Apache Spark Structured Streaming Test

This area covers the ability to read from diverse input sources such as Kafka, socket, file systems, and cloud storage. Candidates are tested on configuring sources, schema inference, and dynamic schema handling. Mastery here is vital for real-world data ingestion pipelines where input formats may vary or evolve rapidly.

Tests proficiency with stateless and stateful transformations using DataFrame operations (select, groupBy, agg, etc.), joins, and event-time processing. This skill is essential as it forms the core logic of streaming applications, enabling users to derive actionable insights from streaming data in near real-time.

Evaluates the ability to aggregate data over defined time windows and handle late-arriving data using watermarks. These features are critical for accurate time-based analytics in event-driven systems, especially when dealing with out-of-order or delayed data in systems like IoT, fraud detection, or user activity tracking.

Assesses the understanding of output destinations (e.g., Kafka, file, memory) and output modes (Append, Update, Complete). Also includes knowledge of checkpointing and fault-tolerant writes. This skill ensures candidates can safely and efficiently persist results in production pipelines without data loss or duplication.

Focuses on stateful streaming operations (e.g., mapGroupsWithState), checkpointing, and exactly-once semantics. These concepts are essential for building reliable applications that maintain state across data events, such as session tracking, accumulators, and dynamic aggregations, while recovering gracefully from failures.

Tests knowledge of tuning Structured Streaming jobs for lower latency, higher throughput, and efficient resource usage. Includes triggers, memory management, caching, and watermark tuning. Critical for deploying scalable pipelines that remain performant under variable load and data volume scenarios.

Covers Kafka consumer/producer configurations, offsets, rebalance handling, and integration with other tools like Delta Lake. This skill is vital for building robust pipelines in real-world environments where Kafka acts as a central data backbone and integration points must be managed securely and efficiently.

Assesses the ability to use Spark UI, logs, and metrics to monitor and debug streaming applications. Essential for diagnosing bottlenecks, query stalls, and runtime issues, ensuring streaming jobs remain operational and performant in 24/7 production settings.

Focuses on applying knowledge to solve complex challenges such as late data correction, schema evolution, idempotent sink design, and use-case simulation. This skill tests the candidate’s ability to apply Structured Streaming in production scenarios with business-critical reliability and custom logic.

Assesses understanding of secure streaming practices, such as encryption, authentication (Kafka SSL/SASL), and data privacy (e.g., masking). Important for regulated industries where secure real-time data handling is mandatory.