Delivered either online or onsite, these instructor-led live Apache Spark training courses utilize hands-on practice to demonstrate how Spark integrates into the Big Data ecosystem and how to leverage it for data analysis.
Apache Spark training is available as "online live training" or "onsite live training". Online live training (also known as "remote live training") is conducted via an interactive remote desktop. Onsite live training can be conducted locally on customer premises in Transylvania Central or at NobleProg corporate training centers in Transylvania Central.
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 Central (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.
This instructor-led live training in Transylvania Central covers the Stratio platform, focusing on the Rocket and Intelligence modules with PySpark. Participants will master data ingestion, transformation, and advanced analytics, gaining practical skills in loops, UDFs, and enterprise data workflows.
This instructor-led, live training in Transylvania Central (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 live, instructor-led training in Transylvania Central, delivered either online or on-site, is tailored for beginner to intermediate system administrators seeking to deploy, maintain, and optimize Spark clusters.
By the conclusion of this program, participants will be equipped to:
Install and configure Apache Spark in a variety of environments.
Manage cluster resources and monitor Spark application activity.
Optimize the performance of Spark clusters.
Implement security measures and ensure high availability.
Debug and troubleshoot common Spark-related issues.
In this live, instructor-led training in Transylvania Central, participants will develop the skills to combine Python and Spark for big data analysis through practical, hands-on exercises.
Upon completion, participants will be equipped to:
Utilize Spark alongside Python to effectively analyze Big Data.
Engage in exercises that simulate real-world data scenarios.
Apply diverse tools and techniques for big data analysis leveraging PySpark.
This instructor-led live training (online or onsite) targets data engineers, data analysts, and data professionals who aim to use Databricks and PySpark to build scalable data pipelines and migrate existing SQL workflows.
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 Central (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.
This hands-on training in Transylvania Central demystifies Apache Spark, covering RDDs, DataFrames, and Python/Scala APIs. Participants will master cloud deployment with Databricks, AWS EMR, and Glue, building practical skills for real-world data engineering and DevOps tasks effectively.
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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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