Available online or on-site, our instructor-led live Apache Hadoop training courses provide interactive, hands-on practice that highlights the core components of the Hadoop ecosystem. We demonstrate how these technologies can be effectively applied to resolve large-scale challenges.
Hadoop training is offered as either “online live training” or “onsite live training.” Online live training (also referred to as “remote live training”) is conducted via an interactive remote desktop session. Onsite live training can be delivered locally at your customer premises in Nepal or at NobleProg corporate training centers in Nepal.
NobleProg -- Your Local Training Provider
Nepal, Kathmandu - Classroom
near Soaltee, Tahachal Marg, Kathmandu, Nepal, 44600
Set in Kathmandu, this classroom is well located near Tahachal Marg 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.
Nepal, Thamel, KTM - Classroom
near Radisson , Ward 2, Kathmandu, Nepal, 44600
Set in Kathmandu, this classroom is well located near Thamel, 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.
This instructor-led, live training in Nepal (online or onsite) is aimed at developers who wish to use and integrate Spark, Hadoop, and Python to process, analyze, and transform large and complex data sets.
By the end of this training, participants will be able to:
Set up the necessary environment to start processing big data with Spark, Hadoop, and Python.
Understand the features, core components, and architecture of Spark and Hadoop.
Learn how to integrate Spark, Hadoop, and Python for big data processing.
Explore the tools in the Spark ecosystem (Spark MlLib, Spark Streaming, Kafka, Sqoop, Kafka, and Flume).
Build collaborative filtering recommendation systems similar to Netflix, YouTube, Amazon, Spotify, and Google.
Use Apache Mahout to scale machine learning algorithms.
Apache NiFi is an open-source platform for data integration and event processing that relies on flow-based architecture. It facilitates automated, real-time data routing, transformation, and system mediation between disparate systems, featuring a web-based UI and fine-grained control capabilities.
This instructor-led live training (available onsite or remotely) is designed for intermediate-level administrators and engineers who aim to deploy, manage, secure, and optimize NiFi dataflows in production environments.
Upon completion of this training, participants will be capable of:
Installing, configuring, and maintaining Apache NiFi clusters.
Designing and managing dataflows connecting various sources and sinks.
Implementing logic for flow automation, routing, and transformation.
Optimizing performance, monitoring operations, and resolving issues.
Course Format
Interactive lectures accompanied by discussions on real-world architecture.
Hands-on labs focused on building, deploying, and managing flows.
Scenario-based exercises conducted within a live-lab environment.
Course Customization Options
For custom training requests regarding this course, please reach out to us to make arrangements.
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Testimonials (2)
Hands on exercises. Class should have been 5 days, but the 3 days helped to clear up a lot of questions that I had from working with NiFi already
James - BHG Financial
Course - Apache NiFi for Administrators
The fact that we were able to take with us most of the information/course/presentation/exercises done, so that we can look over them and perhaps redo what we didint understand first time or improve what we already did.
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