Data Streaming and Real Time Data Processing Training Course

This instructor-led, live training in Bulgaria (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 Bulgaria (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 Bulgaria (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.

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 Bulgaria (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.

Conducted by a live instructor, this training on Bulgaria (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 Bulgaria, 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.

This instructor-led, live training in Bulgaria (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.