Online Veliki podaci Training in Srbija

Ova obuka koju vodi instruktor, održavana uživo na Srbija (online ili na lokaciji klijenta), namenjena je naučnicima i inženjerima podataka srednjeg nivoa znanja koji žele da iskoriste Google Colab i Apache Spark za obradu i analitiku big podataka.

Na kraju ove obuke, učesnici će moći da:

• Podese okruženje za big podatke koristeći Google Colab i Spark.
• Efikasno obrade i analiziraju velike skupove podataka pomoću Apache Spark-a.
• Vizualizuju big podatke u klijentelnom okruženju.
• Integriraju Apache Spark sa alatima baziranim na oblaku.

By completing this instructor-led, live training in Srbija, participants will develop a practical, real-world grasp of Big Data, along with its associated technologies, methodologies, and tools.

Participants will have the chance to apply this knowledge through hands-on exercises. Group interaction and instructor feedback are integral components of the course.

The course begins with an introduction to fundamental Big Data concepts, then moves on to the programming languages and methodologies used for Data Analysis. Finally, we explore the tools and infrastructure that support Big Data storage, Distributed Processing, and Scalability.

This instructor-led, live training in Srbija (online or onsite) targets advanced-level data professionals who wish to optimize data processing workflows, ensure data integrity, and implement robust data lakehouse solutions that can handle the complexities of modern big data applications.

By the end of this training, participants will be able to:

• Gain an in-depth understanding of Iceberg’s architecture, including metadata management and file layout.
• Configure Iceberg for optimal performance in various environments and integrate it with multiple data processing engines.
• Manage large-scale Iceberg tables, perform complex schema changes, and handle partition evolution.
• Master techniques to optimize query performance and data scan efficiency for large datasets.
• Implement mechanisms to ensure data consistency, manage transactional guarantees, and handle failures in distributed environments.

This instructor-led live training in Srbija (online or onsite) is intended for beginner-level data professionals who want to acquire the knowledge and skills needed to effectively use Apache Iceberg for managing large-scale datasets, ensuring data integrity, and optimizing data processing workflows.

By the end of this training, participants will be able to:

• Gain a thorough understanding of Apache Iceberg's architecture, features, and benefits.
• Learn about table formats, partitioning, schema evolution, and time travel capabilities.
• Install and configure Apache Iceberg in various environments.
• Create, manage, and manipulate Iceberg tables.
• Understand the process of migrating data from other table formats to Iceberg.

Ovaj instruktorski, uživo obuka u Srbija (online ili na licu mesta) namenjena je IT profesionalcima srednjeg nivoa znanja koji žele da unaprede svoje veštine u oblastima arhitekture podataka, upravljanja podacima, računarstva u oblaku i tehnologija velikih podataka kako bi efikasno upravljali i analizirali velike skupove podataka u svrhu migracije podataka unutar svojih organizacija.

Na kraju ove obuke, učesnici će moći:

• Da razumeju osnovne koncepte i komponente različitih arhitektura podataka.
• Da steknu sveobuhvatan uvid u principe upravljanja podacima i njihovu važnost u regulatornom okruženju.
• Da primene i upravljaju okvirima za upravljanje podacima, kao što su Dama i Togaf.
• Da iskoriste platforme u oblaku za efikasno skladištenje, obradu i upravljanje podacima.

This instructor-led, live training in Srbija (online or onsite) targets intermediate-level data engineers looking to master Azure Data Lake Storage Gen2 for building effective data analytics solutions.

Upon completion of this training, participants will be able to:

• Comprehend the architecture and core features of Azure Data Lake Storage Gen2.
• Optimize data storage and access for improved cost-efficiency and performance.
• Integrate Azure Data Lake Storage Gen2 with other Azure services to facilitate analytics and data processing.
• Develop solutions leveraging the Azure Data Lake Storage Gen2 API.
• Troubleshoot common issues and refine storage strategies.

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.

This instructor-led live training in Srbija (online or onsite) targets intermediate-level IT professionals aiming to gain a comprehensive understanding of IBM DataStage from both administrative and development perspectives. This equips them to manage and utilize the tool effectively in their respective workplaces.

By the end of this training, participants will be able to:

• Understand the core concepts of DataStage.
• Learn how to effectively install, configure, and manage DataStage environments.
• Connect to various data sources and extract data efficiently from databases, flat files, and external sources.
• Implement effective data loading techniques.

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.

In this instructor-led live training in Srbija, participants will learn the strategic approach to Big Data technologies, assess their impact on existing processes and policies, and implement these technologies to identify criminal activity and prevent crime. We will examine case studies from law enforcement organizations worldwide to gain insights into their adoption strategies, challenges, and results.

Upon completion of this training, participants will be able to:

• Integrate Big Data technologies with traditional data collection methods to reconstruct narratives during investigations.
• Deploy industrial-grade Big Data storage and processing solutions for comprehensive data analysis.
• Develop proposals for adopting the most suitable tools and processes to facilitate a data-driven approach to criminal investigations.

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.

This instructor-led live training in Srbija (available online or on-site) is designed for technical professionals aiming to deploy Talend Open Studio for Big Data to simplify the processes of reading and processing big data.

Upon completion of this training, participants will be capable of:

• Installing and configuring Talend Open Studio for Big Data.
• Establishing connections with big data platforms such as Cloudera, HortonWorks, MapR, Amazon EMR, and Apache.
• Gaining a thorough understanding of and configuring the big data components and connectors within Open Studio.
• Setting parameters to automatically generate MapReduce code.
• Utilizing Open Studio's drag-and-drop interface to execute Hadoop jobs.
• Creating prototypes for big data pipelines.
• Automating big data integration workflows.