Los cursos de capacitación locales en línea de Apache Spark demuestran a través de la práctica cómo Spark encaja en el ecosistema Big Data y cómo utilizar Spark para el análisis de datos El entrenamiento de Apache Spark está disponible como "entrenamiento en vivo en el sitio" o "entrenamiento en vivo a distancia" El entrenamiento en vivo in situ se puede llevar a cabo localmente en las instalaciones del cliente en España o en los centros de capacitación corporativa de NobleProg en España La capacitación remota en vivo se lleva a cabo a través de un escritorio remoto interactivo NobleProg Su proveedor local de capacitación.
Me gusto la metodologia esperada por Jorge
Experian Colombia S.A
Curso: Spark para Desarrolladores
Richard es muy tranquilo y metódico, con una visión analítica, exactamente las cualidades necesarias para presentar este tipo de curso.
Kieran Mac Kenna
Curso: Spark for Developers
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Casos y casos aplicables
zhaopeng liu - Fmr
Curso: Spark for Developers
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Análisis de caso
国栋 张
Curso: Spark for Developers
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todas las partes de esta sesión
Eric Han - Fmr
Curso: Spark for Developers
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Sabemos mucho más sobre todo el entorno.
John Kidd
Curso: Spark for Developers
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El entrenador hizo la clase interesante y entretenida, lo que ayuda bastante con el entrenamiento de todo el día.
Ryan Speelman
Curso: Spark for Developers
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Creo que el entrenador tenía un estilo excelente de combinar el humor y las historias de la vida real para que los temas disponibles fueran muy accesibles. Recomiendo encarecidamente a este profesor en el futuro.
Curso: Spark for Developers
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Ernesto hizo un gran trabajo al explicar los conceptos de alto nivel del uso de Spark y sus diversos módulos.
Michael Nemerouf
Curso: Spark for Developers
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Este es uno de los mejores cursos prácticos de programación de ejercicios que he tomado.
Laura Kahn
Curso: Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP
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Esta es una de las mejores capacitaciones en línea de calidad que he tomado en mi carrera de 13 años. ¡Mantener el buen trabajo!.
Curso: Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP
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| Código | Nombre | Duración | Información General |
|---|---|---|---|
| sparkdev | Spark for Developers | 21 horas | OBJECTIVE: This course will introduce Apache Spark. The students will learn how Spark fits into the Big Data ecosystem, and how to use Spark for data analysis. The course covers Spark shell for interactive data analysis, Spark internals, Spark APIs, Spark SQL, Spark streaming, and machine learning and graphX. AUDIENCE : Developers / Data Analysts |
| hdp | Hortonworks Data Platform (HDP) for Administrators | 21 horas | Hortonworks Data Platform is an open-source Apache Hadoop support platform that provides a stable foundation for developing big data solutions on the Apache Hadoop ecosystem. This instructor-led live training introduces Hortonworks and walks participants through the deployment of Spark + Hadoop solution. By the end of this training, participants will be able to: - Use Hortonworks to reliably run Hadoop at a large scale - Unify Hadoop's security, governance, and operations capabilities with Spark's agile analytic workflows. - Use Hortonworks to investigate, validate, certify and support each of the components in a Spark project - Process different types of data, including structured, unstructured, in-motion, and at-rest. Audience - Hadoop administrators Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| magellan | Magellan: Geospatial Analytics on Spark | 14 horas | Magellan is an open-source distributed execution engine for geospatial analytics on big data. Implemented on top of Apache Spark, it extends Spark SQL and provides a relational abstraction for geospatial analytics. This instructor-led, live training introduces the concepts and approaches for implementing geospacial analytics and walks participants through the creation of a predictive analysis application using Magellan on Spark. By the end of this training, participants will be able to: - Efficiently query, parse and join geospatial datasets at scale - Implement geospatial data in business intelligence and predictive analytics applications - Use spatial context to extend the capabilities