地元の講師主導のApache Sparkの実習コースは、SparkがBig Dataエコシステムにどのように適合しているか、そしてデータ分析にSparkを使用する方法を実践的に実践しています。 Apache Sparkのトレーニングは、「オンサイトライブトレーニング」または「リモートライブトレーニング」として利用できます。現場での現場でのトレーニングは、 日本 NobleProgの企業研修センターで日本 。リモートライブトレーニングは、インタラクティブなリモートデスクトップを介して実行されます。 NobleProg - あなたの地域のトレーニングプロバイダー
Machine Translated
お客様の声
リチャードは、非常に落ち着いて組織的で、分析的な洞察力を持っています。まさにこの種のことを提示するのに必要な資質です。
Kieran Mac Kenna
コース: Spark for Developers
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考えられるシナリオとケース
zhaopeng liu - Fmr
コース: Spark for Developers
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ケーススタディ
国栋 张
コース: Spark for Developers
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セッションのすべての部分
Eric Han - Fmr
コース: Spark for Developers
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私たちは、環境全体についてもっと知っています。
John Kidd
コース: Spark for Developers
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トレーナーはクラスを面白くて面白くし、終日のトレーニングでかなり役立ちました。
Ryan Speelman
コース: Spark for Developers
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トレーナーは、ユーモアと現実のストーリーを組み合わせて優れたスタイルを持っていて、手元にある人に近づけることができたと思います。私はこの教授を将来的に強く勧めます。
コース: Spark for Developers
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Ernestoは、Sparkとそのさまざまなモジュールを使用するという高水準の概念について説明してくれました。
Michael Nemerouf
コース: Spark for Developers
Machine Translated
これは、私がこれまでに受けたエクササイズプログラミングコースの最も実践的なものの1つです。
Laura Kahn
コース: Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP
Machine Translated
これは私が13年のキャリアで務めた最高品質のオンライントレーニングの1つです。これからもいい結果を出し続けてください！。
コース: Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP
Machine Translated
私たちがシラバスではないことについて半関連の質問をしたかった時、リチャードは非常に喜んでいました。説明は明確で、彼は私たちに与えられたアドバイスで警告を前面に出していた。
ARM Limited
コース: Spark for Developers
Machine Translated
私は非常に好きな VM 先生は、トピックだけでなく、他のトピックについて非常に精通していた、彼は非常に素晴らしく、フレンドリーだった 私はドバイの施設が好きでした。
Safar Alqahtani - Elm Information Security
コース: Big Data Analytics in Health
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練習タスク
Pawel Kozikowski - GE Medical Systems Polska Sp. Zoo
コース: Python and Spark for Big Data (PySpark)
Machine Translated
アジャイは非常に友好的で親切であり、彼が話していたトピックについても知識がありました。
Biniam Guulay - ICE International Copyright Enterprise Germany GmbH
コース: Spark for Developers
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ラボでの演習。翌日から初日から理論を適用する。
Dell
コース: A Practical Introduction to Stream Processing
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教師は、現在のニーズに合わせてトレーニングプログラムを調整しました。
EduBroker Sp. z o.o.
コース: Python and Spark for Big Data (PySpark)
Machine Translated
同様の演習をさまざまな方法で行うことは、各コンポーネント（ Hadoop / Spark、standalone / cluster）が単独でおよび一緒に実行できることを理解するのに役立ちます。開発時とクラスターでの展開時のローカルマシンでのアプリケーションのテスト方法に関するアイデアを提供してくれました。
Thomas Carcaud - IT Frankfurt GmbH
コース: Spark for Developers
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個々の注意。
ARCHANA ANILKUMAR - PPL
コース: Python and Spark for Big Data (PySpark)
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スパークストリーミング、データベース、AWS Redshiftを学ぶために入る
Lim Meng Tee - Jobstreet.com Shared Services Sdn. Bhd.
コース: Apache Spark in the Cloud
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コンテンツと知識。
Jobstreet.com Shared Services Sdn. Bhd.
