Data Streaming and Real Time Data Processing Training Course

This instructor-led, live training in Sweden (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 Sweden (online or onsite) is aimed at beginner-level data professionals who wish 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.

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 different environments.
• Create, manage, and manipulate Iceberg tables.
• Understand the process of migrating data from other table formats to Iceberg.

This instructor-led live training in Sweden (online or onsite) targets intermediate-level data scientists and engineers who intend to utilise 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 aimed at 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 constructing scalable data processing and Machine Learning workflows with PySpark. Attendees will gain insight into how Apache Spark functions within contemporary Big Data ecosystems and learn to process substantial datasets efficiently by leveraging distributed computing principles.

This hands-on, three-day programme is dedicated to constructing and fine-tuning high-performance data-processing workloads by leveraging PySpark, Pandas and Polars within Kubernetes-based ecosystems.

Attendees will cultivate a practical grasp of how Spark applications are executed on Kubernetes, gaining insight into how application-level configuration choices directly impact performance, scalability, resource utilisation and overall costs. Key optimisation topics covered include executor sizing, memory allocation, dynamic allocation, partitioning strategies, shuffle mechanics, the challenges of small files, and efficient Parquet processing.

The curriculum also tackles common pain points associated with Pandas, such as memory constraints and out-of-memory errors, while introducing Polars as a high-performance alternative for specific data-processing tasks. Through practical exercises, participants will learn to diagnose performance and memory bottlenecks, evaluate various configuration strategies, and apply optimisation techniques to realistic ETL and machine learning scenarios.

The core focus of the course is on practical decision-making: mastering the ability to identify performance bottlenecks, select the most appropriate tools, configure Spark efficiently, and strike a balance between performance gains and infrastructure resource consumption and cost.

This instructor-led, live training in Sweden (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 live, instructor-led session in Sweden (online or onsite) caters to beginner to intermediate system administrators seeking to deploy, sustain, and refine Spark clusters.

By the conclusion of this program, learners will gain the ability to:

• Deploy and configure Apache Spark within various settings.
• Oversee cluster assets and track Spark application activities.
• Boost the throughput of Spark clusters.
• Establish security controls and maintain high availability.
• Diagnose and fix typical Spark operational challenges.

In this live, instructor-led session held in Sweden, participants will gain insights into utilizing Python and Spark for big data analysis while engaging in practical, hands-on exercises.

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

• Utilize Spark alongside Python for Big Data analysis.
• Complete exercises that simulate real-world challenges.
• Employ diverse tools and techniques for big data analysis using PySpark.

This instructor-led, live training in Sweden (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.