Stream Processing Training Courses

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.

Stream Processing refers to the real-time processing of "data in motion", that is, performing computations on data as it is being received. Such data is read as continuous streams from data sources such as sensor events, website user activity, financial trades, credit card swipes, click streams, etc. Stream Processing frameworks are able to read large volumes of incoming data and provide valuable insights almost instantaneously. In this instructor-led, live training (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.

Audience

• Developers
• Software architects

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.

Apache Kafka е платформа за обработка на потока с отворен код, която осигурява бърза, надеждна и ниска латентност платформа за обработка на анализ на данни в реално време. Apache Kafka може да бъде интегриран с наличните езици за програмиране като Python. Това обучение, ръководено от инструктори (онлайн или онлайн) е насочено към инженери на данни, учени на данни и програмисти, които искат да използват Apache Kafka функции в потока на данни с Python. До края на обучението участниците ще могат да използват Apache Kafka за наблюдение и управление на условията в непрекъснати потоци от данни, като използват Python програмиране. Формат на курса

Интерактивна лекция и дискусия. Много упражнения и упражнения. Изпълнение на ръката в живо лабораторна среда.

Опции за персонализиране на курса

За да поискате персонализирано обучение за този курс, моля, свържете се с нас, за да организирате.

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 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.

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

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

Apache Flink е рамка с отворен код за мащабируема поточна и пакетна обработка на данни.Това водено от инструктор обучение на живо (онлайн или на място) въвежда принципите и подходите зад обработката на разпределен поток и пакетна обработка на данни и превежда участниците през създаването на приложение за стрийминг на данни в реално време в Apache Flink.До края на това обучение участниците ще могат:

Настройте среда за разработване на приложения за анализ на данни. Разберете как работи библиотеката за обработка на графики на Apache Flink (Gelly). Пакетирайте, изпълнявайте и наблюдавайте Flink-базирани, устойчиви на грешки приложения за поточно предаване на данни. Управлявайте различни натоварвания. Извършване на разширен анализ. Настройте Flink клъстер с множество възли. Измерете и оптимизирайте производителността. Интегрирайте Flink с различни Big Data системи. Сравнете възможностите на Flink с тези на други рамки за обработка на големи данни.

Формат на курса

Интерактивна лекция и дискусия. Много упражнения и практика. Практическо внедряване в лабораторна среда на живо.

Опции за персонализиране на курса

За да поискате персонализирано обучение за този курс, моля свържете се с нас, за да уговорим.

Apache NiFi (Hortonworks DataFlow) is a real-time integrated data logistics and simple event processing platform that enables the moving, tracking and automation of data between systems. It is written using flow-based programming and provides a web-based user interface to manage dataflows in real time. In this instructor-led, live training (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.

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.

Apache NiFi (Hortonworks DataFlow) is a real-time integrated data logistics and simple event processing platform that enables the moving, tracking and automation of data between systems. It is written using flow-based programming and provides a web-based user interface to manage dataflows in real time. In this instructor-led, live training, 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.

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.

Apache Storm е разпределен, реално време компютърни двигател, използван за предоставяне на бизнес интелигентност в реално време. Това се прави, като позволява на приложенията надеждно да обработват неограничени потоци от данни (напр. за обработка на потока). "Storm е за обработка в реално време, което Hadoop е за обработка на партида!" В това обучение, ръководено от инструктори, участниците ще научат как да инсталирате и конфигурирате Apache Storm, а след това да развиете и разпространявате Apache Storm приложение за обработка на големи данни в реално време. Някои от темите, включени в това обучение включват:

Apache Storm в контекста на Hadoop Работа с неограничени данни непрекъснато изчисляване Анализ в реално време Разпределена обработка на RPC и ETL

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Софтуер и ETL разработчици Професионалисти от Mainframe Данни учени Анализ на големите данни [ 0 ] Професионалисти

Формат на курса

Разделна лекция, разделна дискусия, упражнения и тежка практика

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.

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.

Apache Ignite е изчислителна платформа в паметта, която се намира между приложението и слоя данни, за да подобри скоростта, мащаба и достъпността.Това водено от инструктор обучение на живо (онлайн или на място) е насочено към разработчици, които искат да научат принципите зад постоянното и чисто съхранение в паметта, докато преминават през създаването на примерен проект за изчисления в паметта.До края на това обучение участниците ще могат:

Използвайте Ignite за постоянство в паметта, на диска, както и за чисто разпределена база данни в паметта. Постигнете постоянство без синхронизиране на данни обратно към релационна база данни. Използвайте Ignite, за да извършите SQL и разпределени съединения. Подобрете производителността, като преместите данните по-близо до процесора, като използвате RAM като място за съхранение. Разпределете набори от данни в клъстер, за да постигнете хоризонтална мащабируемост. Интегрирайте Ignite с RDBMS, NoSQL, Hadoop и процесори за машинно обучение.

Формат на курса

Интерактивна лекция и дискусия. Много упражнения и практика. Практическо внедряване в лабораторна среда на живо.

Опции за персонализиране на курса

За да поискате персонализирано обучение за този курс, моля свържете се с нас, за да уговорим.

Confluent KSQL is a stream processing framework built on top of Apache Kafka. It enables real-time data processing using SQL operations. This instructor-led, live training (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.

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.

Apache Spark Streaming е скалираща, отворена система за обработка на потока, която позволява на потребителите да обработват данни в реално време от поддържани източници. Spark Streaming Осигурява толерантна обработка на данни. Това обучение, ръководено от инструктори (онлайн или онлайн) е насочено към инженери на данни, учени на данни и програмисти, които искат да използват Spark Streaming функции при обработката и анализирането на данни в реално време. До края на обучението, участниците ще могат да използват Spark Streaming за обработка на живите потоци от данни за използване в бази данни, файлови системи и живите панели. Формат на курса

Интерактивна лекция и дискусия. Много упражнения и упражнения. Изпълнение на ръката в живо лабораторна среда.

Опции за персонализиране на курса

За да поискате персонализирано обучение за този курс, моля, свържете се с нас, за да организирате.