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You will get optimize or build your ETL pipeline using Python, SQL, AWS, GCP

Abdul M.Status: Offline
Abdul M.
Preview image

Let a pro handle the details

Buy Other Data Analysis & Reports services from Abdul, priced and ready to go.
Abdul M.Status: Offline
Abdul M.
Preview image

Let a pro handle the details

Buy Other Data Analysis & Reports services from Abdul, priced and ready to go.

Project details

Most ETL pipelines don’t break because of complexity.
They break because of:

-Bad SQL
-Memory issues
-Wrong joins
-Duplicate data
-Disk space errors
-Glue job failures
-Lambda timeouts
-Redshift locking
-S3 partition mistakes
-Poor incremental logic

If your pipeline is:

❌ Running too slow
❌ Failing in AWS Glue
❌ Giving duplicate data
❌ Causing DiskFull or memory errors
❌ Not loading incrementally
❌ Breaking after deployment

I can fix it.

I have experience with:
-SQL optimization (Postgres, MySQL, Redshift)
-Python ETL scripts
-AWS Glue (Spark + Python Shell)
-AWS Lambda
-S3 partitioning strategies
-Incremental load logic
-CDC logic (is_current flag patterns)
-API to RDS pipelines
-GCS & BigQuery
-Power BI & Looker Studio reporting layer
Data Tool
SQL
What's included
Service Tiers Starter
$80
Standard
$200
Advanced
$300
Delivery Time 2 days 5 days 10 days
Number of Revisions
UnlimitedUnlimitedUnlimited
Source Code
Abdul M.Status: Offline

About Abdul

Abdul M.Status: Offline
AWS ETL Specialist | Glue & Redshift Optimization | Fixing Broken Pipe
Bahawalpur, Pakistan - 10:51 pm local time
If your AWS Glue job is failing, your SQL queries are slow, or your ETL pipeline is producing duplicate or inconsistent data — I can help.

I specialize in fixing and optimizing data pipelines built with Python, SQL, and AWS services. Most data systems don’t fail because they’re complex. They fail because of inefficient queries, incorrect incremental logic, poor partitioning, or missing error handling.

I focus on diagnosing the root cause and implementing clean, scalable solutions.

What I Help With

• Fixing AWS Glue job errors (memory issues, DiskFull, timeout failures)
• Optimizing slow SQL queries (Postgres, MySQL, Redshift)
• Building incremental ETL pipelines with proper CDC logic
• Removing duplicate loads and fixing broken joins
• API → S3 → RDS / Redshift data pipelines
• S3 partitioning strategy and performance tuning
• Improving ETL logging and monitoring
• Debugging Lambda-based data workflows
• Data warehouse optimization
• Preparing clean datasets for Power BI & Looker Studio

Technical Stack
- Python (Pandas, PySpark)
- SQL (Postgres, Redshift, MySQL)
- AWS Glue (Spark & Python Shell)
- AWS S3
- AWS Lambda
- AWS RDS
- Amazon Redshift
- GCS & BigQuery
- Power BI
- Looker Studio

My Approach
- Understand the failure or performance issue
- Analyze execution plans and pipeline flow
- Identify inefficiencies in queries or data movement
- Implement optimized logic (incremental loads, partitioning, indexing, batching)
- Ensure the solution is scalable and maintainable
I don’t just patch errors — I improve the structure so the issue doesn’t return.

Ideal Clients

• Startups scaling their data systems
• SaaS companies facing pipeline instability
• Teams migrating to AWS
• Businesses struggling with slow reporting

If your pipeline is breaking, slow, or poorly structured — let’s fix it properly.

Steps for completing your project

After purchasing the project, send requirements so Abdul can start the project.

Delivery time starts when Abdul receives requirements from you.

Abdul works on your project following the steps below.

Revisions may occur after the delivery date.

Understand and deliver

Review the work, release payment, and leave feedback to Abdul.