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Spark Streaming Real-Time

⬢ דרג 2טכני
💰
גבוה
השפעה על השכר
⏱
10 חודשים
זמן לימוד
📊
קשה
רמת קושי
👥
12
מקצועות
במבט חטוף

Spark Structured Streaming is Spark's API for processing continuous data streams with low latency. Includes handling late-arriving data, window aggregations, stateful processing, and integration with Kafka/Kinesis. Used by data engineers building real-time pipelines. Takes 10-12 weeks to develop advanced competence. Sits between Spark SQL and stream processing systems.

מהי Spark Streaming Real-Time

Spark Structured Streaming is Apache Spark's API for processing continuous streams of data in real-time. It treats data streams as unbounded tables, allowing you to write SQL or DataFrame queries that run continuously. Structured Streaming handles complexities like late-arriving data, stateful processing, and fault tolerance. Applications include real-time analytics dashboards, anomaly detection, data pipelines, and event-driven systems. Spark Structured Streaming is the foundation for real-time data platforms.

🔧 כלים ואקוסיסטם
Spark Structured StreamingKafkaKinesisDelta LakePySparkApache FlinkScalaDatabricks

📋 לפני שמתחילים

Spark Sql Data

💰 שכר לפי אזור

אזורג׳וניורבינוניסניור
🇺🇸USA$110k$180k$280k
🇬🇧UK$85k$145k$230k
🇪🇺EU$90k$150k$240k
🇨🇦CANADA$105k$175k$270k

🎓 הסמכות

Databricks Certified Data Engineer→Streaming Data Processing Fundamentals→

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⚖ השוואה עם

Spark Sql Data

❓ שאלות נפוצות

▶What's the difference between Spark Structured Streaming and regular Spark batch?
Streaming processes continuous data with low latency. Batch processes static data. Structured Streaming uses SQL; you write a query that runs continuously.
▶How does Spark Structured Streaming handle late-arriving data?
Watermarks define when you expect data. Data arriving after the watermark is out-of-order. Spark can handle it but you must decide: include it or drop it.
▶Can I use window aggregations in streaming?
Yes. Tumbling windows (fixed size, no overlap), sliding windows (overlap), session windows (defined by inactivity). Windows are essential for streaming analytics.
▶How low-latency can Spark Structured Streaming achieve?
With micro-batch processing, typically 500ms-1s latency. With continuous mode, sub-second. Not as low as single-event systems but good for most use cases.
▶What's the cost of running a streaming application?
Cost scales with compute allocated. Always-on clusters cost money. Right-size your cluster; auto-scaling helps. Spot instances can reduce cost.

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