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I will design big data models and etl pipelines using pyspark and databricks
Data Engineering Expert and Cloud Solutions Architect
Experienced Azure data engineer with 13+ years building scalable data solutions using Microsoft Fabric, Azure Data Factory (ADF), Azure Data Lake, and Synapse Analytics. I also work across Snowflake, ...
About this Gig
Process petabytes of data at lightning speed with optimized PySpark models and Databricks pipelines that scale infinitely.
Overwhelmed by massive datasets that crash traditional systems? Need real-time processing that handles billions of records effortlessly? You've found your big data architect.
What You'll Get:
- Scalable PySpark data models and transformations
- Optimized Databricks cluster configurations
- Delta Lake architecture for ACID transactions
- Real-time and batch processing pipelines
- Performance-tuned Spark SQL queries
- Cost optimization strategies and monitoring setup
My Big Data Expertise:
With 13+ years architecting Spark solutions, I've built pipelines processing 500+ TB daily for tech giants, achieving 10x performance improvements through advanced optimization techniques and cluster tuning.
Technologies I Master:
- Platforms: Databricks, Apache Spark, Delta Lake, MLflow
- Languages: PySpark, Scala, Spark SQL, Python
- Optimization: Catalyst optimizer, partitioning, caching strategies
Other Data Engineering Services I Offer
FAQ
How do you optimize PySpark jobs for maximum performance and cost efficiency?
I implement advanced techniques including intelligent partitioning, broadcast joins, predicate pushdown, column pruning, and dynamic resource allocation to minimize processing time and cluster costs.
Can you design pipelines that handle both batch and streaming data?
Yes! I create unified architectures using Databricks Structured Streaming and Delta Lake that seamlessly process both batch historical data and real-time streams with exactly-once processing guarantees.
How do you ensure data quality and reliability in big data pipelines?
I implement comprehensive data validation frameworks using Delta Lake's schema enforcement, data quality checks, automated testing, and monitoring systems that catch and handle data anomalies.
What's your approach to handling data schema evolution in big data models?
I design schema-agnostic pipelines using Delta Lake's schema evolution capabilities, automatic schema inference, and backward compatibility strategies that adapt to changing data structures seamlessly.
How do you optimize Databricks clusters for different workload types?
I configure clusters based on workload characteristics - autoscaling for variable loads, spot instances for cost optimization, GPU clusters for ML workloads, and memory-optimized instances for complex transformations.
