현지의 강사 생생한 Stream Processing 교육 과정은 대화식 토론과 Handson 연습을 통해 스트림 처리의 기본 및 고급 주제를 실습합니다 스트림 처리 교육은 "현장 실습"또는 "원격 실습"으로 사용할 수 있습니다 현장 실습은 고객 구내에서 현지에서 실시 할 수 있습니다 대한민국 또는 NobleProg 기업 교육 센터에서 대한민국 원격 라이브 교육은 대화 형 원격 데스크톱을 통해 수행됩니다 NobleProg 지역 교육 제공자.
Machine Translated
회원 평가
나는 이론과 실제 실험실 간의 좋은 균형을 즐겼다.
N. V. Nederlandse Spoorwegen
Course: Apache Ignite: Improve Speed, Scale and Availability with In-Memory Computing
Machine Translated
나는 일반적으로 Ignite에 대한 더 많은 이해로부터 이익을 얻었습니다.
N. V. Nederlandse Spoorwegen
Course: Apache Ignite: Improve Speed, Scale and Availability with In-Memory Computing
Machine Translated
나는 좋은 강의를 가장 좋아했다.
N. V. Nederlandse Spoorwegen
Course: Apache Ignite: Improve Speed, Scale and Availability with In-Memory Computing
Machine Translated
토론 된 주제의 키 포인트 불러오기/검토
Paolo Angelo Gaton - SMS Global Technologies Inc.
Course: Building Stream Processing Applications with Kafka Streams
Machine Translated
교육 주제 및 트레이너 참여
Izba Administracji Skarbowej w Lublinie
Course: Apache NiFi for Administrators
Machine Translated
교육에 참석 하는 사람들과의 커뮤니케이션.
Andrzej Szewczuk - Izba Administracji Skarbowej w Lublinie
Course: Apache NiFi for Administrators
Machine Translated
-
Roxane Santiago - SMS Global Technologies Inc.
Course: Building Stream Processing Applications with Kafka Streams
Machine Translated
운동의 유용성
Algomine sp.z.o.o sp.k.
Course: Apache NiFi for Administrators
Machine Translated
나는 정말 그 훈련을 즐겼다. 안톤은 많은 지식을 가지고 있으며 필요한 이론을 매우 접근하기 쉬운 방식으로 제시했다. 교육은 많은 흥미로운 연습 이었으므로 우리는 처음부터 알고있는 기술과 접촉 해 왔습니다.
Szymon Dybczak - Algomine sp.z.o.o sp.k.
Course: Apache NiFi for Administrators
Machine Translated
실험실에서 운동합니다. 다음 날 첫 날부터 이론을 적용합니다.
Dell
Course: A Practical Introduction to Stream Processing
Machine Translated
클러스터 전체의 노드 성능과 확장 기능을 확인하기 위해 클러스터 작업 실습을 정말 좋아했습니다.
CACI Ltd
Course: Apache NiFi for Developers
Machine Translated
Ajay는 경험이 풍부한 컨설턴트였으며 모든 질문에 대답하고 현재 진행중인 프로젝트의 모범 사례에 대한 제안을 할 수도있었습니다.
CACI Ltd
Course: Apache NiFi for Administrators
Machine Translated
Stream processing서브 카테고리
Stream processing코스 개요
코스 이름
Duration
Overview
코스 이름
Duration
Overview
14 시간
Overview
Apache Samza is an open-source near-realtime, asynchronous computational framework for stream processing. It uses Apache Kafka for messaging, and Apache Hadoop YARN for fault tolerance, processor isolation, security, and resource management.
This instructor-led, live training introduces the principles behind messaging systems and distributed stream processing, while walking participants through the creation of a sample Samza-based project and job execution.
By the end of this training, participants will be able to:
- Use Samza to simplify the code needed to produce and consume messages.
- Decouple the handling of messages from an application.
- Use Samza to implement near-realtime asynchronous computation.
- Use stream processing to provide a higher level of abstraction over messaging systems.
Audience
- Developers
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
This instructor-led, live training introduces the principles behind messaging systems and distributed stream processing, while walking participants through the creation of a sample Samza-based project and job execution.
By the end of this training, participants will be able to:
- Use Samza to simplify the code needed to produce and consume messages.
