Large Language Models (LLMs) are redefining what’s possible in data engineering, but beyond the hype, how do you integrate them effectively? For engineers building real-time applications where a streaming database like RisingWave serves as the central hub for ETL, this question is critical.

While RisingWave excels at processing structured data streams at scale, much of the valuable data flowing through these pipelines—from user reviews to IoT sensor logs—is unstructured. The answer isn’t to replace your high-performance streaming database, but to augment it.

The future of data processing is a hybrid model where RisingWave handles the real-time ETL, and LLMs provide on-the-fly intelligence. This guide offers a pragmatic blueprint for building this powerful combination.

The “Sweet Spot”: Where LLMs Excel in Data Pipelines

LLMs are your go-to solution when the logic is complex, fuzzy, and language-based—tasks that are difficult to express with traditional rule-based systems. Think of this as “taming the unstructured beast.”

Extraction and Structuring

LLMs can parse unstructured text and return structured data, going beyond simple regex to true semantic understanding. For example, imagine a pipeline ingesting raw email support tickets. An LLM can be tasked to extract key information into a clean, structured format.

Before (Unstructured Input):

“Subject: Urgent: Order #G-45832 Not Delivered. I am writing because my order, G-45832, which was supposed to arrive last Friday, has still not been delivered. The tracking info is stuck. This is for the ‘Pro-Grade Blender X2’ from Acme Corp, and it’s really frustrating. - John Doe”

After (Structured JSON Output):

{
"ticket_id": "G-45832",
"customer_name": "John Doe",
"issue_summary": "Order not delivered",
"product_mentioned": "Pro-Grade Blender X2",
"sentiment": "negative",
"urgency": "high",
"vendor": "Acme Corp"
}

A traditional approach would require a brittle combination of regex for the order number and keyword searches for product names. The LLM, however, understands the context to infer sentiment, urgency, and the role of each entity.

Transformation and Enrichment

This is about adding structure and context to unstructured data. For example, an e-commerce platform can use an LLM to process user-submitted product titles and descriptions into a standardized, feature-rich catalog entry.

Before (Unstructured Input):

“Men’s blue tee, 100% soft cotton, short sleeves, V-neck style. Great for summer.”

After (Structured & Enriched JSON Output):

{
"product_name": "Men's Blue T-Shirt",
"attributes": {
"material": "cotton",
"color": "blue",
"neck_style": "v-neck",
"sleeve_length": "short",
"gender": "men's"
},
"suggested_category": "Apparel > T-Shirts",
"suggested_tags": ["summer", "casual", "basics"]
}

Here, the LLM not only extracts the explicit attributes (color, material) but also normalizes them and enriches the record by suggesting a likely product category and relevant search tags.

Natural Language Interface

LLMs can act as a bridge between human language and machine language, democratizing data access. This allows a non-technical user to query data directly.

Before (User’s Natural Language Query):

“Show me the top 10 selling products in the EU region during the last quarter.”

After (LLM-Generated SQL Query):

SELECT
p.product_name,
SUM(s.quantity) AS total_quantity_sold
FROM
sales AS s
JOIN
products AS p ON s.product_id = p.product_id
WHERE
s.region = 'EU'
AND s.sale_date >= DATE_TRUNC('quarter', CURRENT_DATE) - INTERVAL '3 months'
AND s.sale_date < DATE_TRUNC('quarter', CURRENT_DATE)
GROUP BY
p.product_name
ORDER BY
total_quantity_sold DESC
LIMIT 10;

This capability empowers business users to perform self-service analytics without needing to learn SQL or wait for a data analyst to build a report.

When to Avoid LLMs in Your Pipeline

Using an LLM for the wrong task is like using a sledgehammer to crack a nut—it’s expensive, slow, and unpredictable. Here are the red flags.

High-Volume, Low-Complexity Tasks

For moving large volumes of structured data, traditional tools are orders of magnitude cheaper and faster. You should avoid using an LLM to simply convert a 1TB CSV file to Parquet when a simple script is far more efficient.

When Every Digit Matters

For financial transactions, scientific calculations, or any process where 100% accuracy and reproducibility are non-negotiable, the probabilistic nature of LLMs is a liability. These tasks, like calculating financial balances or validating transaction integrity, require deterministic logic.

When Real-Time is a Must

The inherent latency of most large LLMs makes them unsuitable for pipelines where millisecond-level processing is critical, such as in a real-time fraud detection system for high-frequency trading.

The Hybrid Blueprint: A Practical Integration Strategy

The smartest way to integrate LLMs is to treat them as a specialized, isolated step within a larger, traditional pipeline. This gives you their power without sacrificing the stability of your core workflows.

Isolate the LLM task. Use traditional ETL/ELT tools for initial data ingestion and pre-processing. 1.

Dispatch only the relevant data. Route only the specific fields that require language understanding (e.g., a free-text comment column) to an LLM service or API. 1.

Validate the output. Crucially, always implement a validation layer after the LLM call. This could be schema validation (did the LLM return valid JSON?), rule-based checks, or even filtering based on a confidence score from the model. 1.

Define a fallback. If the LLM fails or returns low-quality output, what’s the next step? The pipeline could halt, route the data to a human-in-the-loop for review, or simply pass it through with null values. 1.

Merge the results. Join the LLM-processed data back with the main, traditionally-processed data stream. 1.

Cache everything. Caching LLM outputs for identical inputs dramatically reduces costs and latency while improving determinism for repeated queries.

Conclusion: Your LLM as a Co-pilot

Think of an LLM as a powerful co-pilot for your data pipelines. It can handle the complex, nuanced, and language-heavy tasks that require “understanding,” while the reliable, deterministic pilot—your traditional ETL/ELT engine—handles the core flight path. For real-time applications, this “pilot” needs to be a robust streaming database like RisingWave, capable of processing and structuring massive volumes of data with speed and reliability. This collaborative, hybrid approach is the key to unlocking the true potential of LLMs in your data architecture, allowing you to build smarter, more capable, and more valuable pipelines.