Apache SparkStreaming Test

Candidates must demonstrate proficiency in configuring Spark Streaming for data ingestion from sources like Kafka, Flume, Kinesis, and FileStream. This includes managing batch intervals, data parallelism, and employing receiver-based and direct ingestion methods. Understanding backpressure, handling variable input rates, and ensuring reliable data ingestion are critical. Advanced knowledge involves managing large-scale Kafka ingestion and customizing ingestion pipelines.

This skill assesses candidates' abilities to perform basic to complex transformations in Spark Streaming, such as map, flatMap, reduceByKey, and windowed transformations. Candidates must understand stateful transformations using updateStateByKey and perform aggregation over time windows. They should also know how to use actions like saveAsTextFiles and count, and integrate DataFrames in streaming for optimized transformations.

Candidates need to understand Spark's Structured Streaming API, which processes continuous data streams using DataFrame and Dataset APIs. They should handle real-time data querying with SQL-like syntax and manage the transition from DStreams to Structured Streaming. The test includes fault tolerance and exactly-once processing semantics, and practical querying in structured streaming environments.

This involves understanding stateful stream processing where each data element updates or computes a state. Candidates must manage state with operations like mapWithState and updateStateByKey, perform window-based aggregation, and handle time-based events. Advanced skills include event-time processing, session windowing, and using watermarks to manage late-arriving data in real-time streams.

Candidates must demonstrate knowledge of Spark Streaming's fault-tolerance mechanisms, including checkpointing of state and metadata, and using Write-Ahead Logs (WAL) for data recovery. They should set up checkpointing in DStreams and Structured Streaming for robust data recovery, ensuring exactly-once semantics and configuring fault tolerance for complex jobs.

This skill assesses candidates' ability to optimize Spark Streaming applications for latency, throughput, and memory usage. Candidates must configure parallelism, manage backpressure, and optimize resources by tuning memory usage and managing stateful operations. Advanced skills include understanding trade-offs between throughput and latency and employing complex tuning strategies for distributed environments.

Candidates explore advanced features of Spark Streaming APIs, like event-time processing and stream-stream joins. They should apply watermarking techniques for event-time data management and optimize time-sensitive applications. The test assesses the ability to handle stream-based stateful joins and integrate real-time analytics and machine learning algorithms in streaming data pipelines.

This skill evaluates candidates' ability to integrate Spark Streaming with external systems like HDFS, Cassandra, Elasticsearch, and JDBC sinks. They should handle data ingestion and output, understand data serialization formats like Avro, Parquet, and JSON, and scale integrations for large applications. Advanced skills include managing data partitioning and replication strategies for multiple sinks and sources.

Candidates must know how to deploy Spark Streaming applications in production environments using clusters with YARN, Mesos, and Kubernetes. Skills include managing resource allocation, dynamic scaling, and tuning for large deployments. They should evaluate strategies for high availability, resilience, and monitoring real-time applications, and handle complex tasks like optimizing resource management and managing large-scale cluster failures.