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SPARK - LRBA 9

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SPARK - LRBA 9

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Played 30 %Accuracy 85 Average time 05:33
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Conectores en LRBA

Created by

HR Mexico
HR Mexico
Mexico

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SPARK - LRBA 9
 

SPARK - LRBA 9Online version

Conectores en LRBA

by HR Mexico
1

¿Cuál es el rol principal del Partitioner en un conector LRBA?

2

En JDBC connector, ¿qué opción habilita partitioning dinámico?

3

¿Qué interfaz implementa un conector personalizado en LRBA?

4

¿Cuál es el beneficio clave de Connector Fusion?

5

En Delta Connector, ¿qué opción habilita Z-ordering?

6

¿Qué configuración habilita exactly-once en Kafka connector?

7

¿Cuál es el buffer recomendado para S3 connector?

8

¿Qué estrategia maneja schema changes en conectores?

9

En tuning, ¿qué habilita dynamic scaling?

10

¿Cuál es la métrica p99 para conectores?

11

¿Qué patrón usa RetryConnectorWrapper?

12

Para CDC en PostgreSQL, ¿qué modo usar?

13

¿Qué benchmark valida performance LRBA?

14

¿Cuál es el tamaño target para Iceberg files?

15

¿Qué asegura idempotent writes?

16

Para seguridad en S3, ¿qué usar?

17

¿Qué optimiza queries post-ETL?

18

En monitoreo, ¿qué query Prometheus para latency?

19

¿Qué habilita columnar zero-copy?

20

En migración Spark a LRBA, ¿qué usar?

Explicación

El Partitioner genera particiones lógicas basadas en claves o rangos para parallelism.

partitionColumn, lowerBound y upperBound definen rangos para múltiples particiones.

LRBAConnector[T] es la trait base para sources, sinks y transforms genéricos.

Fusion optimiza DAGs fusionando conectores compatibles.

OPTIMIZE tabla ZORDER BY(col) clustering multidimensional.

WAL y idempotence garantizan no duplicados en batch.

fs.s3a.buffer.size=64MB equilibra throughput y memoria.

evolve/append permite migraciones automáticas.

Permite min/max executors basados en demanda.

p99 mide latencia en percentil 99.

Exponential backoff y circuit breaker para resiliencia.

Habilita append-only changes.

TPC-DS para decision support batch.

write.target-file-size-bytes=134217728.

MERGE INTO o conditional writes.

IAM evita hardcoded credentials.

Filtra en fuente reduciendo datos transferidos.

Calcula p99 de buckets.

spark.sql.execution.arrow.pyspark para eficiencia.

Adapter transpila jobs legacy.

 
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