- Decouple the handling of messages from an application.
- Use Samza to implement near-realtime asynchronous computation.
- Use stream processing to provide a higher level of abstraction over messaging systems.
Audience
- Developers
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
14 시간
Overview
Tigon is an open-source, real-time, low-latency, high-throughput, native YARN, stream processing framework that sits on top of HDFS and HBase for persistence. Tigon applications address use cases such as network intrusion detection and analytics, social media market analysis, location analytics, and real-time recommendations to users.
This instructor-led, live training introduces Tigon's approach to blending real-time and batch processing as it walks participants through the creation a sample application.
By the end of this training, participants will be able to:
- Create powerful, stream processing applications for handling large volumes of data
- Process stream sources such as Twitter and Webserver Logs
- Use Tigon for rapid joining, filtering, and aggregating of streams
Audience
- Developers
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
This instructor-led, live training introduces Tigon's approach to blending real-time and batch processing as it walks participants through the creation a sample application.
By the end of this training, participants will be able to:
- Create powerful, stream processing applications for handling large volumes of data
- Process stream sources such as Twitter and Webserver Logs
- Use Tigon for rapid joining, filtering, and aggregating of streams
Audience
- Developers
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
7 시간
Overview
In this instructor-led, live training, participants will learn the core concepts behind MapR Stream Architecture as they develop a real-time streaming application.
By the end of this training, participants will be able to build producer and consumer applications for real-time stream data procesing.
Audience
- Developers
- Administrators
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.
By the end of this training, participants will be able to build producer and consumer applications for real-time stream data procesing.
Audience
- Developers
- Administrators
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.
7 시간
Overview
Kafka Streams is a client-side library for building applications and microservices whose data is passed to and from a Kafka messaging system. Traditionally, Apache Kafka has relied on Apache Spark or Apache Storm to process data between message producers and consumers. By calling the Kafka Streams API from within an application, data can be processed directly within Kafka, bypassing the need for sending the data to a separate cluster for processing.
In this instructor-led, live training, participants will learn how to integrate Kafka Streams into a set of sample Java applications that pass data to and from Apache Kafka for stream processing.
By the end of this training, participants will be able to:
- Understand Kafka Streams features and advantages over other stream processing frameworks
- Process stream data directly within a Kafka cluster
- Write a Java or Scala application or microservice that integrates with Kafka and Kafka Streams
- Write concise code that transforms input Kafka topics into output Kafka topics
- Build, package and deploy the application
Audience
- Developers
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
In this instructor-led, live training, participants will learn how to integrate Kafka Streams into a set of sample Java applications that pass data to and from Apache Kafka for stream processing.
By the end of this training, participants will be able to:
- Understand Kafka Streams features and advantages over other stream processing frameworks
- Process stream data directly within a Kafka cluster
- Write a Java or Scala application or microservice that integrates with Kafka and Kafka Streams
- Write concise code that transforms input Kafka topics into output Kafka topics
- Build, package and deploy the application
Audience
- Developers
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
21 시간
Overview
In this instructor-led, live training in 대한민국 (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.
14 시간
Overview
This instructor-led, live training (online or onsite) is aimed at engineers who wish to use Confluent (a distribution of Kafka) to build and manage a real-time data processing platform for their applications.
By the end of this training, participants will be able to:
- Install and configure Confluent Platform.
- Use Confluent's management tools and services to run Kafka more easily.
- Store and process incoming stream data.
- Optimize and manage Kafka clusters.
- Secure data streams.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- This course is based on the open source version of Confluent: Confluent Open Source.
- To request a customized training for this course, please contact us to arrange.
By the end of this training, participants will be able to:
- Install and configure Confluent Platform.
- Use Confluent's management tools and services to run Kafka more easily.
- Store and process incoming stream data.
- Optimize and manage Kafka clusters.
- Secure data streams.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- This course is based on the open source version of Confluent: Confluent Open Source.
- To request a customized training for this course, please contact us to arrange.
7 시간
Overview
This instructor-led, live training in 대한민국 (online or onsite) is aimed at data engineers, data scientists, and programmers who wish to use Apache Kafka features in data streaming with Python.
By the end of this training, participants will be able to use Apache Kafka to monitor and manage conditions in continuous data streams using Python programming.
