Formation Apache Spark - un moteur de traitement Big Data
Nos Clients témoignent
Richard est très calme et méthodique, avec un aperçu analytique - exactement les qualités nécessaires pour présenter ce type de cours.
Kieran Mac Kenna
Formation: Spark for Developers
Machine Translated
partager le diagramme de concept et aussi l'échantillon pour les mains sales
Mark Yang - FMR
Formation: Spark for Developers
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Scénarios et cas applicables
zhaopeng liu - Fmr
Formation: Spark for Developers
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Analyse de cas
国栋 张
Formation: Spark for Developers
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toutes les parties de cette session
Eric Han - Fmr
Formation: Spark for Developers
Machine Translated
Nous en savons beaucoup plus sur l’ensemble de l’environnement.
John Kidd
Formation: Spark for Developers
Machine Translated
Le formateur a rendu le cours intéressant et divertissant, ce qui aide un peu tout au long de la journée.
Ryan Speelman
Formation: Spark for Developers
Machine Translated
Je pense que le formateur avait un excellent style en combinant l'humour et des histoires réelles pour rendre les sujets abordables. Je recommande vivement ce professeur à l'avenir.
Formation: Spark for Developers
Machine Translated
Ernesto a fait un excellent travail en expliquant les concepts de haut niveau de l'utilisation de Spark et de ses différents modules.
Michael Nemerouf
Formation: Spark for Developers
Machine Translated
Il s’agit de l’un des meilleurs exercices pratiques de programmation que je connaisse.
Laura Kahn
Formation: Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP
Machine Translated
C’est l’une des meilleures formations en ligne de qualité que j’ai jamais eues au cours de ma carrière de 13 ans. Continuez ce bon travail!.
Formation: Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP
Machine Translated
Richard était très disposé à faire une digression quand nous voulions poser des questions semi-liées sur des choses qui ne figuraient pas dans le programme. Les explications étaient claires et il était au premier plan des mises en garde dans tous les conseils qu'il nous donnait.
ARM Limited
Formation: Spark for Developers
Machine Translated
La VM que j'aimais beaucoup L'enseignant était très bien informé sur le sujet ainsi que d'autres sujets, il était très gentil et sympathique J'ai aimé l'installation à Dubaï.
Safar Alqahtani - Elm Information Security
Formation: Big Data Analytics in Health
Machine Translated
tâches de pratique
Pawel Kozikowski - GE Medical Systems Polska Sp. Zoo
Formation: Python and Spark for Big Data (PySpark)
Machine Translated
Ajay était très sympathique, serviable et connaissait bien le sujet dont il discutait.
Biniam Guulay - ICE International Copyright Enterprise Germany GmbH
Formation: Spark for Developers
Machine Translated
Les exercices de laboratoire. Appliquer la théorie à partir du premier jour des jours suivants.
Dell
Formation: A Practical Introduction to Stream Processing
Machine Translated
L'enseignant a adapté le programme de formation à nos besoins actuels.
EduBroker Sp. z o.o.
Formation: Python and Spark for Big Data (PySpark)
Machine Translated
Faire des exercices similaires de différentes manières aide vraiment à comprendre ce que chaque composant ( Hadoop / Spark, autonome / cluster) peut faire seul et ensemble. Cela m'a donné des idées sur la façon de tester mon application sur ma machine locale lorsque je développe vs lorsqu'elle est déployée sur un cluster.
Thomas Carcaud - IT Frankfurt GmbH
Formation: Spark for Developers
Machine Translated
l’attention individuelle.
ARCHANA ANILKUMAR - PPL
Formation: Python and Spark for Big Data (PySpark)
Machine Translated
Apprenez à apprendre Spark streaming, Databricks et AWS Redshift
Lim Meng Tee - Jobstreet.com Shared Services Sdn. Bhd.
Formation: Apache Spark in the Cloud
Machine Translated
Le contenu et la connaissance.
Jobstreet.com Shared Services Sdn. Bhd.
Formation: Apache Spark in the Cloud
Machine Translated
C'était très instructif. J'ai eu très peu d'expérience avec l'étincelle avant et jusqu'ici ce cours a fourni une très bonne introduction au sujet.
Intelligent Medical Objects
Formation: Apache Spark in the Cloud
Machine Translated
C’était génial d’avoir une compréhension de ce qui se passe sous le capot de Spark. Savoir ce qui se passe sous le capot aide à mieux comprendre pourquoi votre code est ou ne fait pas ce que vous attendez qu’il fasse. Une grande partie de la formation a été mains sur ce qui est toujours génial et la section sur les optimisations a été exceptionnellement pertinent pour mon travail actuel qui était agréable.