コース: Apache Spark in the Cloud
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それは非常に有益でした。 私は以前にスパークとはまだ大きな経験を持っていました。このコースは、被写体の非常に良い紹介を提供しました。
Intelligent Medical Objects
コース: Apache Spark in the Cloud
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Sparkのボンネットの下で何が起こっているのかを理解することは素晴らしかったです。フードの下で何が起こっているのかを知ることは、コードが期待していることをなぜ行っているのか、または何をしていないのかを理解するのに役立ちます。トレーニングの多くは常に素晴らしい手であり、最適化に関するセクションは私の現在の仕事に非常に関連していました。
Intelligent Medical Objects
コース: Apache Spark in the Cloud
Machine Translated
これは素晴らしいクラスです！ 私は最もアフターが非常に明確に、火花があれば、それがどこから来たのか、そして何が解決できるということを説明しています。 他の紹介よりもはるかに優れています。 ANDRASはトピックの深い知識を持っており、物事をよく説明します。
Intelligent Medical Objects
コース: Apache Spark in the Cloud
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スパークの基本的な側面を与えられた、実験したライブの例。
Intelligent Medical Objects
コース: Apache Spark in the Cloud
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1 \。 高レベルの概念と技術的な詳細の間の直流バランス。 2. ANDRASは彼の教授について非常に知識があります。 3.運動
Steven Wu - Intelligent Medical Objects
コース: Apache Spark in the Cloud
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セッション/割り当てを手に入れる
Poornima Chenthamarakshan - Intelligent Medical Objects
コース: Apache Spark in the Cloud
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トレーナーは視聴者の要求に基づいてわずかにトレーニングを調整したので、私たちが要求したいくつかの差分トピックにいくつかの
Intelligent Medical Objects
コース: Apache Spark in the Cloud
Machine Translated
彼のペースは素晴らしかったです。 私は彼が理論に入ったという事実が大好きだと、私は彼が尋ねていることをする理由を理解しています。
Intelligent Medical Objects
コース: Apache Spark in the Cloud
Machine Translated
トレーナーは、ユーモアと現実のストーリーを組み合わせて優れたスタイルを持っていて、手元にある人に近づけることができたと思います。私はこの教授を将来的に強く勧めます。
コース: Spark for Developers
Machine Translated
これは私が13年のキャリアで務めた最高品質のオンライントレーニングの1つです。これからもいい結果を出し続けてください！。
コース: Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP
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Sparkサブカテゴリ
Apache Sparkコース概要
コース名
期間
概要
コース名
期間
概要
21 時間
In this instructor-led, live training in 日本, 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.
21 時間
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 時間
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 時間
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 時間
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 時間
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 時間
In this instructor-led, live training in 日本 (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 時間
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 時間
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 時間
This instructor-led, live training in 日本 (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 時間
This instructor-led, live training in 日本 (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 時間
This instructor-led, live training in 日本 (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 時間
This instructor-led, live training in 日本 (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 時間
This instructor-led, live training in 日本 (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 時間
This instructor-led, live training in 日本 (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.).
21 時間
This instructor-led, live training in 日本 (online or onsite) is aimed at developers who wish to use and integrate Spark, Hadoop, and Python to process, analyze, and transform large and complex data sets.
By the end of this training, participants will be able to:
- Set up the necessary environment to start processing big data with Spark, Hadoop, and Python.
- Understand the features, core components, and architecture of Spark and Hadoop.
- Learn how to integrate Spark, Hadoop, and Python for big data processing.
- Explore the tools in the Spark ecosystem (Spark MlLib, Spark Streaming, Kafka, Sqoop, Kafka, and Flume).
- Build collaborative filtering recommendation systems similar to Netflix, YouTube, Amazon, Spotify, and Google.
- Use Apache Mahout to scale machine learning algorithms.
By the end of this training, participants will be able to:
- Set up the necessary environment to start processing big data with Spark, Hadoop, and Python.
- Understand the features, core components, and architecture of Spark and Hadoop.
- Learn how to integrate Spark, Hadoop, and Python for big data processing.
- Explore the tools in the Spark ecosystem (Spark MlLib, Spark Streaming, Kafka, Sqoop, Kafka, and Flume).
- Build collaborative filtering recommendation systems similar to Netflix, YouTube, Amazon, Spotify, and Google.
- Use Apache Mahout to scale machine learning algorithms.
35 時間
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 時間
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 時間
In this instructor-led, live training in 日本, 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.
Last Updated:
今後のApache Sparkコース
Apache Spark in the Cloud
23-02-2022 10:00
21 時間
Big Data Analytics in Health
23-02-2022 10:00
21 時間
Apache Spark SQL
04-03-2022 10:00
7 時間
Apache Spark Streaming with Scala
07-03-2022 10:00
21 時間
A Practical Introduction to Stream Processing
21-03-2022 10:00
21 時間
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