Apache Spark Training in Srbija

This instructor-led live training in Srbija (available online or onsite) is targeted at intermediate-level data scientists and engineers interested in employing Google Colab and Apache Spark for big data processing and analytics.

By the conclusion of this training, participants will be equipped to:

• Configure a big data environment using Google Colab and Spark.
• Efficiently process and analyze large datasets via Apache Spark.
• Integrate Apache Spark with cloud-based tools.

This instructor-led live training in Srbija provides an in-depth look at the Stratio platform, specifically highlighting the integration of PySpark within the Rocket and Intelligence modules. Participants will develop a comprehensive mastery of data ingestion, transformation, and advanced analytics, while building practical expertise in loops, UDFs, and enterprise-level data workflows.

Ova obuka vođena instrukcijom u <lokalizaciji > (online ili uživo) namenjena je developerima koji žele da koriste i integriraju Spark, Hadoop i Python radi obrade, analize i transformacije velikih i složenih skupova podataka.

Nakon završetka ove obuke, učesnici će biti u mogućnosti da:

• Postave neophodno okruženje za početak obrade big data pomoću Sparka, Hadopa i Pythona.
• Uvvide karakteristike, ključne komponente i arhitekturu Sparka i Hadopa.
• Uče kako da integrišu Spark, Hadoop i Python radi obrade big data.
• Istraže alate iz ekosistema Sparka (Spark MLlib, Spark Streaming, Kafka, Sqoop, Kafka i Flume).
• Naprave sisteme za preporuke zasnovane na kolaborativnom filtriranju, slične onima koje koriste Netflix, YouTube, Amazon, Spotify i Google.
• Koriste Apache Mahout za skaliranje algoritama mašinskog učenja.

Ova obuka sa trenerom uživo u Srbija (online ili licno) namenjena je administratorima sistema početnog do srednjeg nivoa koji žele da postavljaju, održavaju i optimizuju Spark klasterne okruženja.

Na kraju ove obuke, učesnici će moći da:

• Instaliraju i konfigurišu Apache Spark u različitim okruženjima.
• Upravljaju resursima klastera i prate rad Spark aplikacija.
• Optimizuju performanse Spark klastera.
• Implementiraju mere bezbednosti i osiguraju visoku dostupnost.
• Otkrivaju i rešavaju uobičajene probleme u Sparku.

In this instructor-led live training held in Srbija, participants will discover how to combine Python and Spark for big data analysis while engaging in practical, hands-on activities.

By the conclusion of this program, participants will be able to:

• Leverage Spark and Python to conduct Big Data analysis.
• Complete exercises that simulate real-world business challenges.
• Employ various tools and methodologies for big data analysis via PySpark.

This instructor-led live training (online or onsite) is designed for data engineers, analysts, and professionals who wish 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 gain insights into how Apache Spark functions within contemporary Big Data ecosystems and learn to process large datasets efficiently by applying distributed computing principles.

This hands-on, three-day program is dedicated to designing and tuning high-efficiency data processing pipelines that leverage PySpark, Pandas, and Polars within Kubernetes-based ecosystems.

Learners will gain a working knowledge of how Spark applications are orchestrated on Kubernetes, with a specific focus on how configuration choices at the application layer directly impact throughput, scalability, resource utilization, and operational expenditure. Key areas of optimization covered include executor dimensioning, memory distribution, dynamic resource allocation, partitioning methodologies, shuffle mechanics, mitigating small-file issues, and streamlined Parquet data handling.

Additionally, the curriculum tackles frequent bottlenecks associated with Pandas, such as memory constraints and out-of-memory exceptions, while presenting Polars as a high-performance alternative for specific processing tasks. Through interactive lab sessions, participants will learn to pinpoint performance and memory inefficiencies, evaluate various configuration approaches, and implement optimization strategies for realistic ETL and machine learning use cases.

The core objective of this training is practical decision-making: mastering the identification of system bottlenecks, selecting the right tool for the job, configuring Spark for maximum efficiency, and striking a balance between processing speed and infrastructure resource costs.

This instructor-led, live training in Srbija (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.

Ova praktična obuka u Srbija razjašnjava Apache Spark, pokrivajući RDD-ove, DataFrame-ove i Python/Scala API-je. Polaznici će savladati cloud implementaciju koristeći Databricks, AWS EMR i Glue, razvijajući praktična veštine za stvarne zadatke u inženjerstvu podataka i DevOps-u.