全部技能
Apache Spark
⬢ 層級 2技術
高
薪資影響
9 個月
學習時長
困難
難度
2
職業
一覽
Apache Spark is the standard framework for large-scale data processing: process hundreds of petabytes via PySpark, Spark SQL, and Structured Streaming across Databricks, AWS EMR, Google Dataproc. Career path: Practitioner (DataFrames, SQL, $120-150k) → Developer (partitioning, joins, streaming, $150-190k) → Architect (cluster mgmt, Delta Lake, $190-250k+). Ecosystem: Delta Lake (ACID lakehouse), Databricks, MLlib (distributed ML), Iceberg (versioned tables).
什麼是 Apache Spark
Distributed data processing engine for big data analytics. Process petabytes of data across clusters. Standard for batch and streaming data processing at scale. Learning Curve: Medium-Hard (distributed computing concepts)
🔧 工具與生態
DatabricksAWS EMRGoogle DataprocPySparkSpark SQLSpark StreamingDelta LakeMLlibApache IcebergHive MetastoreParquetScalaHadoop
💰 各地區薪資
| 地區 | 初級 | 中級 | 高級 |
|---|---|---|---|
| USA | $125,000 | $165,000 | $220,000 |
| UK | £70,000 | £95,000 | £135,000 |
| EU | €75,000 | €100,000 | €140,000 |
| CANADA | CA$130,000 | CA$175,000 | CA$230,000 |
🎓 認證
🎯 使用 Apache Spark 的職業
❓ 常見問題
Spark vs SQL warehouses (Snowflake/BigQuery), when use which?
Spark: complex transformations, ML, streaming, open-source, lower cost at massive scale (petabytes). SQL warehouses: structured analytics, dashboards, simple SQL, managed infra, faster queries on GB-TB scale. Sweet spot: Spark for ETL/pipelines, warehouse for BI. Many orgs use both: Spark for prep, warehouse for serving.
Databricks vs DIY Spark (EMR/Dataproc), what's the real cost difference?
Databricks: managed, Delta Lake native, ~2-3x opex of raw cloud but includes cluster mgmt/optimization/notebooks. EMR/Dataproc: cheaper per compute-hour but ops overhead, DevOps time, monitoring. For teams <5: Databricks saves money. For teams >15 with Kubernetes expertise: DIY wins if you automate. 2026 trend: Databricks serverless (Compute) removes cluster ops entirely.
When NOT to use Spark, when is it overkill?
Spark has overhead: cluster startup (2-5 min), memory footprint (4GB+ minimum). Avoid: <10GB datasets (Pandas faster), real-time <100ms latency (use Kafka/Flink), simple SQL (use warehouse), batch <1GB (local machine). Spark shines: >1TB, complex multi-stage, ML training, streaming millions/sec, diverse data sources.
PySpark vs Scala Spark, which should I learn?
PySpark: easier to learn, richer ML ecosystem (scikit-learn, TensorFlow), preferred by data scientists, slower UDFs. Scala: native, best performance, used by Netflix/Uber backends, steeper learning curve. Start PySpark. If you hit performance walls on UDFs, learn Scala for critical paths. Most jobs ask for PySpark.
Spark 4 changes, what broke and do I care?
Spark 3.5→4.0: PySpark Type Hints (opt-in), ANSI compliance stricter, minor API changes. Your Spark 3 code runs fine. Benefits: 30-50% faster on TPC-DS, better Kubernetes, serverless improvements. Upgrade gradually. New projects: use 4.0 directly.
How do I optimize Spark costs and performance?
Partition smartly (avoid >10k tiny partitions), use broadcast for <100MB joins, cache only hot DataFrames, tune shuffle (spark.shuffle.partitions), prefer Parquet/Delta over CSV, use columnar filters, right-size executors (don't oversell). Databricks Photon: 10-50x speedup on SQL at no extra cost. Monitor with Spark UI (stages, executors, shuffle).
Spark for ML, when to use vs scikit-learn or TensorFlow?
MLlib (Spark's ML library): distributed training on petabytes, good for classification/regression/clustering at scale. Downsides: slower than TensorFlow/PyTorch, limited deep learning. Use MLlib: >1TB data, distributed CPU training. Use TensorFlow: deep learning, GPUs, <1TB. Hybrid: PySpark for prep, TensorFlow for training on sampled data, score back in Spark.
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