Automating Data Pipelines: Generating PySpark and SQL Jobs With LLMs in Cloudera

With the rise of generative AI and large language models (LLMs), data engineers and analysts can now translate natural language directly into working PySpark or SQL jobs. 

By integrating LLMs into Cloudera Machine Learning (CML) and executing workloads on Cloudera Data Engineering (CDE) with Iceberg table formats, enterprises can accelerate data pipeline development, improve collaboration, and simplify access to large-scale analytics.

Architecture Overview

Key Components of Cloudera Data Platform (CDP)

Cloudera is a hybrid data platform designed for secure data management, advanced analytics, and machine learning at scale. Here are its core components:

Together, these let you ingest, govern, analyze, and learn from data in a secure and scalable way.

How Large Language Models (LLMs) Work

LLMs like GPT, LLaMA, or Mistral are deep learning models trained on massive text corpora to understand and generate human-like language. Here’s a simplified breakdown:

 LLMs can be used in Cloudera to:

• Translate natural language to PySpark/SQL.
• Generate documentation or pipeline code.
• Assist with data exploration using conversational interfaces.
• Enforce policies via smart governance assistants.

LLM Inside Cloudera Workflow

Step-by-Step Implementation Guide

Step 1: Set Up Your Environment on Cloudera

Step 2: Deploy a Pre-Trained LLM in CML

Use CML to host a model that translates natural language to code.

• Use a pre-trained LLM like GPT-4, LLaMA 2, or a domain-specific model
• Provide a prompt template and integrate with Streamlit or Flask for the UI

Options

• Local model: Use LLaMA 2, Mistral, or Falcon (quantized versions work well).
• API model: OpenAI (GPT-4), Cohere, or Anthropic via REST.

Sample Code to Set Up the Model (CML Notebook)

from transformers import pipeline
pipe = pipeline("text2text-generation", model="bigcode/starcoder", max_new_tokens=200)

def generate_code(prompt):
    result = pipe(prompt)
    return result[0]['generated_text']

Step 3: Build the Prompt Engineering Logic

Define a system prompt and examples that instruct the LLM how to convert natural language to PySpark or SQL.

Prompt Template Example 1

You are a data engineer assistant. Convert the following natural language request into PySpark code that queries Iceberg tables.

Input: "Calculate weekly revenue per region over the last 12 weeks."

Output

from pyspark.sql import SparkSession
from pyspark.sql.functions import col, sum, weekofyear

spark = SparkSession.builder.appName("RevenueReport").getOrCreate()
df = spark.read.format("iceberg").load("sales_db.transactions")

result = df.filter(col("transaction_date") >= date_sub(current_date(), 84)) \\
           .groupBy(weekofyear("transaction_date"), "region") \\
           .agg(sum("amount").alias("weekly_revenue"))

result.write.format("iceberg").mode("overwrite").save("sales_db.weekly_revenue")

Prompt Template Example 2

"Summarize daily website traffic by country and device type for the past 7 days."

Output

from pyspark.sql import SparkSession
from pyspark.sql.functions import col, count, to_date

spark = SparkSession.builder.appName("TrafficSummary").getOrCreate()
df = spark.read.format("iceberg").load("analytics_db.web_traffic")
summary = df.filter(to_date(col("timestamp")) >= date_sub(current_date(), 7)) \
            .groupBy("country", "device_type") \
            .agg(count("session_id").alias("visits"))
summary.write.format("iceberg").mode("overwrite").save("analytics_db.daily_summary")
3. Submit Jobs to CDE

Step 4: Build a UI for Natural Language Input (Optional)

Use Streamlit or Flask on CML to build a simple web interface:

Streamlit Example

import streamlit as st
user_prompt = st.text_input("Describe your data pipeline:")
if st.button("Generate Code"):
    code = generate_code(user_prompt)
    st.code(code, language='python')

Step 5: Submit Generated Code to CDE for Execution

Use the CDE CLI or API to create and run Spark jobs with the generated script.

CDE CLI Example

cde job create --name weekly-revenue-job \
  --type spark \
  --mount-1-path code.py \
  --script code.py
cde job run --name weekly-revenue-job

Step 6: Monitor Jobs and Logs

CDE offers:

• Logs for Spark execution
• Retry and scheduling options
• Resource management (YARN or Kubernetes)

Step 7: Apply Governance With SDX (Ranger + Atlas)

Enable Time Travel With Iceberg

Iceberg tables support rollback and versioned queries. Example:

-- Query a snapshot from 7 days ago
SELECT * FROM sales_db.weekly_revenue TIMESTAMP AS OF current_timestamp() - interval 7 days;

Benefits

Conclusion

This LLM-powered workflow redefines how data engineering is approached in enterprises using Cloudera. With natural language as the new interface, data teams can reduce friction, cut development time, and ensure governance at scale. By combining Cloudera’s robust data engineering and governance capabilities with the power of large language models, organizations can turn natural language prompts into actionable PySpark or SQL jobs. 

This integration accelerates data pipeline development, empowers non-technical users, and ensures enterprise-grade governance through SDX. The result is a streamlined, secure, and intelligent approach to building data products, enabling faster insights, greater collaboration, and scalable automation across the modern data stack.