By the end of this training, participants will be able to use Apache Kafka to monitor and manage conditions in continuous data streams using Python programming.
28 시간
Overview
This instructor-led, live training in 대한민국 introduces the principles and approaches behind distributed stream and batch data processing, and walks participants through the creation of a real-time, data streaming application in Apache Flink.
21 시간
Overview
In this instructor-led, live training in 대한민국 (onsite or remote), participants will learn how to deploy and manage Apache NiFi in a live lab environment.
By the end of this training, participants will be able to:
- Install and configure Apachi NiFi.
- Source, transform and manage data from disparate, distributed data sources, including databases and big data lakes.
- Automate dataflows.
- Enable streaming analytics.
- Apply various approaches for data ingestion.
- Transform Big Data and into business insights.
By the end of this training, participants will be able to:
- Install and configure Apachi NiFi.
- Source, transform and manage data from disparate, distributed data sources, including databases and big data lakes.
- Automate dataflows.
- Enable streaming analytics.
- Apply various approaches for data ingestion.
- Transform Big Data and into business insights.
7 시간
Overview
In this instructor-led, live training in 대한민국, participants will learn the fundamentals of flow-based programming as they develop a number of demo extensions, components and processors using Apache NiFi.
By the end of this training, participants will be able to:
- Understand NiFi's architecture and dataflow concepts.
- Develop extensions using NiFi and third-party APIs.
- Custom develop their own Apache Nifi processor.
- Ingest and process real-time data from disparate and uncommon file formats and data sources.
By the end of this training, participants will be able to:
- Understand NiFi's architecture and dataflow concepts.
- Develop extensions using NiFi and third-party APIs.
- Custom develop their own Apache Nifi processor.
- Ingest and process real-time data from disparate and uncommon file formats and data sources.
28 시간
Overview
Apache Storm is a distributed, real-time computation engine used for enabling real-time business intelligence. It does so by enabling applications to reliably process unbounded streams of data (a.k.a. stream processing).
"Storm is for real-time processing what Hadoop is for batch processing!"
In this instructor-led live training, participants will learn how to install and configure Apache Storm, then develop and deploy an Apache Storm application for processing big data in real-time.
Some of the topics included in this training include:
- Apache Storm in the context of Hadoop
- Working with unbounded data
- Continuous computation
- Real-time analytics
- Distributed RPC and ETL processing
Request this course now!
Audience
- Software and ETL developers
- Mainframe professionals
- Data scientists
- Big data analysts
- Hadoop professionals
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
"Storm is for real-time processing what Hadoop is for batch processing!"
In this instructor-led live training, participants will learn how to install and configure Apache Storm, then develop and deploy an Apache Storm application for processing big data in real-time.
Some of the topics included in this training include:
- Apache Storm in the context of Hadoop
- Working with unbounded data
- Continuous computation
- Real-time analytics
- Distributed RPC and ETL processing
Request this course now!
Audience
- Software and ETL developers
- Mainframe professionals
- Data scientists
- Big data analysts
- Hadoop professionals
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
21 시간
Overview
Apache Apex is a YARN-native platform that unifies stream and batch processing. It processes big data-in-motion in a way that is scalable, performant, fault-tolerant, stateful, secure, distributed, and easily operable.
This instructor-led, live training introduces Apache Apex's unified stream processing architecture, and walks participants through the creation of a distributed application using Apex on Hadoop.
By the end of this training, participants will be able to:
- Understand data processing pipeline concepts such as connectors for sources and sinks, common data transformations, etc.
- Build, scale and optimize an Apex application
- Process real-time data streams reliably and with minimum latency
- Use Apex Core and the Apex Malhar library to enable rapid application development
- Use the Apex API to write and re-use existing Java code
- Integrate Apex into other applications as a processing engine
- Tune, test and scale Apex applications
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 Apache Apex's unified stream processing architecture, and walks participants through the creation of a distributed application using Apex on Hadoop.
By the end of this training, participants will be able to:
- Understand data processing pipeline concepts such as connectors for sources and sinks, common data transformations, etc.
- Build, scale and optimize an Apex application
- Process real-time data streams reliably and with minimum latency
- Use Apex Core and the Apex Malhar library to enable rapid application development
- Use the Apex API to write and re-use existing Java code
- Integrate Apex into other applications as a processing engine
- Tune, test and scale Apex applications
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.
