Processes Terabytes To Petabytes Of Event Logs And Transactions Within Batch Windows Or Near-real-time SLAs.
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Skills
Big Data (Spark/Hadoop)
Big data with Spark and Hadoop is the ability to design and run distributed pipelines that store, process, and analyze datasets too large for single machines. It matters because reliable, scalable processing enables timely analytics and model training while controlling cost, latency, and operational risk in production.
A professional with strong big data (spark/hadoop) can:
Build Spark Jobs For Batch And Streaming Workloads, Selecting Appropriate Transformations, Partitioning, And Caching Strategies.
Design Hadoop-based Storage And Ingestion Patterns (e.g., HDFS, Hive, Parquet) To Optimize Query Performance And Data Reliability.
Tune Cluster Resources And Job Execution (executors, Memory, Shuffle, YARN) To Reduce Failures And Improve Throughput.
Implement Data Quality Checks, Lineage, And Monitoring To Detect Schema Drift, Late Data, And Pipeline Regressions.
