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SPARK - LRBA 1

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SPARK - LRBA 1

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About this activity

Introducción a Apache Spark

Created by

HR Mexico
HR Mexico
Mexico

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SPARK - LRBA 1
 

SPARK - LRBA 1Online version

Introducción a Apache Spark

by HR Mexico
1

¿Cuál es la diferencia principal entre transformaciones y acciones en RDD?

2

En Spark, ¿qué optimizador maneja DataFrames/SQL?

3

¿Qué StorageLevel usa memoria y disco serializado?

4

En Structured Streaming, ¿qué maneja late data?

5

¿Cuál evita shuffle en groupByKey?

6

¿Feature de Spark 3.0+ para skew?

7

¿API unificada post-Spark 2.0?

8

En arquitectura, ¿coordina tasks?

9

¿Para joins pequeños en DataFrames?

10

¿Fault-tolerance vía?

11

¿Modo output para agregaciones streaming completas?

12

¿Tamaño ideal partición?

13

¿Wide dependency genera?

14

En Netflix, Spark reemplaza?

15

¿Off-heap memory?

16

¿Para ML pipelines?

17

¿Dynamic allocation requiere?

18

En Uber, Spark procesa?

19

¿Whole-stage codegen?

20

¿Cluster manager en cloud?

Explicación

Transformaciones construyen DAG lazy; acciones materializan cómputo.

Catalyst aplica rule/cost-based optimizaciones pre-ejecución.

MEMORY_AND_DISK_SER serializa para ahorrar memoria.

Watermark define threshold para dropear eventos tardíos.

reduceByKey combina localmente antes de shuffle.

Adaptive Query Execution ajusta runtime (skew handling).

Unifica batch/streaming/ML con optimizaciones.

Task Scheduler asigna tasks considerando locality.

Broadcast envía tabla a todos executors, evita shuffle.

Lineage permite recomputar particiones perdidas.

complete emite tabla completa por trigger.

Balancea overhead/paralelismo (Spark doc).

Wide requiere shuffle, divide stages.

10x speedup en ETL batch.

Tungsten usa off-heap para GC efficiency.

MLlib provee transformers/estimators distribuidos.

Escala executors dinámicamente.

Structured Streaming para movilidad real-time.

Fusiona stages en single function para velocidad.

Soporta YARN, Kubernetes para orquestación.

 
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