I Wasted 100 Hours on ETL Bugs — How Copilot Slashed My Data Pipeline Time by 55%

Author(s): Navya Sravani Jammalamadaka

Originally published on Towards AI.

Hey data engineers — ever stared at a Spark job log for hours, cursing schema mismatches while your coffee goes cold? 😩

Last quarter, I did. 100 hours. Gone. Poof. Debugging ETL pipelines in AWS Glue. One job took 3 full days to fix a sneaky null pointer in a nested JSON parse. Another? OOM errors from bad partitioning on a 10TB dataset. StackOverflow was my therapist, but it wasn’t cutting it.

Before Copilot:

• Manual hell: Reproduce bugs locally with spark-submit, tweak configs, pray.
• Repetitive drudgery: Copy-paste boilerplate for UDFs, error handling, optimizations.
• Time sink: 200 hours/month on pipelines → 50% debugging. Deployments? Weekly nightmares.
• Result: Burnout + delayed dashboards for the biz.

Then, I flipped the script with GitHub Copilot in VS Code. Not hype — real 55% time slash. Pipelines are now built in days, not weeks. Debugging? Seconds.

The Before/After Glow-Up

BEFORE (No Copilot):

# My old PySpark job - 500+ LOC of pain
df = spark.read.json("s3://bucket/raw/")
# 2 hours later... schema inference FAILS
df = df.withColumn("parsed", from_json(col("json_col"), schema)) # CRASH
# OOM on join: df.join(other_df, "id") # 4GB spill -> timeout

AFTER (Copilot Magic):

• Type a comment like // Fix JSON parse with error handling → Full UDF generated.
• One prompt: Pipeline from raw S3 → Glue Catalog → Athena-ready in <10 mins.

Proof? My last 10TB pipeline: 4 hours vs. 2 weeks prior. Copilot autocompletes Spark idioms, spots anti-patterns, and writes tests.

5 Prompt Hacks for AWS Glue Warriors

Hate repetitive code? Copy-paste these into Copilot (hit Tab to accept). Tailored for PySpark in Glue.

1. Schema Inference Nightmare Fix

Prompt:

// AWS Glue PySpark: Auto-infer schema from messy S3 JSON, handle nulls/malformed rows, output to Glue Catalog
df = spark.read...

Output: Bulletproof spark.read.option(“mode”, “PERMISSIVE”).json() + dynamic schema via spark.read.option(“inferSchema”, “true”).

2. OOM on Partitioning/Joins

Prompt:

// Optimize Spark join for 10TB datasets in Glue: Broadcast small DF, repartition by join key, avoid shuffle explosion
df1.join(df2...

Output: broadcast(df_small).join(df_large.repartition(“key”)) + spark.conf.set(“spark.sql.adaptive.enabled”, “true”).

3. Nested JSON Hell

Prompt:

// Parse deeply nested JSON in PySpark Glue job: Explode arrays, flatten structs, handle missing fields with coalesce
from pyspark.sql.functions import *
df.withColumn("nested"...

Output: Chained get_json_object + explode + coalesce = Flat DF ready for Redshift.

4. Nulls Blowing Up Aggregations

Prompt:

// Robust null handling in Spark aggregations: Fill NA by group, drop rows with >50% nulls, add quality metrics column
df.groupBy("category").agg...

Output: fillna(0) + filter(sum_nulls / count < 0.5) + DQ score col.

5. Performance Tuning Boilerplate

Prompt:

// Full AWS Glue job template: S3 input -> transformations -> write Delta/Parquet partitioned, with monitoring metrics
import sys
from awsglue...

Output: Complete job script with GlueContext, job.commit(), CloudWatch metrics, and auto-scaling configs.

Your Turn: Level Up Today

Engineers: Stop wasting hours. Copilot isn’t “cheating” — it’s your new senior dev.

Try this prompt RIGHT NOW (in VS Code + Copilot):

// Quick AWS Glue fix: Debug why my Spark job fails on "Cannot resolve column" error - add full error handling

Tag me @navya-jammalamadaka on LinkedIn with your before/after! 🚀 Let’s crowdsource more hacks.

What’s your worst ETL bug story? Drop it below. 👇

#DataEngineering #AWSTGlue #Spark #Copilot #ETL

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI