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. Onsite live Stream Processing training can be carried out locally on customer premises in Pakistan or in NobleProg corporate training centers in Pakistan.
NobleProg -- Your Local Training Provider
Testimonials
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
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 mostly liked the good lectures.
N. V. Nederlandse Spoorwegen
Course: Apache Ignite: Improve Speed, Scale and Availability with In-Memory Computing
Recalling/reviewing keypoints of the topics discussed.
Paolo Angelo Gaton - SMS Global Technologies Inc.
Course: Building Stream Processing Applications with Kafka Streams
-
Roxane Santiago - SMS Global Technologies Inc.
Course: Building Stream Processing Applications with Kafka Streams
The lab exercises. Applying the theory from the first day in subsequent days.
Dell
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
I genuinely liked work exercises with cluster to see performance of nodes across cluster and extended functionality.
CACI Ltd
Course: Apache NiFi for Developers
The trainers in depth knowledge of the subject
CACI Ltd
Course: Apache NiFi for Administrators
Ajay was a very experienced consultant and was able to answer all our questions and even made suggestions on best practices for the project we are currently engaged on.
CACI Ltd
Course: Apache NiFi for Administrators
That I had it in the first place.
Peter Scales - CACI Ltd
Course: Apache NiFi for Developers
The NIFI workflow excercises
Politiets Sikkerhetstjeneste
Course: Apache NiFi for Administrators
answers to our specific questions
MOD BELGIUM
Course: Apache NiFi for Administrators
Exercises.
David Lehotak - NVision Czech Republic ICT a.s.
Course: Apache Ignite for Developers
Training topics and engagement of the trainer
Izba Administracji Skarbowej w Lublinie
Course: Apache NiFi for Administrators
Machine Translated
Communication with people attending training.
Andrzej Szewczuk - Izba Administracji Skarbowej w Lublinie
Course: Apache NiFi for Administrators
Machine Translated
usefulness of exercises
Algomine sp.z.o.o sp.k.
Course: Apache NiFi for Administrators
Machine Translated
I really enjoyed the training. Anton has a lot of knowledge and laid out the necessary theory in a very accessible way. It is great that the training was a lot of interesting exercises, so we have been in contact with the technology we know from the very beginning.
Szymon Dybczak - Algomine sp.z.o.o sp.k.
Course: Apache NiFi for Administrators
Machine Translated
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 Pakistan
Stream Processing Course Outlines in Pakistan
Course Name
Duration
Overview
Course Name
Duration
Overview
14 hours
Apache Samza is an opensource nearrealtime, 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 instructorled, live training introduces the principles behind messaging systems and distributed stream processing, while walking participants through the creation of a sample Samzabased 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 nearrealtime 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 handson practice
.
14 hours
Tigon is an opensource, realtime, lowlatency, highthroughput, 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 realtime recommendations to users
This instructorled, live training introduces Tigon's approach to blending realtime 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 handson practice
.
7 hours
In this instructorled, live training, participants will learn the core concepts behind MapR Stream Architecture as they develop a realtime streaming application
By the end of this training, participants will be able to build producer and consumer applications for realtime stream data procesing
Audience
Developers
Administrators
Format of the course
Part lecture, part discussion, exercises and heavy handson practice
Note
To request a customized training for this course, please contact us to arrange
.
7 hours
Kafka Streams is a clientside 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 instructorled, 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 handson practice
Notes
To request a customized training for this course, please contact us to arrange
.
21 hours
In this instructor-led, live training in Pakistan (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 Pakistan (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 Pakistan 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 Pakistan (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 Pakistan, 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 YARNnative platform that unifies stream and batch processing It processes big datainmotion in a way that is scalable, performant, faulttolerant, stateful, secure, distributed, and easily operable
This instructorled, 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 realtime 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 reuse existing Java code
Integrate Apex into other applications as a processing engine
Tune, test and scale Apex applications
Audience
Developers
Enterprise architects
Format of the course
Part lecture, part discussion, exercises and heavy handson practice
.
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 backends: 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 instructorled, 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
Audience
Developers
Format of the Course
Part lecture, part discussion, exercises and heavy handson practice
Note
This course will be available Scala in the future Please contact us to arrange
.
14 hours
In this instructor-led, live training in Pakistan, 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 Pakistan (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 Pakistan (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.
Last Updated:
Other countries
Consulting in Pakistan
Online Stream Processing courses, Weekend Stream Processing courses, Evening Stream Processing training, Stream Processing boot camp, Stream Processing instructor-led, Weekend Stream Processing training, Evening Stream Processing courses, Stream Processing coaching, Stream Processing instructor, Stream Processing trainer, Stream Processing training courses, Stream Processing classes, Stream Processing on-site, Stream Processing private courses, Stream Processing one on one training
