Big Data
Data Engineering
High-throughput distributed computing, real-time streaming, and resilient data architectures.
Big Data Capabilities
Modern data architectures require more than just running scripts on raw files. We design distributed, fault-tolerant pipelines using Apache Spark and Apache Kafka that scale reliably under intense data workloads.
Core Competencies
- Apache Spark Optimization: Designing strongly typed Spark Dataset workflows in Java and Scala, partition tuning, memory management, and caching strategies.
- Event Streaming with Apache Kafka: Building real-time event backbones, topic modeling, partition balancing, schema registry integration, and consumer group design.
- Complex ETL / ELT Pipelines: Ingesting, transforming, and persisting structured and semi-structured datasets into modern data warehouses and distributed stores.
- Type Safety in Distributed Systems: Preventing serialization and schema mismatch bugs at compile time through typed case classes and robust encoders.
Featured Spark & Data Topics
Type-Safe Spark Joins & Datasets
Techniques for leveraging the Spark Dataset API to enforce type safety on joins and transformations, preventing silent runtime failures.
JSON Serialization in Spark
Clean patterns for serializing complex nested structures into database columns with minimal performance overhead.
Stream Processing & Resiliency
Exactly-once and at-least-once processing guarantees across Kafka and distributed consumers.