Intelligent Medical Objects
Formation: Apache Spark in the Cloud
Machine Translated
C'est une grande classe! Je l'apprécie tous que l'Andras explique très clairement ce que l'étincelle est tout, d'où vient ce que, et quels problèmes il est capable de résoudre. Beaucoup mieux que les autres introductions que j'ai vues qui plongent dans la façon de l'utiliser. Les Andras ont une connaissance approfondie du sujet et explique très bien les choses.
Intelligent Medical Objects
Formation: Apache Spark in the Cloud
Machine Translated
Les exemples vivants ont été donnés et ont montré les aspects fondamentaux de l'étincelle.
Intelligent Medical Objects
Formation: Apache Spark in the Cloud
Machine Translated
1 . Balance droite entre les concepts de haut niveau et les détails techniques. 2. Andras est très bien informé de son enseignement. 3. Exercice
Steven Wu - Intelligent Medical Objects
Formation: Apache Spark in the Cloud
Machine Translated
Avoir des mains sur session / missions
Poornima Chenthamarakshan - Intelligent Medical Objects
Formation: Apache Spark in the Cloud
Machine Translated
L'entraîneur ajusté la formation légèrement basée sur la demande d'audience, alors jetez quelques lumières sur quelques sujets de diffaction que nous avons demandé
Intelligent Medical Objects
Formation: Apache Spark in the Cloud
Machine Translated
Son rythme, était génial. J'ai aimé le fait qu'il allait en théorie aussi pour que je comprends pourquoi je ferais les choses qu'il demandait.
Intelligent Medical Objects
Formation: Apache Spark in the Cloud
Machine Translated
Je pense que le formateur avait un excellent style en combinant l'humour et des histoires réelles pour rendre les sujets abordables. Je recommande vivement ce professeur à l'avenir.
Formation: Spark for Developers
Machine Translated
C’est l’une des meilleures formations en ligne de qualité que j’ai jamais eues au cours de ma carrière de 13 ans. Continuez ce bon travail!.
Formation: Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP
Machine Translated
Nos clients
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Sous-catégories Apache Spark
Plans de cours Apache Spark
Nom du Cours
Durée
Aperçu
Nom du Cours
Durée
Aperçu
21 hours
Aperçu
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
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
21 hours
Aperçu
This instructor-led, live training in (online or onsite) introduces Hortonworks Data Platform (HDP) 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.
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.
14 hours
Aperçu
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
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
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
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
7 hours
Aperçu
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
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
7 hours
Aperçu
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.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
- 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.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
21 hours
Aperçu
In this instructor-led, live training in Quebec (onsite or remote), participants will learn how to set up 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.
- Integrate the most appropriate stream processing library with enterprise 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.
- Integrate the most appropriate stream processing library with enterprise applications and microservices.
21 hours
Aperçu
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.
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.
21 hours
Aperçu
Apache Spark's learning curve is slowly increasing at the begining, it needs a lot of effort to get the first return. This course aims to jump through the first tough part. After taking this course the participants will understand the basics of Apache Spark , they will clearly differentiate RDD from DataFrame, they will learn Python and Scala API, they will understand executors and tasks, etc. Also following the best practices, this course strongly focuses on cloud deployment, Databricks and AWS. The students will also understand the differences between AWS EMR and AWS Glue, one of the lastest Spark service of AWS.
AUDIENCE:
Data Engineer, DevOps, Data Scientist
AUDIENCE:
Data Engineer, DevOps, Data Scientist
21 hours
Aperçu
This instructor-led, live training in Quebec (online or onsite) is aimed at software engineers who wish to stream big data with Spark Streaming and Scala.
By the end of this training, participants will be able to:
- Create Spark applications with the Scala programming language.
- Use Spark Streaming to process continuous streams of data.
- Process streams of real-time data with Spark Streaming.
By the end of this training, participants will be able to:
- Create Spark applications with the Scala programming language.
- Use Spark Streaming to process continuous streams of data.
- Process streams of real-time data with Spark Streaming.
14 hours
Aperçu
This instructor-led, live training in Quebec (online or onsite) is aimed at data scientists who wish to use the SMACK stack to build data processing platforms for big data solutions.
By the end of this training, participants will be able to:
- Implement a data pipeline architecture for processing big data.
- Develop a cluster infrastructure with Apache Mesos and Docker.
- Analyze data with Spark and Scala.
- Manage unstructured data with Apache Cassandra.
By the end of this training, participants will be able to:
- Implement a data pipeline architecture for processing big data.
- Develop a cluster infrastructure with Apache Mesos and Docker.
- Analyze data with Spark and Scala.