14 시간
Overview
Apache Beam is an open source, unified programming model for defining and executing parallel data processing pipelines. It's power lies in its ability to run both batch and streaming pipelines, with execution being carried out by one of Beam's supported distributed processing back-ends: Apache Apex, Apache Flink, Apache Spark, and Google Cloud Dataflow. Apache Beam is useful for ETL (Extract, Transform, and Load) tasks such as moving data between different storage media and data sources, transforming data into a more desirable format, and loading data onto a new system.
In this instructor-led, live training (onsite or remote), participants will learn how to implement the Apache Beam SDKs in a Java or Python application that defines a data processing pipeline for decomposing a big data set into smaller chunks for independent, parallel processing.
By the end of this training, participants will be able to:
- Install and configure Apache Beam.
- Use a single programming model to carry out both batch and stream processing from withing their Java or Python application.
- Execute pipelines across multiple environments.
Format of the Course
- Part lecture, part discussion, exercises and heavy hands-on practice
Note
- This course will be available Scala in the future. Please contact us to arrange.
In this instructor-led, live training (onsite or remote), participants will learn how to implement the Apache Beam SDKs in a Java or Python application that defines a data processing pipeline for decomposing a big data set into smaller chunks for independent, parallel processing.
By the end of this training, participants will be able to:
- Install and configure Apache Beam.
- Use a single programming model to carry out both batch and stream processing from withing their Java or Python application.
- Execute pipelines across multiple environments.
Format of the Course
- Part lecture, part discussion, exercises and heavy hands-on practice
Note
- This course will be available Scala in the future. Please contact us to arrange.
14 시간
Overview
In this instructor-led, live training in 대한민국, participants will learn the principles behind persistent and pure in-memory storage as they step through the creation of a sample in-memory computing project.
By the end of this training, participants will be able to:
- Use Ignite for in-memory, on-disk persistence as well as a purely distributed in-memory database.
- Achieve persistence without syncing data back to a relational database.
- Use Ignite to carry out SQL and distributed joins.
- Improve performance by moving data closer to the CPU, using RAM as a storage.
- Spread data sets across a cluster to achieve horizontal scalability.
- Integrate Ignite with RDBMS, NoSQL, Hadoop and machine learning processors.
By the end of this training, participants will be able to:
- Use Ignite for in-memory, on-disk persistence as well as a purely distributed in-memory database.
- Achieve persistence without syncing data back to a relational database.
- Use Ignite to carry out SQL and distributed joins.
- Improve performance by moving data closer to the CPU, using RAM as a storage.
- Spread data sets across a cluster to achieve horizontal scalability.
- Integrate Ignite with RDBMS, NoSQL, Hadoop and machine learning processors.
7 시간
Overview
This instructor-led, live training in 대한민국 (online or onsite) is aimed at developers who wish to implement Apache Kafka stream processing without writing code.
By the end of this training, participants will be able to:
- Install and configure Confluent KSQL.
- Set up a stream processing pipeline using only SQL commands (no Java or Python coding).
- Carry out data filtering, transformations, aggregations, joins, windowing, and sessionization entirely in SQL.
- Design and deploy interactive, continuous queries for streaming ETL and real-time analytics.
By the end of this training, participants will be able to:
- Install and configure Confluent KSQL.
- Set up a stream processing pipeline using only SQL commands (no Java or Python coding).
- Carry out data filtering, transformations, aggregations, joins, windowing, and sessionization entirely in SQL.
- Design and deploy interactive, continuous queries for streaming ETL and real-time analytics.
7 시간
Overview
This instructor-led, live training in 대한민국 (online or onsite) is aimed at data engineers, data scientists, and programmers who wish to use Spark Streaming features in processing and analyzing real-time data.
By the end of this training, participants will be able to use Spark Streaming to process live data streams for use in databases, filesystems, and live dashboards.
By the end of this training, participants will be able to use Spark Streaming to process live data streams for use in databases, filesystems, and live dashboards.
Last Updated:
향후Stream processing 코스
27-12-2021 10:00
14 hours
05-01-2022 10:00
14 hours
10-01-2022 10:00
21 hours
27-01-2022 10:00
7 hours
27-01-2022 10:00
7 hours
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