Descripción del curso:
Learn Spark skills from a data science perspective to build unified big data applications combining batch, streaming, and interactive analytics on your data.
Análisis y BI
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Apache Spark para científicos de datos
*¿Busca un horario flexible (fuera de horario o fines de semana)? Por favor llame o envíenos un correo electrónico: 858-208-4141 o sales@ccslearningacademy.com.
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- Descripción
- Información adicional
- Esquema del curso
- Público objetivo
- Lo que aprenderás
- Requisitos previos
- Inclusiones
Apache Spark is a powerful, open-source processing engine for data in the Hadoop cluster, optimized for speed, ease of use, and sophisticated analytics. The Spark framework supports streaming data processing and complex iterative algorithms, enabling applications to run up to 100x faster than traditional Hadoop MapReduce programs. With Spark, you can write sophisticated applications to execute faster decisions and real-time actions to a wide variety of use cases, architectures, and industries.
This hands-on course explores using Spark for common data related activities from a data science perspective. You will learn to build unified big data applications combining batch, streaming, and interactive analytics on your data.
| Formato | Dirigido por un instructor |
|---|---|
| Tema | |
| Longitud |
Esquema del curso
Spark
- Data Science: The State of the Art
- Hadoop, Yarn, and Spark
- Architectural Overview
- Spark and Storm
- MLib and Mahout
- Distributed vs. Local Run Modes
- Hello, Spark
Spark Overview
- Spark Core
- Spark SQL
- Spark and Hive
- MLib
- Mahout
- Spark Streaming
- Spark API
DataFrames
- DataFrames and Resilient Distributed Datasets (RDDs)
- Particiones
- DataFrame Types
- DataFrame Operations
- Map/Reduce with DataFrames
Spark SQL
- Spark SQL Overview
- Data stores: HDFS, Cassandra, HBase, Hive, and S3
- Table Definitions
- ETL in Spark
- Queries
Spark MLib
- MLib overview
- MLib Algorithms Overview
Spark Streaming
- Streaming overview
- Real-time data ingestion
- State
- Window Operations
Spark GraphX
- GraphX overview
- ETL with GraphX
- Graph computation
Performance and Tuning
- Broadcast variables
- Accumulators
- Memory Management
Cluster Mode
- Standalone Cluster
- Masters and Workers
- Configurations
- Working with large data sets
Público objetivo
Data Scientists, System Administrators, Testers, and other technical business professionals who seek to use Spark for data processing and analysis.
Lo que aprenderás
Join an engaging hands-on learning environment, where you’ll learn:
- The essentials of Spark architecture and applications
- How to execute Spark Programs
- How to create and manipulate both RDDs (Resilient Distributed Datasets) and UDFs (Unified Data Frames)
- How to integrate machine learning into Spark applications
- How to use Spark Streaming
Requisitos previos
Before attending this course, you should have:
- Introduction to Java Programming (at least exposure to basic Java syntax)
- Introduction to SQL (familiarity wits SQL basics)
- Basic knowledge of Statistics and Probability
- Data Science background
Inclusiones
Con CCS Learning Academy, recibirás:
- Instructor-led training
- Manual del estudiante del seminario de capacitación
- Colaboración con compañeros de clase (actualmente no disponible para cursos a su propio ritmo)
- Escenarios y actividades de aprendizaje del mundo real.
- Soporte para la programación de exámenes*
- Disfrute de asistencia para la colocación laboral durante los primeros 12 meses después de finalizar el curso.
- Este curso es elegible para el programa Learn and Earn de CCS Learning Academy: obtenga un reembolso de la matrícula de hasta 50% si se le coloca en un trabajo a través de CCS Global Tech. División de colocación*
- Precios gubernamentales y privados disponibles.*
*Para más detalles llame al: 858-208-4141 o correo electrónico: formación@ccslearningacademy.com; ventas@ccslearningacademy.com
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