Online Big Data Training in Greece

This instructor-led, live training in Greece (online or onsite) is designed for intermediate-level data scientists and engineers seeking to apply Google Colab and Apache Spark to big data processing and analytics.

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

• Establish a big data environment using Google Colab and Spark.
• Process and analyse large datasets efficiently with Apache Spark.
• Visualise big data in a collaborative environment.
• Integrate Apache Spark with cloud-based tools.

Stratio offers a comprehensive, data-centric platform that unifies big data capabilities, artificial intelligence, and governance into a single, cohesive solution. Its Rocket and Intelligence modules empower organizations to perform rapid data exploration, transformation, and advanced analytics within enterprise-grade environments.

This instructor-led live training, available online or on-site, is designed for intermediate-level data professionals seeking to harness the full potential of Stratio’s Rocket and Intelligence modules using PySpark. The curriculum emphasizes looping structures, user-defined functions, and sophisticated data logic to enhance workflow efficiency.

Upon completion of this training, participants will be able to:

• Navigate and effectively utilize the Stratio platform, specifically the Rocket and Intelligence modules.
• Apply PySpark techniques for data ingestion, transformation, and analysis within the Stratio ecosystem.
• Implement loops and conditional logic to manage data workflows and execute feature engineering tasks.
• Develop and manage User-Defined Functions (UDFs) to enable reusable data operations in PySpark.

Course Format

• Interactive lectures paired with group discussions.
• Extensive hands-on exercises and practical practice sessions.
• Real-world implementation through a live-lab environment.

Customization Options

• For tailored training solutions, please reach out to us to discuss your specific requirements.

Data Warehousing involves the design, construction, and management of centralized data repositories that facilitate analytics, reporting, and strategic decision-making.

This instructor-led live training (available online or onsite) targets intermediate-level data professionals seeking to master dimensional data modeling, develop robust ETL pipelines, and optimize analytical workloads.

Upon completing this training, participants will be able to:

• Articulate fundamental data warehousing concepts and architectures.
• Design dimensional models and select appropriate star or snowflake schemas.
• Construct and orchestrate reliable ETL and ELT pipelines.
• Distinguish between OLTP and OLAP workloads and optimize systems for analytical performance.

Course Format

• Interactive lectures and discussions.
• Extensive exercises and practical application.
• Hands-on implementation within a live-lab environment.

Customization Options

• To arrange customized training for this course, please contact us.

Participants who complete this instructor-led, live training in Greece will gain a practical, real-world understanding of Big Data, along with its associated technologies, methodologies, and tools.

Attendees will have the chance to apply this knowledge through hands-on exercises. Group interaction and feedback from the instructor are integral components of the class.

The course begins with an introduction to the fundamental concepts of Big Data, then moves on to the programming languages and methodologies used for Data Analysis. Finally, we examine the tools and infrastructure that facilitate Big Data storage, distributed processing, and scalability.

Greenplum is an open-source massively parallel processing (MPP) data platform specifically designed for analytics and large-scale data environments.

This instructor-led live training, available online or onsite, targets intermediate to advanced technical users seeking to grasp Greenplum’s internal architecture, construct optimized data models, and implement high-performance SQL within distributed environments.

Upon completion of this training, participants will be able to:

• Comprehend Greenplum’s shared-nothing architecture and its parallelism model.
• Design distributed table structures and partitions tailored for optimal query performance.
• Utilize indexing strategies, storage formats, and physical design techniques.
• Interpret EXPLAIN plans and optimize queries using established best practices.

Course Format

• Interactive lectures and discussions.
• Extensive exercises and practical application.
• Hands-on implementation within a live laboratory environment.

Customization Options

• For customized training arrangements, please contact us directly.

Greenplum is an open-source massively parallel data platform built on PostgreSQL, designed for advanced analytics and large-scale data warehousing.

This instructor-led live training (available online or onsite) targets intermediate-level administrators who want to effectively install, configure, and manage Greenplum environments, including handling system updates and libraries.

Upon completing this training, participants will be able to:

• Install and configure a production-ready Greenplum cluster.
• Manage system updates and apply patches safely.
• Handle Greenplum library management for analytics and extensions.
• Troubleshoot and monitor Greenplum systems.

Format of the Course

• Interactive lecture and discussion.
• Hands-on use of Greenplum in a controlled lab environment.
• Guided exercises focused on cluster setup, upgrade workflows, and system extensions.

Course Customization Options

• To request a customized training for this course based on your environment or data platform, please contact us to arrange.

