Big Data Training in Varna

This instructor-led, live training in Varna (online or onsite) is aimed at intermediate-level data scientists and engineers who wish to use Google Colab and Apache Spark for big data processing and analytics.

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

• Set up 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.
• Integrate Apache Spark with cloud-based tools.

Upon completion of this instructor-led, live training in Varna, participants will acquire a practical, real-world understanding of Big Data, along with its associated technologies, methodologies, and tools.

Through hands-on exercises, participants will have the opportunity to apply this knowledge directly. Group interaction and feedback from the instructor are key components of the class experience.

The course begins with an introduction to the fundamental concepts of Big Data, then moves on to the programming languages and methodologies employed 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 Varna (online or onsite) is aimed at 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 Varna (online or onsite) targets beginner-level data professionals seeking to acquire the knowledge and skills necessary to effectively utilize Apache Iceberg for managing large-scale datasets, ensuring data integrity, and optimizing data processing workflows.

Upon completing this training, participants will be able to:

• Develop a comprehensive understanding of Apache Iceberg's architecture, features, and benefits.
• Explore table formats, partitioning strategies, schema evolution, and time travel capabilities.
• Install and configure Apache Iceberg across various environments.
• Create, manage, and manipulate Iceberg tables.
• Comprehend the process of migrating data from other table formats to Iceberg.

This instructor-led live training in Varna (online or onsite) targets intermediate-level IT professionals seeking to enhance their skills in data architecture, governance, cloud computing, and big data technologies to effectively manage and analyze large datasets for organizational data migration.

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

• Understand the foundational concepts and components of various data architectures.
• Gain a comprehensive understanding of data governance principles and their importance in regulatory environments.
• Implement and manage data governance frameworks such as DAMA and TOGAF.
• Leverage cloud platforms for efficient data storage, processing, and management.

This instructor-led live training in Varna (online or onsite) is aimed at intermediate-level data engineers who wish to learn how to use Azure Data Lake Storage Gen2 for effective data analytics solutions.

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

• Understand the architecture and key features of Azure Data Lake Storage Gen2.
• Optimize data storage and access for cost and performance.
• Integrate Azure Data Lake Storage Gen2 with other Azure services for analytics and data processing.
• Develop solutions using the Azure Data Lake Storage Gen2 API.
• Troubleshoot common issues and optimize storage strategies.

This instructor-led, live training in Varna (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 Varna (online or onsite) targets intermediate-level IT professionals seeking a comprehensive understanding of IBM DataStage from both administrative and development perspectives. This knowledge enables 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.

Conducted by a live instructor, this training on Varna (available online or on-site) is designed for system administrators with beginner to intermediate expertise who aim to deploy, maintain, and optimize Spark clusters.

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

• Install and configure Apache Spark in various environments.
• Manage cluster resources and monitor Spark applications.
• Optimize the performance of Spark clusters.
• Implement security measures and ensure high availability.
• Debug and troubleshoot common Spark issues.

In this live, instructor-led training in Varna, participants will explore how to harness the combined power of Python and Spark for big data analysis. The course emphasizes practical application through a sequence of hands-on exercises.

Upon completing this training, participants will be able to:

• Utilize Spark alongside Python to conduct comprehensive Big Data analysis.
• Complete exercises that reflect authentic, real-world data scenarios.
• Employ diverse tools and techniques for big data analysis using the PySpark framework.

In this instructor-led live training in Varna, participants will learn the mindset with which to approach Big Data technologies, assess their impact on existing processes and policies, and implement these technologies for the purpose of identifying criminal activity and preventing crime. Case studies from law enforcement organizations around the world will be examined to gain insights on their adoption approaches, challenges and results.

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

• Combine Big Data technology with traditional data gathering processes to piece together a story during an investigation.
• Implement industrial big data storage and processing solutions for data analysis.
• Prepare a proposal for the adoption of the most adequate tools and processes for enabling a data-driven approach to criminal investigation.

This instructor-led live training (online or onsite) is designed for data engineers, data analysts, and data 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 with PySpark. Attendees will gain insight into how Apache Spark functions within contemporary Big Data ecosystems and how to effectively manage large datasets by leveraging distributed computing principles.

This intensive three-day workshop is designed to help you build and refine high-performance data-processing workloads by leveraging PySpark, Pandas and Polars within Kubernetes-based environments.

Attendees will gain a functional grasp of how Spark applications operate on Kubernetes, and how specific configuration choices at the application level directly impact performance, scalability, resource utilisation and overall costs. Key optimisation topics covered include executor sizing, memory allocation strategies, dynamic allocation, partitioning logic, shuffle mechanics, resolving small-file issues and enhancing Parquet processing efficiency.

Additionally, the course tackles frequent challenges encountered with Pandas, such as memory constraints and out-of-memory errors, while introducing Polars as a high-performance solution for specific data-processing tasks. Through practical, hands-on labs, participants will learn to diagnose performance and memory bottlenecks, evaluate various configuration approaches and implement optimisation techniques in realistic ETL and machine learning contexts.

Throughout the training, the focus remains on practical decision-making: learning how to pinpoint bottlenecks, choose the right tools, configure Spark for maximum efficiency and strike a balance between performance and infrastructure costs.

This instructor-led live training in Varna (online or onsite) is designed for engineers aiming to set up and deploy an Apache Spark system for processing massive data volumes.

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

• Install and configure Apache Spark.
• Efficiently process and analyze extensive data sets.
• Distinguish between Apache Spark and Hadoop MapReduce and understand their respective use cases.
• Integrate Apache Spark with external machine learning tools.

This instructor-led live training in Varna (online or onsite) is aimed at technical professionals who wish to deploy Talend Open Studio for Big Data to simplify the process of reading and analyzing big data.

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

• Install and configure Talend Open Studio for Big Data.
• Connect with big data systems such as Cloudera, Hortonworks, MapR, Amazon EMR, and Apache.
• Understand and set up Open Studio's big data components and connectors.
• Configure parameters to automatically generate MapReduce code.
• Use Open Studio's drag-and-drop interface to run Hadoop jobs.
• Prototype big data pipelines.
• Automate big data integration projects.