of mobile devices, sensors, logs, and wearables Audience - Application developers Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| alluxio | Alluxio: Unifying Disparate Storage Systems | 7 horas | Alluxio is an open-source virtual distributed storage system that unifies disparate storage systems and enables applications to interact with data at memory speed. It is used by companies such as Intel, Baidu and Alibaba. In this instructor-led, live training, participants will learn how to use Alluxio to bridge different computation frameworks with storage systems and efficiently manage multi-petabyte scale data as they step through the creation of an application with Alluxio. By the end of this training, participants will be able to: - Develop an application with Alluxio - Connect big data systems and applications while preserving one namespace - Efficiently extract value from big data in any storage format - Improve workload performance - Deploy and manage Alluxio standalone or clustered Audience - Data scientist - Developer - System administrator Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| sparkpython | Python and Spark for Big Data (PySpark) | 21 horas | Python is a high-level programming language famous for its clear syntax and code readibility. Spark is a data processing engine used in querying, analyzing, and transforming big data. PySpark allows users to interface Spark with Python. In this instructor-led, live training, participants will learn how to use Python and Spark together to analyze big data as they work on hands-on exercises. By the end of this training, participants will be able to: - Learn how to use Spark with Python to analyze Big Data - Work on exercises that mimic real world circumstances - Use different tools and techniques for big data analysis using PySpark Audience - Developers - IT Professionals - Data Scientists Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| sparksql | Apache Spark SQL | 7 horas | Spark SQL is Apache Spark's module for working with structured and unstructured data. Spark SQL provides information about the structure of the data as well as the computation being performed. This information can be used to perform optimizations. Two common uses for Spark SQL are: - to execute SQL queries. - to read data from an existing Hive installation. In this instructor-led, live training (onsite or remote), participants will learn how to analyze various types of data sets using Spark SQL. By the end of this training, participants will be able to: - Install and configure Spark SQL. - Perform data analysis using Spark SQL. - Query data sets in different formats. - Visualize data and query results. Audience - Data analysts - Data scientists - Data engineers Format of the Course - Part lecture, part discussion, exercises and heavy hands-on practice Notes - To request a customized training for this course, please contact us to arrange. |
| introtostreamprocessing | A Practical Introduction to Stream Processing | 21 horas | Stream Processing refers to the real-time processing of "data in motion", that is, performing computations on data as it is being received. Such data is read as continuous streams from data sources such as sensor events, website user activity, financial trades, credit card swipes, click streams, etc. Stream Processing frameworks are able to read large volumes of incoming data and provide valuable insights almost instantaneously. In this instructor-led, live training (onsite or remote), participants will learn how to setup and integrate different Stream Processing frameworks with existing big data storage systems and related software applications and microservices. By the end of this training, participants will be able to: - Install and configure different Stream Processing frameworks, such as Spark Streaming and Kafka Streaming - Understand and select the most appropriate framework for the job - Process of data continuously, concurrently, and in a record-by-record fashion - Integrate Stream Processing solutions with existing databases, data warehouses, data lakes, etc. - Integrating the most appropriate stream processing library with enterprise applications and microservices Audience - Developers - Software architects Format of the Course - Part lecture, part discussion, exercises and heavy hands-on practice Notes - To request a customized training for this course, please contact us to arrange. |
