Data Streaming and Real Time Data Processing Training Course

This instructor-led, live training in Latvia (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 a deep understanding of Iceberg’s architecture, including metadata management and file layout.
• Configure Iceberg for optimal performance across various environments and integrate it with multiple data processing engines.
• Manage large-scale Iceberg tables, execute complex schema changes, and manage 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 live, instructor-led training in Latvia (offered online or onsite) is tailored for beginner-level data professionals seeking to gain the expertise needed to utilize Apache Iceberg for handling large-scale datasets, safeguarding data integrity, and enhancing data processing efficiency.

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

• Build a solid grasp of Apache Iceberg's architecture, features, and benefits.
• Study table formats, partitioning, schema evolution, and time travel capabilities.
• Set up and configure Apache Iceberg in diverse environments.
• Create, manage, and interact with Iceberg tables.
• Comprehend the procedure for migrating data from alternative table formats to Iceberg.

This live, instructor-led training in Latvia (delivered online or onsite) is tailored for intermediate-level data scientists and engineers aiming to employ Google Colab and Apache Spark for big data processing and analytics.

Following this training, participants will be equipped to:

• Establish a big data framework utilizing Google Colab and Spark.
• Efficiently process and analyze extensive datasets with Apache Spark.
• Visualize big data in a collaborative setting.
• Integrate Apache Spark with cloud-based platforms.

This instructor-led live training, delivered either online or onsite, targets data engineers, analysts, and professionals looking to utilize Databricks and PySpark for constructing scalable data pipelines and migrating legacy SQL workflows.

This program provides a practical foundation for constructing scalable data processing and Machine Learning workflows with PySpark. It teaches participants how Apache Spark integrates into modern Big Data ecosystems and how to utilize distributed computing to process large datasets efficiently.

Over the course of three intensive days, this practical training programme centres on the development and refinement of high-efficiency data processing workflows leveraging PySpark, Pandas, and Polars within Kubernetes-based ecosystems.

Learners will cultivate a hands-on grasp of Spark application execution on Kubernetes, specifically examining how configuration decisions at the application level impact performance, scalability, resource utilization, and operational costs. Key optimisation topics include sizing executors, managing memory allocation, dynamic allocation, partitioning methodologies, shuffle dynamics, mitigating small-file issues, and enhancing Parquet processing efficiency.

Additionally, the course tackles frequent obstacles encountered in Pandas usage, such as memory constraints and out-of-memory exceptions, while introducing Polars as a high-performance alternative for specific data processing tasks. Through practical exercises, participants will learn to identify performance and memory bottlenecks, evaluate various configuration strategies, and implement optimisation techniques in realistic ETL and machine learning contexts.

The primary focus remains on practical decision-making: gaining the ability to pinpoint bottlenecks, select the most suitable tool, configure Spark for peak efficiency, and strike a balance between performance and infrastructure resource consumption and cost.

This instructor-led, live training in Latvia (online or onsite) is designed for engineers seeking to set up and deploy Apache Spark systems for processing vast amounts of data.

By the end of this program, participants will have the ability to:

• Install and configure Apache Spark.
• Swiftly process and analyze large-scale data sets.
• Understand the distinctions between Apache Spark and Hadoop MapReduce, and determine when to apply each.
• Integrate Apache Spark with external machine learning tools.

This live, instructor-led training in Latvia, offered online or onsite, targets system administrators from beginner to intermediate levels looking to deploy, maintain, and optimize Spark clusters.

By the conclusion of the program, participants will be equipped to:

• Deploy and configure Apache Spark in multiple settings.
• Administer cluster resources and keep track of Spark applications.
• Improve the performance metrics of Spark clusters.
• Establish security controls and ensure high availability.
• Identify and fix typical Spark operational problems.

Join this instructor-led, live training in Latvia, where you will discover how to leverage the combined power of Python and Spark for big data analysis through practical, hands-on exercises.

Upon completion, participants will be equipped to:

• Apply Spark and Python synergistically to process and analyze large datasets.
• Engage with exercises that replicate real-world scenarios.
• Leverage various tools and methodologies for big data analytics using PySpark.

This instructor-led live training in Latvia (offered online or onsite) targets developers who aim to utilize and integrate Spark, Hadoop, and Python for the processing, analysis, and transformation of large and complex datasets.

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

• Configure the necessary environment to commence big data processing with Spark, Hadoop, and Python.
• Comprehend the features, core components, and architecture of Spark and Hadoop.
• Integrate Spark, Hadoop, and Python effectively for big data operations.
• Explore essential tools in the Spark ecosystem, such as Spark MlLib, Spark Streaming, Kafka, Sqoop, and Flume.
• Construct collaborative filtering recommendation systems akin to those found on Netflix, YouTube, Amazon, Spotify, and Google.
• Utilize Apache Mahout to scale machine learning algorithms.