This instructor-led live training in Greece (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 Greece (online or onsite) is designed for 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 completion of this training, participants will be able to:

• Gain a comprehensive understanding of Apache Iceberg's architecture, features, and advantages.
• Learn about table formats, partitioning, schema evolution, and time travel capabilities.
• Install and configure Apache Iceberg across various environments.
• Create, manage, and manipulate Iceberg tables.
• Understand the process of migrating data from other table formats to Iceberg.

This instructor-led training, available in Greece (online or onsite), targets intermediate IT professionals who wish to enhance their skills in data architecture, governance, cloud computing, and big data technologies. The objective is to enable effective management and analysis of large datasets for data migration within their organizations.

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 Greece (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 Greece (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.

Big Data and Advanced Analytics involves the use of sophisticated techniques and tools to analyse large, complex datasets, yielding actionable insights and supporting strategic decision-making.

This instructor-led live training, available online or on-site, is designed for advanced-level data professionals who wish to leverage cutting-edge analytical methods and big data technologies for predictive, prescriptive, and real-time analytics.

Upon completion of this training, participants will be able to:

• Design and implement large-scale data processing pipelines for both structured and unstructured data.
• Apply advanced machine learning and deep learning techniques to massive datasets.
• Utilise distributed computing frameworks for real-time analytics and data streaming.
• Integrate big data analytics into business intelligence and decision-making systems.

Format of the Course

• Interactive lectures and discussions.
• Extensive exercises and practice sessions.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To arrange customized training for this course, please contact us.

This instructor-led, live training in Greece (online or on-site) is designed for intermediate-level IT professionals seeking a comprehensive understanding of IBM DataStage from both administrative and developmental perspectives. This knowledge enables them to manage and utilize the tool effectively in their respective workplaces.

Upon completion of this training, participants will be able to:

• Comprehend the fundamental concepts of DataStage.
• Effectively install, configure, and oversee DataStage environments.
• Establish connections with various data sources and efficiently extract data from databases, flat files, and external systems.
• Apply efficient data loading techniques.

This instructor-led, live training in Greece (online or onsite) targets beginner to intermediate system administrators who wish to deploy, maintain, and optimize Spark clusters.

Upon completing this training, participants will be able to:

• Install and configure Apache Spark across diverse environments.
• Manage cluster resources and monitor Spark applications.
• Enhance the performance of Spark clusters.
• Implement security protocols and ensure high availability.
• Debug and resolve common Spark issues.

In this instructor-led live training in Greece, participants will learn how to leverage Python and Spark together to analyze big data while completing hands-on exercises.

Upon completion of this training, participants will be able to:

• Utilize Spark with Python to analyze Big Data.
• Complete exercises that simulate real-world scenarios.
• Apply various tools and techniques for big data analysis using PySpark.

Technological advancements and the exponential growth of information are fundamentally transforming business operations across numerous industries, including the public sector. Government data generation and digital archiving are accelerating, driven by the rapid proliferation of mobile devices and applications, smart sensors, cloud computing solutions, and citizen-facing portals. As digital information expands and becomes increasingly complex, the tasks of managing, processing, storing, securing, and disposing of that data also become more intricate. New tools for capture, search, discovery, and analysis are enabling organizations to extract valuable insights from their unstructured data. The government sector is reaching a critical juncture, recognizing information as a strategic asset. Governments must now protect, leverage, and analyze both structured and unstructured data to better serve citizens and meet mission requirements. As government leaders strive to evolve into data-driven organizations to successfully achieve their missions, they are establishing the foundation to correlate dependencies across events, people, processes, and information.

High-impact government solutions will emerge from a combination of the most disruptive technologies:

• Mobile devices and applications
• Cloud services
• Social business technologies and networking
• Big Data and analytics

Big Data represents a transformative industry solution, allowing governments to make better decisions by taking action based on patterns revealed through the analysis of large volumes of data—whether related or unrelated, structured or unstructured.

However, achieving these capabilities requires more than just accumulating massive quantities of data. "Making sense of these volumes of Big Data requires cutting-edge tools and technologies that can analyze and extract useful knowledge from vast and diverse streams of information," Tom Kalil and Fen Zhao of the White House Office of Science and Technology Policy wrote in a post on the OSTP Blog.

The White House took a significant step toward helping agencies identify these technologies by establishing the National Big Data Research and Development Initiative in 2012. This initiative allocated over $200 million to maximize the potential of the Big Data explosion and the tools necessary to analyze it.

The challenges posed by Big Data are nearly as daunting as the opportunities are encouraging. Efficient data storage is one such challenge. With budgets always tight, agencies must minimize the per-megabyte cost of storage while ensuring data remains easily accessible so users can retrieve it whenever and however they need it. Backing up massive amounts of data further intensifies this challenge.

