Let’s be real AWS Glue is powerful, but if you're not careful, it can quietly eat into your budget. I learned that the hard way.

Our ETL pipeline was fast, scalable… and shockingly expensive.

Here’s how I turned things around and reduced costs by nearly 60% without sacrificing performance.

☁️ The Problem: Fast Pipeline, Bloated Bill

When we first migrated to AWS Glue, everything was smooth. No server management, auto-scaling, and tight integration with S3, Athena, and more.

But after a few weeks, the cost charts looked scary.

A few jobs were running frequently. Some were processing small files. And others were idle for half the time they were billed.

🔍 Step 1: Audit All Glue Jobs

First, I listed all running jobs and grouped them by frequency, data size, and runtime.

What I found:

• Some jobs ran every 15 mins but processed a few MBs

• Others used G.1X workers by default — overkill

• No job bookmarks — so we were reprocessing old data unnecessarily

📌 Tip: Use AWS Cost Explorer with Glue as a service filter. It’s eye-opening.

⚙️ Step 2: Right-Size Workers

Many jobs were using G.1X or G.2X workers. But they didn’t need that much power.

I did test runs with Standard worker types and tuned memory configs.

🧠 What helped:

--conf spark.executor.memory=4g
--conf spark.driver.memory=2g

📉 Result: ~30% cost reduction just by resizing.

📆 Step 3: Rethink Job Scheduling

Jobs were scheduled by habit — every 15 mins or every hour — even when the data updated once a day.

I switched to:

• Event-driven triggers (S3, EventBridge)

• Daily/weekly cron expressions

• Conditional checks (don’t run if data size = 0)

📉 Result: ~10–15% cost saved by avoiding unnecessary runs.

🧹 Step 4: Enable Job Bookmarks (Where Possible)

One major problem: we were reading full datasets every time.

By enabling job bookmarks and filtering only new data, we drastically cut processing time and I/O.

📉 Result: ~10% cost reduction + faster job completion

🧱 Step 5: Use Glue for What It’s Best At

Glue is great for:

• Schema inference

• Large parallel data processing

• Managing Spark jobs serverlessly

But for lightweight transforms, file format conversion, or simple aggregations, we moved to:

• AWS Lambda

• Athena queries (scheduled)

• Fargate for short containers

📉 Result: Another ~5–10% drop in Glue costs

✅ Bonus Tips

• 🧪 Test in dev with sampling before full production runs

• 🧾 Tag jobs by team/project to trace high spenders

• 🔁 Turn on job retry limits — infinite retries = infinite charges

• 🛑 Always set a timeout (-timeout 10) to avoid stuck jobs

📊 Final Result: 60% Cost Cut, Same Output

By taking these 5 steps:

1. Auditing usage

2. Right-sizing workers

3. Smarter scheduling

4. Using bookmarks

5. Offloading lightweight jobs

We dropped our AWS Glue spend by over 60% — and made our data pipeline more efficient than ever.

💬 Your Turn

AWS Glue can be affordable — but only if you use it smartly.

🔽 Have you optimized your Glue setup? Got cost-saving tips or horror stories?

Drop them in the comments — let's help the next engineer avoid a painful bill!

#AWS #DataEngineering #Glue #CostOptimization #ETL #Serverless #BigData #CloudTips #LinkedInTech #GlueJobs

Let me know if you’d like this turned into a LinkedIn carousel or downloadable PDF!

Absolutely! Here's a story-driven, informative blog post titled:

💸 AWS Glue is Expensive? Here’s How I Cut ETL Costs by 60%

Let’s be real — AWS Glue is powerful, but if you're not careful, it can quietly eat into your budget. I learned that the hard way.

Our ETL pipeline was fast, scalable… and shockingly expensive.

Here’s how I turned things around — and reduced costs by nearly 60% without sacrificing performance.

☁️ The Problem: Fast Pipeline, Bloated Bill

When we first migrated to AWS Glue, everything was smooth. No server management, auto-scaling, and tight integration with S3, Athena, and more.

But after a few weeks, the cost charts looked scary.

A few jobs were running frequently. Some were processing small files. And others were idle for half the time they were billed.

🔍 Step 1: Audit All Glue Jobs

First, I listed all running jobs and grouped them by frequency, data size, and runtime.

What I found:

• Some jobs ran every 15 mins but processed a few MBs

• Others used G.1X workers by default — overkill

• No job bookmarks — so we were reprocessing old data unnecessarily

📌 Tip: Use AWS Cost Explorer with Glue as a service filter. It’s eye-opening.

⚙️ Step 2: Right-Size Workers

Many jobs were using G.1X or G.2X workers. But they didn’t need that much power.

I did test runs with Standard worker types and tuned memory configs.

🧠 What helped:

--conf spark.executor.memory=4g
--conf spark.driver.memory=2g

📉 Result: ~30% cost reduction just by resizing.

📆 Step 3: Rethink Job Scheduling

Jobs were scheduled by habit — every 15 mins or every hour — even when the data updated once a day.

I switched to:

• Event-driven triggers (S3, EventBridge)

• Daily/weekly cron expressions

• Conditional checks (don’t run if data size = 0)

📉 Result: ~10–15% cost saved by avoiding unnecessary runs.

🧹 Step 4: Enable Job Bookmarks (Where Possible)

One major problem: we were reading full datasets every time.

By enabling job bookmarks and filtering only new data, we drastically cut processing time and I/O.

📉 Result: ~10% cost reduction + faster job completion

🧱 Step 5: Use Glue for What It’s Best At

Glue is great for:

• Schema inference

• Large parallel data processing

• Managing Spark jobs serverlessly

But for lightweight transforms, file format conversion, or simple aggregations, we moved to:

• AWS Lambda

• Athena queries (scheduled)

• Fargate for short containers

📉 Result: Another ~5–10% drop in Glue costs

✅ Bonus Tips

• 🧪 Test in dev with sampling before full production runs

• 🧾 Tag jobs by team/project to trace high spenders

• 🔁 Turn on job retry limits — infinite retries = infinite charges

• 🛑 Always set a timeout (-timeout 10) to avoid stuck jobs

📊 Final Result: 60% Cost Cut, Same Output

By taking these 5 steps:

1. Auditing usage

2. Right-sizing workers

3. Smarter scheduling

4. Using bookmarks

5. Offloading lightweight jobs

We dropped our AWS Glue spend by over 60% — and made our data pipeline more efficient than ever.