Praktyczne szkolenia na żywo z Stream Processing to kursy, pokazujące poprzez interaktywną dyskusję i ćwiczenia zagadnienia związane ze przetwarzaniem strumieniowym.
Szkolenie Stream Processing jest dostępne jako "szkolenie stacjonarne" lub "szkolenie online na żywo".
Szkolenie stacjonarne może odbywać się lokalnie w siedzibie klienta w Polsce lub w ośrodkach szkoleniowych NobleProg w Polsce. Zdalne szkolenie online odbywa się za pomocą interaktywnego, zdalnego pulpitu DaDesktop .
NobleProg -- Twój lokalny dostawca szkoleń.
Zapytaj konsultanta NobleProg o dedykowane szkolenie dla Twojego zespołu.
Opinie uczestników
tematyka szkolenia oraz zaangażowanie trenera
Izba Administracji Skarbowej w Lublinie
Szkolenie: Apache NiFi for Administrators
Koleżeńska komunikacja z osobami biorących udział w szkoleniu.
Andrzej Szewczuk - Izba Administracji Skarbowej w Lublinie
Szkolenie: Apache NiFi for Administrators
przydatność ćwiczeń
Algomine sp.z.o.o sp.k.
Szkolenie: Apache NiFi for Administrators
Szkolenie bardzo mi się podobało. Anton posiada dużą wiedzę i wyłożył niezbędną teorię w bardzo przystępny sposób. Świetne jest to, że na szkoleniu było dużo ciekawych ćwiczeń, więc praktycznie od samego początku mamy kontakt z poznawaną technologią.
Szymon Dybczak - Algomine sp.z.o.o sp.k.
Szkolenie: Apache NiFi for Administrators
Cieszyłem się dobrą równowagą między teorią a praktycznymi laboratoriami.
N. V. Nederlandse Spoorwegen
Szkolenie: Apache Ignite: Improve Speed, Scale and Availability with In-Memory Computing
Machine Translated
Generalnie korzystałem z większego zrozumienia Ignite ...
N. V. Nederlandse Spoorwegen
Szkolenie: Apache Ignite: Improve Speed, Scale and Availability with In-Memory Computing
Machine Translated
Najbardziej podobały mi się dobre wykłady.
N. V. Nederlandse Spoorwegen
Szkolenie: Apache Ignite: Improve Speed, Scale and Availability with In-Memory Computing
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Przywoływanie/recenzowanie punktów kluczowych omawianych tematów.
Paolo Angelo Gaton - SMS Global Technologies Inc.
Szkolenie: Building Stream Processing Applications with Kafka Streams
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Roxane Santiago - SMS Global Technologies Inc.
Szkolenie: Building Stream Processing Applications with Kafka Streams
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Ćwiczenia laboratoryjne. Zastosowanie teorii od pierwszego dnia w kolejnych dniach.
Dell
Szkolenie: A Practical Introduction to Stream Processing
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Szkolenie: A Practical Introduction to Stream Processing
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Naprawdę podobały mi się ćwiczenia z klastrem, aby zobaczyć wydajność węzłów w klastrze i rozszerzoną funkcjonalność.
CACI Ltd
Szkolenie: Apache NiFi for Developers
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CACI Ltd
Szkolenie: Apache NiFi for Administrators
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Ajay był bardzo doświadczonym konsultantem i był w stanie odpowiedzieć na wszystkie nasze pytania, a nawet zasugerował najlepsze praktyki dotyczące projektu, w który jesteśmy obecnie zaangażowani.
CACI Ltd
Szkolenie: Apache NiFi for Administrators
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Peter Scales - CACI Ltd
Szkolenie: Apache NiFi for Developers
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Politiets Sikkerhetstjeneste
Szkolenie: Apache NiFi for Administrators
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MOD BELGIUM
Szkolenie: Apache NiFi for Administrators
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David Lehotak - NVision Czech Republic ICT a.s.
Szkolenie: Apache Ignite for Developers
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Szkolenie: A Practical Introduction to Stream Processing
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Podkategorie Stream processing
Plany szkoleń z technologii Stream processing
Nazwa kursu
Czas trwania
Opis
Nazwa kursu
Czas trwania
Opis
14 godzin
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 godzin
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 godzin
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 godzin
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 godzin
In this instructor-led, live training in w Polsce (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 godzin
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.
28 godzin
This instructor-led, live training in w Polsce 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 godzin
In this instructor-led, live training in w Polsce (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 godzin
In this instructor-led, live training in w Polsce, 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 godzin
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 godzin
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 godzin
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 godzin
In this instructor-led, live training in w Polsce, 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 godzin
This instructor-led, live training in w Polsce (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 godzin
This instructor-led, live training in w Polsce (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.
7 godzin
This instructor-led, live training in w Polsce (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.
Last Updated:
Nadchodzące szkolenia z technologii Stream processing
Tigon: Real-time Streaming for the Real World
08-02-2022 09:00
Warszawa, ul. Złota 3 / 11
14 godzin
Apache NiFi for Developers
09-02-2022 09:00
Warszawa, ul. Złota 3 / 11
7 godzin
Apache NiFi for Administrators
16-02-2022 09:00
Katowice ul. Stawowa 10
21 godzin
Apache NiFi for Administrators
01-03-2022 09:00
Warszawa, ul. Złota 3 / 11
21 godzin
Samza for Stream Processing
21-03-2022 09:00
Warszawa, ul. Złota 3 / 11
14 godzin
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Konsultacje
Szkolenie Stream processing, Stream processing boot camp, Szkolenia Zdalne Stream processing, szkolenie wieczorowe Stream processing, szkolenie weekendowe Stream processing, Kurs Stream processing,Kursy Stream processing, Trener Stream processing, instruktor Stream processing, kurs zdalny Stream processing, edukacja zdalna Stream processing, nauczanie wirtualne Stream processing, lekcje UML, nauka przez internet Stream processing, e-learning Stream processing, kurs online Stream processing, wykładowca Stream processing
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