Effective data analysis is another major hurdle. Many agencies utilize commercial tools that enable them to sift through mountains of data, identifying trends that can enhance operational efficiency. (A recent MeriTalk study found that federal IT executives believe Big Data could help agencies save more than $500 billion while also fulfilling mission objectives.)

Custom-developed Big Data tools are also enabling agencies to address their analytical needs. For example, Oak Ridge National Laboratory’s Computational Data Analytics Group has made its Piranha data analytics system available to other agencies. This system has helped medical researchers identify links that can alert doctors to aortic aneurysms before they occur. It is also used for more routine tasks, such as sifting through resumes to match job candidates with hiring managers.

In this instructor-led, live training in Greece, 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.

Big Data encompasses solutions designed for the storage and processing of large-scale datasets. Initially developed by Google, these Big Data frameworks have evolved and inspired numerous analogous projects, many of which are available as open-source software. R has established itself as a widely used programming language within the financial sector.

Course Overview

This programme offers a practical and structured entry point into constructing real time data streaming systems. It explores core concepts, architectural patterns, and the industry-standard tools required to process continuous data at scale. Participants will acquire the skills to design, implement, and optimise streaming pipelines using contemporary frameworks. The curriculum advances from foundational principles to practical applications, empowering learners to confidently develop production-grade real time solutions.

Training Format

• Instructor-led sessions with guided explanations

• Concept walkthroughs featuring real world examples

• Hands on demonstrations and coding exercises

• Progressive labs aligned with daily topics

• Interactive discussions and Q&A

Course Objectives

• Grasp the concepts of real time data streaming and system architecture

• Distinguish between batch and streaming data processing models

• Design scalable and fault tolerant streaming pipelines

• Work with distributed streaming tools and frameworks

• Apply event time processing, windowing, and stateful operations

• Build and optimise real time data solutions for specific business use cases

Apache NiFi is an open-source platform designed for flow-based data integration and event processing. It facilitates automated, real-time data routing, transformation, and system mediation between diverse systems, featuring a web-based UI that offers fine-grained control.

This instructor-led training session, available either on-site or remotely, targets intermediate-level administrators and engineers who aim to deploy, manage, secure, and optimize NiFi dataflows within production environments.

Upon completing this training, participants will be equipped to:

• Install, configure, and maintain Apache NiFi clusters.
• Design and manage dataflows originating from various sources and destinations.
• Implement logic for flow automation, routing, and transformation.
• Optimize performance, monitor operational metrics, and troubleshoot issues.

Course Format

• Interactive lectures accompanied by discussions on real-world architectures.
• Hands-on labs focused on building, deploying, and managing data flows.
• Scenario-based exercises conducted in a live laboratory environment.

Course Customization Options

• For organizations seeking a tailored training experience for this course, please reach out to us to make arrangements.

This training offers a hands-on introduction to developing scalable data processing and Machine Learning workflows utilizing PySpark. Participants will discover how Apache Spark functions within contemporary Big Data ecosystems and learn to process large datasets efficiently by applying distributed computing principles.

This instructor-led live training in Greece (online or on-site) is aimed at engineers who wish to set up and deploy an 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.

While Apache Spark initially presents a steep learning curve requiring considerable effort to yield early results, this course is designed to help learners navigate and overcome that initial hurdle. Upon completion, participants will gain a solid grasp of Apache Spark fundamentals, clearly distinguish between RDDs and DataFrames, become proficient with the Python and Scala APIs, and understand core concepts such as executors and tasks. Aligned with industry best practices, the curriculum places a strong emphasis on cloud deployment, specifically targeting Databricks and AWS. Students will also explore the distinctions between AWS EMR and AWS Glue, which represents one of AWS's newer Spark-based services.

AUDIENCE:

Data Engineers, DevOps Professionals, Data Scientists

Spark SQL serves as the module within Apache Spark designed for processing both structured and unstructured data. It offers insights into the data's structure and the computations being executed, enabling performance optimizations. Spark SQL is commonly utilized to:
- execute SQL queries;
- access data from existing Hive installations.

Through this instructor-led live training (available onsite or remotely), participants will learn to analyze diverse datasets using Spark SQL.

Upon completion of this training, participants will be equipped to:

• Install and configure Spark SQL.
• Conduct data analysis with Spark SQL.
• Query datasets in various formats.
• Visualize data and query outcomes.

Course Format

• Interactive lectures and discussions.
• Extensive exercises and practical sessions.
• Hands-on implementation in a live-lab environment.

Customization Options

• To request a tailored training for this course, please contact us to arrange.

This instructor-led, live training in Greece (online or onsite) is designed for technical professionals who want to deploy Talend Open Studio for Big Data to streamline the process of reading and analyzing big data.

Upon completion of this training, participants will be able to:

• Install and configure Talend Open Studio for Big Data.
• Connect to 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.