Навучальныя курсы Stream Processing

Навучальныя курсы Stream Processing

Local instructor-led live Stream Processing training courses in Беларусь.

Stream Processing Course Outlines

Course Name
Duration
Overview
Course Name
Duration
Overview
14 hours
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
14 hours
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
7 hours
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.
7 hours
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
21 hours
In this instructor-led, live training in Беларусь (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.
14 hours
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.
7 hours
Apache Kafka з'яўляецца платформай для працэдуры працэдуры працэдуры працэдуры працэдуры працэдуры працэдуры працэдуры працэдуры працэдуры працэдуры працэдуры працэдуры працэдуры працэдуры працэдуры працэдуры працэдуры. Apache Kafka можа быць інтэгравана з даступнымі мовамі праграмавання, такія як Python.

Гэта інструктар-праведзены, жывы трэнінг (онлайн або на сайце) звязаны з інжынерамі дадзеных, дадзенымі навукоўцамі і праграмамі, якія хацелі выкарыстоўваць Apache Kafka функцыі ў дадзеных перадачы з Python.

У канцы гэтага трэніравання, удзельнікі будуць ў змозе выкарыстоўваць Apache Kafka для прагляду і кіравання умовах у працягваных дадзеных потоках з дапамогай Python праграмавання.

Формат курса

Інтэрактыўныя лекцыі і дискусіі. Многія практыкаванні і практыкаванні. Вынікі ў Live-Lab Environment.

Вынікі пошуку - Customization options

Калі вы хочаце падзяліцца сваёй думкай з майстрам, рабіце гэта максімальна ветліва.
28 hours
This instructor-led, live training in Беларусь 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 hours
In this instructor-led, live training in Беларусь (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.
7 hours
In this instructor-led, live training in Беларусь, 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.
28 hours
Apache Storm з'яўляецца распаўсюджаным, рэальным часам камп'ютарным матарам, які выкарыстоўваецца для ажыццяўлення рэальнага часу бізнес-інтэлекту. Засяроджанымі на тых пытаннях, якія недастаткова асветлены і/або з’яўляюцца адпрэчанымі. Прамысловы працэс).

"Storm з'яўляецца для працэдуры ў рэальным часе, што Hadoop з'яўляецца для працэдуры аб'ектаў!"

У гэтым інструктар-праведзены жывы трэнінг, удзельнікі навучаюцца, як ўсталяваць і наладзіць Apache Storm, а затым распрацоўваць і распаўсюджваць Apache Storm прыклад для працэдуры вялікіх дадзеных у рэальным часе.

Некаторыя тэмы, якія ўключаюцца ў гэтую навучанне, з'яўляюцца:

Apache Storm У дадзеным выпадку Hadoop Працаваць з небяспечнымі дадзенымі Сцягнуць рахункі Аналіз рэальнага часу Распаўсюджаныя RPC і ETL працэдуры

Запрашаем на гэты курс зараз!

Адукацыя

Праграмнае забеспячэнне і ETL Магілёўскія працэдуры дадзеных навукоўцы Big Data аналітыкі Hadoop Прафесійнасць

Формат курса

Частка лекцыі, Частка дискусіі, практыкаванні і цяжкія практыкаванні
21 hours
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.
14 hours
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.
14 hours
Афарызм (гр. aphorismos - выказванне) - выслоўе, у якім у трапнай, лаканічнай форме выказана значная і арыгінальная думка.

У гэтым інструктар-праведзены, жывы трэнінг, удзельнікі будуць даведацца пра правілы за працягваным і чыстым памяць, як яны праходзяць па стварэнні праекта выпрабавання ў памяць.

У канцы гэтага трэніру ўдзельнікі зможаць:

Выкарыстоўвайце Ignite для in-memory, on-disk perseverance, а таксама чыста распаўсюджанай in-memory базы дадзеных. Далучайцеся да настаўніцтва без сінфігурацыі дадзеных назад да рэлацыйнай базы. Здаровая касметыка вы можаце зрабіць самі Дадатковыя функцыі ўключаюць у сябе джакузі для поўнай рэлаксацыі і камінам, каб трымаць вас у цяпле. Дадатковыя функцыі ўключаюць у сябе джакузі для поўнай рэлаксацыі і камінам. Інтэграваць Ignite з RDBMS, NoSQL, Hadoop і працэсарамі машиннага навучання.

Формат курса

Інтэрактыўныя лекцыі і дискусіі. Многія практыкаванні і практыкаванні. Вынікі ў Live-Lab Environment.

Вынікі пошуку - Customization options

Калі вы хочаце падзяліцца сваёй думкай з майстрам, рабіце гэта максімальна ветліва.
7 hours
This instructor-led, live training in Беларусь (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.
7 hours
Сярод версій гульняў онлайн call of duty можна знайсці мноства займальных і дасціпных сюжэтаў, а апошняй навінкай, выпушчанай у канцы восені гэтага года, стала гульня Call of duty. Spark Streaming Забяспечвае непаўторную працэдуру дадзеных.

Гэта інструктар-праведзены, жывы трэнінг (онлайн або на сайце) звязаны з інжынерамі дадзеных, дадзенымі навукоўцамі і праграмамі, якія хацелі выкарыстоўваць Spark Streaming функцыі ў працэсе і аналізе дадзеных у рэальным часе.

У канцы гэтага трэніравання, удзельнікі будуць ў змозе выкарыстоўваць Spark Streaming для працэсу жывых дадзеных потокаў для выкарыстання ў базах дадзеных, дадзеных сістэмах і жывых дашборках.

Формат курса

Інтэрактыўныя лекцыі і дискусіі. Многія практыкаванні і практыкаванні. Вынікі ў Live-Lab Environment.

Вынікі пошуку - Customization options

Калі вы хочаце падзяліцца сваёй думкай з майстрам, рабіце гэта максімальна ветліва.

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