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USD
I will build big data pipelines and process datasets using pyspark and sql
AI, Data, and Web3 Engineer
I am an ML & Data Engineer with a Master in Data & Intelligence from Université Claude Bernard Lyon 1. I specialize in bridging the gap between advanced AI research and scalable, production-ready soft...
About this Gig
Struggling with massive datasets or slow processing times?
I am a Data Engineer specializing in large-scale Big Data processing, ETL, and analytics. I build highly optimized data pipelines to ingest, clean, and transform gigabytes of data efficiently using PySpark and Python. Whether you need complex aggregations, geospatial mapping, or clean visualizations, I deliver production-ready code.
My Core Services:
- Big Data Pipelines: High-performance ETL workflows using Apache Spark, PySpark, and Python.
- Advanced Transformations: Optimized Spark SQL queries, complex window functions, UDFs, and large-scale joins.
- Data Integration: Cleaning and formatting structured/semi-structured data for downstream analytics.
- Geospatial Data: Processing location-based and time-series data.
- Visual Insights: Translating big data into actionable visualizations using Pandas and Matplotlib.
Tech Stack: Python | Apache Spark | PySpark | Spark SQL | Pandas | Matplotlib
Why Me?
I write clean, scalable, and fully documented code, ensuring your data operations are accurate and computationally optimized.
Please message me before ordering to discuss your dataset!
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Tools & Platforms:
Other
FAQ
Is my data safe and confidential?
Absolutely. To ensure complete privacy, I do not need access to your sensitive information. You can simply provide me with an anonymized or dummy dataset. I will build and test the pipeline using that, and deliver the final code for you to run securely on your actual data.
Can your code run on cloud platforms like Databricks, AWS, or GCP?
Yes. I specialize in writing robust, standard PySpark pipelines. Because the code is highly portable, you can easily execute the scripts I deliver locally, on Databricks, or submit them to your own cloud-managed Spark clusters like AWS EMR or Google Cloud Dataproc.
Can you handle multi-gigabyte or terabyte datasets?
Yes! That is exactly what Apache Spark is built for. I write optimized, distributed data pipelines specifically designed to process massive datasets that are too large for standard Pandas workflows.
What exactly will I receive upon delivery?
You will receive fully commented, production-ready code (as .py scripts or Jupyter Notebooks), plus clear documentation explaining how to run the pipeline and schedule the job.
