
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 India can be carried out locally on customer premises or in NobleProg corporate training centers.
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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
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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 India
Stream Processing Course Outlines in India
- 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
- 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
- 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.
- 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
- 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.
- 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.
- 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.
- 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.
- 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
- 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.
- 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.
- 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.
- 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.
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