Online or onsite, instructor-led live Stream Processing training courses demonstrate through interactive discussion and hands-on practice the fundamentals and advanced topics of Stream Processing.
Stream Processing training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Kazakhstan onsite live Stream Processing trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Testimonials
Recalling/reviewing keypoints of the topics discussed.
Paolo Angelo Gaton - SMS Global Technologies Inc.
Course: Building Stream Processing Applications with Kafka Streams
Sufficient hands on, trainer is knowledgable
Chris Tan
Course: A Practical Introduction to Stream Processing
The trainer was passionate and well-known what he said I appreciate his help and answers all our questions and suggested cases.
Course: A Practical Introduction to Stream Processing
The lab exercises. Applying the theory from the first day in subsequent days.
Dell
Course: A Practical Introduction to Stream Processing
I like how he was able to elaborate about Nifi and how powerful it is. You can basically use it for any infrastructure and use many different computer languages. Also i was glad we were able to fix the Nifi cert renewal issue we were having with the Truststore.
Joachim Martin - Jacob Jaskolka, BHG Financial
Course: Apache NiFi for Administrators
general knowledge and the possibilities that the training offered in terms on the tool.
Nalfis Tobar - Jacob Jaskolka, BHG Financial
Course: Apache NiFi for Administrators
The working sessions where we worked on real issues we are trying to solve and built out solutions together.
Jacob Jaskolka, BHG Financial
Course: Apache NiFi for Administrators
Hands on
Isaac Hastings, New Zealand Defence Force
Course: Apache NiFi for Administrators
Hands on.
Dwayne McDonald - Isaac Hastings, New Zealand Defence Force
Course: Apache NiFi for Administrators
Virtual environment working well and trainer positive attitude
Wojciech Lukawski - Orsted Polska sp. z o.o.
Course: Apache NiFi for Developers
I liked trainer's attitude and choice of examples. Trainer was very willing to help and answer questions. Trainer tried to go with as many examples as possible, even though we were short on time.
Waldemar Sobiecki - Orsted Polska sp. z o.o.
Course: Apache NiFi for Developers
Excersises, working with actual nifi. It was working very well - working on live, virtual machines.
Orsted Polska sp. z o.o.
Course: Apache NiFi for Developers
The trainer is very polite, tolerant, helpful. I was not afraid to ask for help when I couldn't handle something myself. I like that most of the exercises we did together without splitting into separate groups or by ourselves. That way we could ask questions on regular basis.
Elżbieta Doniek - Orsted Polska sp. z o.o.
Course: Apache NiFi for Developers
Very practical, a lot of exercises during the training
Andrii Feshchenko - Orsted Polska sp. z o.o.
Course: Apache NiFi for Developers
Developing a Custom Apache Nifi Processor using JSON provided a useful demostration of how we can use NiFi to transform data before forwarding to our analytical tools.
Kenny MacLeod - MOD A BLOCK
Course: Apache NiFi for Developers
James answered my every question, was extremely patient and explained me everything. NIFI was Greek and latin for me and i have learnt what was a processor, flunnel and the root process from Beginner level to Advanced Level.
Firdous Hashim Ali - MOD A BLOCK
Course: Apache NiFi for Developers
That I had it in the first place.
Peter Scales - CACI Ltd
Course: Apache NiFi for Developers
Work exercises with cluster to see performance of nodes across cluster and extended functionality
CACI Ltd
Course: Apache NiFi for Developers
I mostly liked the good lectures.
N. V. Nederlandse Spoorwegen
Course: Apache Ignite: Improve Speed, Scale and Availability with In-Memory Computing
I generally was benefit from the more understanding of Ignite.
N. V. Nederlandse Spoorwegen
Course: Apache Ignite: Improve Speed, Scale and Availability with In-Memory Computing
I enjoyed the good balance between theory and hands-on labs.
N. V. Nederlandse Spoorwegen
Course: Apache Ignite: Improve Speed, Scale and Availability with In-Memory Computing
The trainer was passionate and well-known what he said I appreciate his help and answers all our questions and suggested cases.
Course: A Practical Introduction to Stream Processing
Stream Processing Subcategories in Kazakhstan
Stream Processing Course Outlines in Kazakhstan
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.
- Developers
- 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
- Developers
- 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
- Part lecture, part discussion, exercises and heavy hands-on practice
- 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
- Developers
- Part lecture, part discussion, exercises and heavy hands-on practice
- To request a customized training for this course, please contact us to arrange
21 hours
In this instructor-led, live training in Kazakhstan (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.
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
- 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
This instructor-led, live training in Kazakhstan (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.
28 hours
This instructor-led, live training in Kazakhstan 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 Kazakhstan (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 Kazakhstan, 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 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
- Software and ETL developers
- Mainframe professionals
- Data scientists
- Big data analysts
- Hadoop professionals
- Part lecture, part discussion, exercises and heavy hands-on practice
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
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
- 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.
- Part lecture, part discussion, exercises and heavy hands-on practice
- This course will be available Scala in the future. Please contact us to arrange.
14 hours
In this instructor-led, live training in Kazakhstan, 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.
7 hours
This instructor-led, live training in Kazakhstan (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
This instructor-led, live training in Kazakhstan (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.
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