Whether conducted online or on-site, instructor-led live Apache Spark training courses illustrate through practical exercises how Spark integrates into the Big Data ecosystem and demonstrate its application in data analysis.
Apache Spark training is offered in two formats: "online live training" and "onsite live training." Online live training (also known as "remote live training") is delivered via an interactive remote desktop connection. Onsite live training can take place locally at the client's premises in Transylvania or at NobleProg's corporate training centers in Transylvania.
NobleProg -- Your Local Training Provider
Brasov, Calea Bucuresti Street
NobleProg Brasov, 13 Calea Bucuresti Street Sacele, Brasov, romania, 505600
The hotel is situated at the city entrance, being the best conference and training location in Brasov. Three conference rooms can accommodate up to 170 guests and can be modified for the company needs. These training rooms are equipped with the all necessities of a good conference or training.
Salile de training sunt situate central în Sibiu, pe principalul bulevard. Clădirea hotelului care le gazduieste este de factură clasicistă si reprezinta un punct de referinta arhitecturala a orasului.
Turnul Sfatului, Galeria de Arta Contemporana, sau parcurile Corneliu Coposu si Astra se află în imediata apropiere.
Pentru a ajunge aici de la gara, puteti merge pe jos 20min sau cu autobuzul 5 circa 10min
Locuri de parcare găsiți în parcarea supravegheata, sau pe străzile alăturate.
Cluj-Napoca, Pitești Street
NobleProg Cluj-Napoca, Strada Pitești 19, Cluj-Napoca, romania, 400124
ClujHUB has more rooms for events and training sessions, which can host from 4 to 80 people. These areas can be setup up to fit the companies need. The location is great, 10 minutes walk from the historical city center.
This instructor-led, live training in Transylvania (online or onsite) is designed for intermediate-level data scientists and engineers who intend to utilize Google Colab and Apache Spark for big data processing and analytics.
By the end of this training, participants will be able to:
Configure a big data environment using Google Colab and Spark.
Process and analyze large datasets efficiently with Apache Spark.
Visualize big data in a collaborative environment.
Stratio is a data-centric platform that combines big data, artificial intelligence, and governance into a unified solution. Its Rocket and Intelligence modules facilitate rapid data exploration, transformation, and advanced analytics within enterprise settings.
This instructor-led training session, available both online and on-site, is designed for data professionals at an intermediate level who aim to effectively utilize Stratio's Rocket and Intelligence modules with PySpark. The curriculum focuses on looping structures, user-defined functions, and complex data logic.
Upon completing this training, participants will be capable of:
Navigating and operating within the Stratio platform using the Rocket and Intelligence modules.
Applying PySpark for data ingestion, transformation, and analysis.
Utilizing loops and conditional logic to manage data workflows and feature engineering tasks.
Creating and managing user-defined functions (UDFs) to enable reusable data operations in PySpark.
Format of the Course
Interactive lectures and discussions.
Extensive exercises and practical practice.
Hands-on implementation within a live-lab environment.
Course Customization Options
To request customized training for this course, please contact us to arrange.
This instructor-led, live training in Transylvania (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.
This instructor-led, live training in Transylvania (online or onsite) is designed for beginner to intermediate system administrators who want to deploy, maintain, and optimize Spark clusters.
Upon completion of this training, participants will be able to:
Install and configure Apache Spark across various environments.
Manage cluster resources and monitor Spark applications.
Optimize the performance of Spark clusters.
Implement security measures and ensure high availability.
In this instructor-led, live training in Transylvania, participants will learn how to use Python and Spark together to analyze big data as they work on hands-on exercises.
By the end of this training, participants will be able to:
Learn how to use Spark with Python to analyze Big Data.
Work on exercises that mimic real world cases.
Use different tools and techniques for big data analysis using PySpark.
This course offers a hands-on introduction to developing scalable data processing and Machine Learning workflows using PySpark. Participants will discover how Apache Spark functions within contemporary Big Data ecosystems and learn to process large datasets efficiently by leveraging distributed computing principles.
This instructor-led, live training in Transylvania (online or onsite) is aimed at engineers who wish to set up and deploy Apache Spark system for processing very large amounts of data.
By the end of this training, participants will be able to:
Install and configure Apache Spark.
Quickly process and analyze very large data sets.
Understand the difference between Apache Spark and Hadoop MapReduce and when to use which.
Integrate Apache Spark with other machine learning tools.
Initially, the learning curve for Apache Spark can be steep, requiring significant effort before achieving tangible results. This course is designed to help learners quickly overcome that initial hurdle. Upon completion, participants will gain a solid understanding of Apache Spark fundamentals, clearly distinguish between RDDs and DataFrames, and master both the Python and Scala APIs. They will also develop a comprehensive grasp of executors, tasks, and other core concepts. In alignment with industry best practices, the course places a strong emphasis on cloud-based deployment, specifically focusing on Databricks and AWS environments. Students will also learn to differentiate between AWS EMR and AWS Glue, one of AWS's most recent Spark services.
AUDIENCE:
Data Engineers, DevOps Professionals, Data Scientists
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Testimonials (4)
A journey through the Spark world: a very intense course. DSL, spark sql, partitioning vs bucketing for me.
Georgiana Elisabeta
Course - Apache Spark Fundamentals
I liked that it was practical. Loved to apply the theoretical knowledge with practical examples.
Aurelia-Adriana - Allianz Services Romania
Course - Python and Spark for Big Data (PySpark)
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.
Raul Mihail Rat - Accenture Industrial SS
Course - Python, Spark, and Hadoop for Big Data
Having hands on session / assignments
Poornima Chenthamarakshan - Intelligent Medical Objects
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