Big Data Consulting Training Course

This instructor-led, live training in Spain (online or onsite) is designed for advanced data professionals seeking to streamline data processing workflows, secure data integrity, and establish robust data lakehouse solutions that address the complexities of modern big data applications.

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

• Acquire a deep understanding of Iceberg’s architecture, including metadata management and file layout.
• Configure Iceberg for peak performance in diverse environments and integrate it with various data processing engines.
• Administer large-scale Iceberg tables, execute complex schema changes, and manage partition evolution.
• Apply techniques to optimize query performance and enhance data scan efficiency for large datasets.
• Implement strategies to ensure data consistency, manage transactional guarantees, and handle failures in distributed settings.

This live, instructor-led training in Spain (available online or onsite) is tailored for entry-level data professionals aiming to gain the requisite expertise to utilize Apache Iceberg for overseeing large-scale datasets, safeguarding data integrity, and refining data processing operations.

By the conclusion of this program, participants will be capable of:

• Achieving a deep understanding of Apache Iceberg's architecture, features, and advantages.
• Studying table formats, partitioning, schema evolution, and time travel capabilities.
• Installing and configuring Apache Iceberg in diverse environments.
• Creating, managing, and manipulating Iceberg tables.
• Understanding the procedure for migrating data from other table formats to Iceberg.

Tailored for intermediate-level data scientists and engineers, this instructor-led live training in Spain (online or onsite) explores the use of Google Colab and Apache Spark for efficient big data processing and analytics.

By the end of the session, participants will be equipped to:

• Establish a big data environment leveraging Google Colab and Spark.
• Perform efficient processing and analysis of extensive datasets using Apache Spark.
• Present big data visualizations in a collaborative setting.
• Connect Apache Spark with integrated 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 training provides a practical foundation for developing scalable data processing and Machine Learning workflows using PySpark. It covers how Apache Spark integrates into modern Big Data ecosystems and demonstrates efficient large-scale dataset processing through distributed computing.

This three-day practical course focuses on building and optimising efficient data-processing workloads using PySpark, Pandas and Polars in Kubernetes-based environments.

Participants will develop a practical understanding of how Spark applications execute on Kubernetes and how application-level configuration decisions influence performance, scalability, resource consumption and cost. The course covers key optimisation areas including executor sizing, memory allocation, dynamic allocation, partitioning strategies, shuffle behaviour, small-file problems and efficient Parquet processing.

The course also addresses common challenges when working with Pandas, including memory limitations and out-of-memory failures, and introduces Polars as a high-performance alternative for selected data-processing workloads. Through hands-on exercises, participants will diagnose performance and memory issues, compare different configuration strategies and apply optimisation techniques to realistic ETL and machine learning scenarios.

The emphasis throughout the course is on practical decision-making: understanding how to identify bottlenecks, select the appropriate tool, configure Spark efficiently and balance performance with infrastructure resource consumption and cost.

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

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

• Install and configure Apache Spark.
• Rapidly process and analyze very large data sets.
• Understand the distinctions between Apache Spark and Hadoop MapReduce, and select the appropriate tool for specific scenarios.
• Integrate Apache Spark with other machine learning tools.

This live, instructor-led training in Spain, offered online or on-site, targets system administrators from beginner to intermediate levels who seek to deploy, maintain, and optimize Spark clusters.

By the end of the session, participants will have the ability to:

• Install and configure Apache Spark in a variety of environments.
• Manage cluster resources while monitoring Spark applications.
• Optimize the operational performance of Spark clusters.
• Execute security measures and ensure high availability.
• Debug and troubleshoot frequent Spark issues.

In this live, instructor-led training session held in Spain, participants will explore how to combine Python and Spark for big data analysis while completing practical, hands-on exercises.

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

• Use Spark with Python to analyze big data.
• Practice with exercises that simulate real-world cases.
• Employ diverse tools and techniques for big data analysis using PySpark.

This instructor-led, live session, conducted either online or on-site, is crafted for developers aiming to leverage Spark, Hadoop, and Python to process, analyze, and transform extensive and complex datasets.

By the end of this program, participants will be equipped to:

• Establish the requisite environment for initiating big data processing with Spark, Hadoop, and Python.
• Comprehend the features, core components, and architectural design of Spark and Hadoop.
• Learn effective methods for integrating Spark, Hadoop, and Python in big data projects.
• Investigate essential tools within the Spark ecosystem, such as Spark MlLib, Spark Streaming, Kafka, Sqoop, and Flume.
• Create collaborative filtering recommendation systems akin to those found on Netflix, YouTube, Amazon, Spotify, and Google.
• Apply Apache Mahout to optimize the scaling of machine learning algorithms.

This live, instructor-led session in Spain explores the Stratio platform, with a specific focus on integrating the Rocket and Intelligence modules using PySpark. Participants will develop proficiency in data ingestion, transformation, and advanced analytics, acquiring practical expertise in loops, UDFs, and enterprise-grade data workflows.