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 trainings in Bhutan can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Bhutan, Thimphu - Classroom
near Le Méridien , Chorten Lam, Thimphu, Bhutan, 11001
Set in Thimphu, this classroom is well located in Chorten Lam with all amenities and WiFi.
For Sales Enquires and Meetings
All our centres have batches running on weekdays and weekends hence, please note that, in most cases, usually we are not able to organise ad hoc sales meetings, especially on our classrooms as they are all occupied with ongoing training sessions . Please contact us by e-mail or phone at least one day earlier to make an appointment with one of our consultants at our corporate offices.
Bhutan, Paro - Classroom
near Le Méridien Riverfront, thimphu hwy, Shaba, Paro, Bhutan, 12001
Set in Paro, this classroom is well located near Paro-Thimphu Highway around 4 km from the airport, and 7 km from Rinpung Dzong, and possess all amenities and WiFi.
For Sales Enquires and Meetings
All our centres have batches running on weekdays and weekends hence, please note that, in most cases, usually we are not able to organise ad hoc sales meetings, especially on our classrooms as they are all occupied with ongoing training sessions . Please contact us by e-mail or phone at least one day earlier to make an appointment with one of our consultants at our corporate offices.
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.
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.
This instructor-led, live training in Bhutan (online or onsite) 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.
By the end of this training, participants will be able to:
Set up an environment for developing data analysis applications.
Understand how Apache Flink's graph-processing library (Gelly) works.
Package, execute, and monitor Flink-based, fault-tolerant, data streaming applications.
Manage diverse workloads.
Perform advanced analytics.
Set up a multi-node Flink cluster.
Measure and optimize performance.
Integrate Flink with different Big Data systems.
Compare Flink capabilities with those of other big data processing frameworks.
In this instructor-led, live training in Bhutan (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.
This instructor-led, live training in Bhutan (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.
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
This instructor-led, live training in Bhutan (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.
In this instructor-led, live training in Bhutan (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.
In this instructor-led, live training in Bhutan, 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.
This instructor-led, live training in Bhutan (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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Testimonials (4)
Sufficient hands on, trainer is knowledgable
Chris Tan
Course - A Practical Introduction to Stream Processing
During the exercises, James explained me every step whereever I was getting stuck in more detail. I was completely new to NIFI. He explained the actual purpose of NIFI, even the basics such as open source. He covered every concept of Nifi starting from Beginner Level to Developer Level.
Firdous Hashim Ali - MOD A BLOCK
Course - Apache NiFi for Administrators
That I had it in the first place.
Peter Scales - CACI Ltd
Course - Apache NiFi for Developers
Recalling/reviewing keypoints of the topics discussed.
Paolo Angelo Gaton - SMS Global Technologies Inc.
Course - Building Stream Processing Applications with Kafka Streams
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