| bigdataanahealth | Big Data Analytics in Health | 21 horas | Big data analytics involves the process of examining large amounts of varied data sets in order to uncover correlations, hidden patterns, and other useful insights. The health industry has massive amounts of complex heterogeneous medical and clinical data. Applying big data analytics on health data presents huge potential in deriving insights for improving delivery of healthcare. However, the enormity of these datasets poses great challenges in analyses and practical applications to a clinical environment. In this instructor-led, live training (remote), participants will learn how to perform big data analytics in health as they step through a series of hands-on live-lab exercises. By the end of this training, participants will be able to: - Install and configure big data analytics tools such as Hadoop MapReduce and Spark - Understand the characteristics of medical data - Apply big data techniques to deal with medical data - Study big data systems and algorithms in the context of health applications Audience - Developers - Data Scientists Format of the Course - Part lecture, part discussion, exercises and heavy hands-on practice. Note - To request a customized training for this course, please contact us to arrange. |
| spmllib | Apache Spark MLlib | 35 horas | MLlib is Spark’s machine learning (ML) library. Its goal is to make practical machine learning scalable and easy. It consists of common learning algorithms and utilities, including classification, regression, clustering, collaborative filtering, dimensionality reduction, as well as lower-level optimization primitives and higher-level pipeline APIs. It divides into two packages: - spark.mllib contains the original API built on top of RDDs. - spark.ml provides higher-level API built on top of DataFrames for constructing ML pipelines. Audience This course is directed at engineers and developers seeking to utilize a built in Machine Library for Apache Spark |
| aitech | Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP | 21 horas | This course is aimed at developers and data scientists who wish to understand and implement AI within their applications. Special focus is given to Data Analysis, Distributed AI and NLP. |
| graphcomputing | Introduction to Graph Computing | 28 horas | A large number of real world problems can be described in terms of graphs. For example, the Web graph, the social network graph, the train network graph and the language graph. These graphs tend to be extremely large; processing them requires a specialized set of tools and processes -- these tools and processes can be referred to as Graph Computing (also known as Graph Analytics). In this instructor-led, live training, participants will learn about the technology offerings and implementation approaches for processing graph data. The aim is to identify real-world objects, their characteristics and relationships, then model these relationships and process them as data using a graph computing approach. We start with a broad overview and narrow in on specific tools as we step through a series of case studies, hands-on exercises and live deployments. By the end of this training, participants will be able to: - Understand how graph data is persisted and traversed - Select the best framework for a given task (from graph databases to batch processing frameworks) - Implement Hadoop, Spark, GraphX and Pregel to carry out graph computing across many machines in parallel - View real-world big data problems in terms of graphs, processes and traversals Audience - Developers Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| Curso | Fecha | Precio del Curso [A distancia / Presencial] |
|---|---|---|
| Apache Spark SQL - Barcelona | Jue, 2018-10-11 09:30 | 1500EUR / 1900EUR |
| Una introducción práctica al procesamiento de flujo - Valencia | Lun, 2018-10-15 09:30 | 4500EUR / 5300EUR |
| Python y Spark para Big Data (PySpark) - Madrid | Lun, 2018-10-15 09:30 | 5250EUR / 6050EUR |
| Inteligencia Artificial - La Materia más Aplicada - Análisis de Datos + AI Distribuido + PNL - Málaga | Mar, 2018-11-20 09:30 | 5250EUR / 6050EUR |
| Big Data Analytics in Health - Bilbao | Lun, 2018-11-26 09:30 | 4500EUR / 5300EUR |
| Curso | Ubicación | Fecha | Precio del Curso [A distancia / Presencial] |
|---|---|---|---|
| Taller de un Día para la Autenticación PEAP de Windows 7 Supplicant Mediante un Conmutador Cisco como Autenticador y Servidor de Windows 2008 R2 | Madrid | Vie, 2018-11-09 09:30 | 1350EUR / 1750EUR |
| Programación con Big Data en R | Madrid | Mar, 2018-11-13 09:30 | 4725EUR / 5525EUR |
| Node.js para Desarrolladores de JavaScript | Málaga | Lun, 2018-11-19 09:30 | 4050EUR / 4850EUR |
| Apache Solr - Full-Text Search Server | Barcelona | Mié, 2018-11-21 09:30 | 2700EUR / 3300EUR |
| Administrar y Optimizar la Máquina Virtual de HipHop (HHVM) | Barcelona | Mar, 2019-01-22 09:30 | 2700EUR / 3300EUR |
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