- Manage unstructured data with Apache Cassandra.
21 hours
Aperçu
This instructor-led, live training in Quebec (online or onsite) is aimed at engineers who wish to set up and deploy Apache Spark system for processing very large amounts of data.
By the end of this training, participants will be able to:
- Install and configure Apache Spark.
- Quickly process and analyze very large data sets.
- Understand the difference between Apache Spark and Hadoop MapReduce and when to use which.
- Integrate Apache Spark with other machine learning tools.
By the end of this training, participants will be able to:
- Install and configure Apache Spark.
- Quickly process and analyze very large data sets.
- Understand the difference between Apache Spark and Hadoop MapReduce and when to use which.
- Integrate Apache Spark with other machine learning tools.
21 hours
Aperçu
This instructor-led, live training in Quebec (online or onsite) is aimed at developers who wish to carry out big data analysis using Apache Spark in their .NET applications.
By the end of this training, participants will be able to:
- Install and configure Apache Spark.
- Understand how .NET implements Spark APIs so that they can be accessed from a .NET application.
- Develop data processing applications using C# or F#, capable of handling data sets whose size is measured in terabytes and pedabytes.
- Develop machine learning features for a .NET application using Apache Spark capabilities.
- Carry out exploratory analysis using SQL queries on big data sets.
By the end of this training, participants will be able to:
- Install and configure Apache Spark.
- Understand how .NET implements Spark APIs so that they can be accessed from a .NET application.
- Develop data processing applications using C# or F#, capable of handling data sets whose size is measured in terabytes and pedabytes.
- Develop machine learning features for a .NET application using Apache Spark capabilities.
- Carry out exploratory analysis using SQL queries on big data sets.
35 hours
Aperçu
This instructor-led, live training in Quebec (online or onsite) is aimed at system administrators who wish to learn how to set up, deploy and manage Hadoop clusters within their organization.
By the end of this training, participants will be able to:
- Install and configure Apache Hadoop.
- Understand the four major components in the Hadoop ecoystem: HDFS, MapReduce, YARN, and Hadoop Common.
- Use Hadoop Distributed File System (HDFS) to scale a cluster to hundreds or thousands of nodes.
- Set up HDFS to operate as storage engine for on-premise Spark deployments.
- Set up Spark to access alternative storage solutions such as Amazon S3 and NoSQL database systems such as Redis, Elasticsearch, Couchbase, Aerospike, etc.
- Carry out administrative tasks such as provisioning, management, monitoring and securing an Apache Hadoop cluster.
By the end of this training, participants will be able to:
- Install and configure Apache Hadoop.
- Understand the four major components in the Hadoop ecoystem: HDFS, MapReduce, YARN, and Hadoop Common.
- Use Hadoop Distributed File System (HDFS) to scale a cluster to hundreds or thousands of nodes.
- Set up HDFS to operate as storage engine for on-premise Spark deployments.
- Set up Spark to access alternative storage solutions such as Amazon S3 and NoSQL database systems such as Redis, Elasticsearch, Couchbase, Aerospike, etc.
- Carry out administrative tasks such as provisioning, management, monitoring and securing an Apache Hadoop cluster.
14 hours
Aperçu
This instructor-led, live training in Quebec (online or onsite) is aimed at data scientists and developers who wish to use Spark NLP, built on top of Apache Spark, to develop, implement, and scale natural language text processing models and pipelines.
By the end of this training, participants will be able to:
- Set up the necessary development environment to start building NLP pipelines with Spark NLP.
- Understand the features, architecture, and benefits of using Spark NLP.
- Use the pre-trained models available in Spark NLP to implement text processing.
- Learn how to build, train, and scale Spark NLP models for production-grade projects.
- Apply classification, inference, and sentiment analysis on real-world use cases (clinical data, customer behavior insights, etc.).
By the end of this training, participants will be able to:
- Set up the necessary development environment to start building NLP pipelines with Spark NLP.
- Understand the features, architecture, and benefits of using Spark NLP.
- Use the pre-trained models available in Spark NLP to implement text processing.
- Learn how to build, train, and scale Spark NLP models for production-grade projects.
- Apply classification, inference, and sentiment analysis on real-world use cases (clinical data, customer behavior insights, etc.).
35 hours
Aperçu
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
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
21 hours
Aperçu
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.
28 hours
Aperçu
In this instructor-led, live training in Quebec, 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 (also known as Graph Analytics) 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.
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.
21 hours
Aperçu
In this instructor-led, live training in Quebec, 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 cases.
- Use different tools and techniques for big data analysis using PySpark.
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 cases.
- Use different tools and techniques for big data analysis using PySpark.
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