I require a query that would enable me to get the first ten customers by total sales from the table of sales.’”

LLM: Creates the SQL query while bringing in other compatibilities like join and aggregation.

Beyond writing SQL, LLMs can also:

Make performance suggestions such as indexing or rewriting joins.

The final role it can help with is debugging, where you can figure out why a query is not returning the desired results.

2. Data Cleaning Made Easy

Data cleaning is one of the job’s least glamorous (but most important) parts. An LLM can:

• Identify and suggest fixes for inconsistencies in the data.
• Generate code for tasks like deduplication, handling nulls, or standardizing formats.
• Explain the cleaning process step by step, making it easier to debug.

For example, you could ask: “How can I remove duplicate rows from a pandas DataFrame?”

And the LLM will provide you with clear, ready-to-use Python code to get the job done efficiently.

3. Helping with Schema Mapping

Generally, if datasets are being merged, then there is always a guaranteed clash of data schemas. It is also interesting that LLMs can analyze concerns and make schema mappings. For instance, the first table works with cust_id while the second one works with customer_id, and the LLM can suggest how to handle this.

4. Debugging and Monitoring Pipelines

Pipeline failures can be stressful. LLMs can:

• Analyze error logs to pinpoint issues.
• Suggest solutions, like fixing data type mismatches or adjusting pipeline steps.

Imagine feeding your error logs into an LLM and getting a response like:

“Your pipeline failed because the order_date column contains invalid dates. Try converting it to a datetime format.”

5. Generating Documentation

Let’s be honest! Writing documentation isn’t exactly anyone’s idea of fun, but it’s undeniably important. LLMs can take the burden off your shoulders by:

• Writing documentation from your code based on a certain set of predefined rules.
• Creating accessible/low-none technical descriptions of business processes and their various iterations.
• Procedures that help new teammates comprehend pipelines in record time.

For instance, you might input your pipeline structure into the LLM and then expect it to present an abstract on what to relay to your project manager.

How LLMs Could Change Data Engineering

These tools are so great, but they aren’t here to replace you – they are here to assist you. Here’s how the role of data engineers might evolve:

• Focusing on Big-Picture Problems: This leaves time for creating outstanding solutions and constructing strong architectures since repetitive work is already automated.
• Working with AI as a Partner: Specifically, the findings reveal that LLMs can be your sidekick when they are assigned the responsibility of doing routine work and allow the professional to formulate strategic plans.
• Making Data Engineering Accessible: They found that engineering time was being wasted due to non-technical team members having to manually complete many of the processes, while more complex instructions could be made to be easily programmable with LLM-powered tools, allowing engineers to assist with the harder tasks.

Best Practices for Using LLMs in Data Engineering

Okay, great. So you’re excited about LLMs; indeed, we should be, but let’s look at what a real head-scratcher here is. How can we leverage the heck out of them without getting singed? These are great tools; however, as with everything related to technology, these are not silver bullets. It’s time to explore some recommendations to guarantee that LLMs would be your MVP (Most Valuable Player).

1. Know the Limitations (This One Is Important Too!)

Now that’s out of the way, let me clarify that LLMs are intelligent but not infallible. Imagine them as helpful friends who often provide some factual inaccuracies.

• Always review the output: Before running any SQL query or attempting to fix a pipeline issue, double-check and glance at it. Trust, but verify!
• Be specific in your inputs: Aimlessly, you will get aimless answers; if you need specific information, be specific in your query. It is always helpful to offer more context; the more specific you can be, the better.

2. Play It Safe with Security

Just think about forwarding something important to an LLM; we are sure no one wants to experience that. Here’s how to avoid it:

• Don’t share private data: Use LLMs responsibly. There is nothing wrong with this setup; however, if you use highly confidential data, you might opt for private or local ones only.
• Set boundaries: Not all tasks need an LLM. When it comes to anything that is mission-critical, one must then ensure that compliance and security rules are met.

3. Train Them to Know Your World

Think of LLMs as interns; they come with good settings out of the box but improve tremendously when trained to work on the organization’s flow.

• Fine-tune them: There are always modifications that can be made if you find the LLM does not fit your organization’s exact needs, or if you can use your own data and processes. He was explaining to his assistant how he wanted his coffee to be brewed.
• Blend their smarts with yours: They’ll take care of the means, but the result-making touch and the fine details come from your strategies.

4. Don’t Lean Too Hard on Them

Of course, LLMs are excellent, but you don’t have to worry about them taking your job (though if they wanted to, they could). Treat them as instruments, not as aids.

• Keep learning: Of course, even if you get an LLM, you’ve got to have your feet on the ground because when there is a problem (and there always is), you’re it.
• Think of them as training wheels: It’s all done to ensure things run more smoothly; however, you push the bicycle.

5. Integrate Them into Your Process (Smartly)

Describing an LLM as the sous chef in your data kitchen is quite interesting. Besides, let them cut and slice while you design and create the look of the food.

• Automate the boring stuff: Cleaning data? Writing documentation? Let the LLM take care of it. It means you have other things that are more important to deal with.
• Stay involved: Automate this and keep an eye on the output nevertheless. Note that humans in the loop are the rule of thumb.

Of course, LLMs are not free of blemishes. Here are some limitations to keep in mind: you will not only be using LLMs as your ally, but also be able to minimize the dangers that fall with blind reliance on such LLMs. Don’t be scared of them. They are here to help, so work together to overcome those data engineering problems!

Wrapping It Up

Data engineering is changing, and LLMs are at the forefront of this process. You don’t just use them; it is as if you hired a new member of your team who is efficient in repetitive work, knows the right practices, and does not require rest. In essence, LLMs can help you with your work, whether writing SQL queries, cleaning data, or fixing pipeline bugs.

But do not forget, the magic is in how these tools are applied. They stand ready to assist you to get more out of your working day without doing more work. So, what do you think? Are LLMs the new ‘can’t-miss’ in your tool kit? Let’s look at this as a promising development and strive to bring data engineering to the next level, starting with the pipelines! To create efficient data pipelines seamlessly, sign up for Hevo’s 14-day free trial. 

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