Data Streaming and Real Time Data Processing Training Course

This instructor-led, live training in Turkey (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 Turkey (online or onsite) is targeted 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 Turkey (online or onsite) is tailored for intermediate-level data scientists and engineers who want to utilize 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.

Technological advancements and the exponential growth of information are reshaping operational practices across numerous sectors, including government. The generation of government data and the rates of digital archiving are climbing, driven by the rapid expansion of mobile devices and applications, smart sensors and IoT devices, cloud computing solutions, and citizen-facing portals. As digital information becomes more expansive and complex, the management, processing, storage, security, and disposition of this data also become more intricate. New tools for capture, search, discovery, and analysis are enabling organizations to derive meaningful insights from unstructured data. The government sector is reaching a critical juncture, recognizing information as a strategic asset. To better serve the public and fulfill mission requirements, governments must protect, leverage, and analyze both structured and unstructured information. As government leaders strive to evolve their organizations into data-driven entities, they are establishing the foundation to correlate dependencies among events, personnel, processes, and information.

High-value government solutions will emerge from the integration of several disruptive technologies:

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

Big Data represents a transformative industry solution that empowers government agencies to make superior decisions by acting on patterns revealed through the analysis of vast volumes of data—whether related or unrelated, structured or unstructured.

However, achieving these outcomes requires more than merely accumulating large amounts 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," wrote Tom Kalil and Fen Zhao of the White House Office of Science and Technology Policy in a post on the OSTP Blog.

The White House took a significant step toward assisting agencies in identifying 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 promise it offers is encouraging. Efficient data storage is one such challenge. With budgets remaining tight, agencies must minimize the per-megabyte cost of storage while ensuring data remains easily accessible, allowing users to retrieve information when and how they need it. Backing up massive quantities of data further intensifies this challenge.

Effective data analysis presents another major hurdle. Many agencies utilize commercial tools to sift through vast amounts of data, identifying trends that enhance operational efficiency. (A recent study by MeriTalk revealed that federal IT executives believe Big Data could help agencies save over $500 billion while simultaneously fulfilling mission objectives.)

Custom-developed Big Data tools are also enabling agencies to meet their analytical needs. For instance, the Computational Data Analytics Group at Oak Ridge National Laboratory has made its Piranha data analytics system available to other agencies. This system has assisted medical researchers in identifying links that can alert doctors to aortic aneurysms before they occur. It is also employed for routine tasks, such as screening resumes to connect job candidates with hiring managers.

By completing this instructor-led, live training in Turkey, participants will acquire a practical, real-world understanding of Big Data, along with its associated technologies, methodologies, and tools.

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

The course begins with an introduction to the foundational concepts of Big Data, then moves on to the programming languages and methodologies used for Data Analysis. Finally, we discuss the tools and infrastructure that facilitate Big Data storage, Distributed Processing, and Scalability.

Big Data and Advanced Analytics involves utilizing sophisticated techniques and tools to examine large, complex datasets, thereby generating actionable insights and supporting strategic decision-making.

This instructor-led training, available online or onsite, is designed for advanced-level data professionals eager to harness cutting-edge analytical methods and big data technologies for predictive, prescriptive, and real-time analytics.

Upon completing this training, participants will be capable of:

• Designing and implementing large-scale data processing pipelines for both structured and unstructured data.
• Applying advanced machine learning and deep learning techniques to massive datasets.
• Leveraging distributed computing frameworks for real-time analytics and data streaming.
• Integrating big data analytics into business intelligence and decision-making systems.

Course Format

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

Course Customization Options

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

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 disparate systems, featuring a web-based user interface and granular control capabilities.

This instructor-led live training, available onsite or remotely, is designed for intermediate-level administrators and engineers looking to deploy, manage, secure, and optimize NiFi dataflows within production environments.

Upon completion of this training, participants will be capable of:

• Installing, configuring, and maintaining Apache NiFi clusters.
• Designing and managing dataflows connecting various sources and sinks.
• Implementing logic for flow automation, routing, and data transformation.
• Optimizing performance, monitoring operations, and resolving technical issues.

Course Format

• Interactive lectures complemented by real-world architecture discussions.
• Practical labs focused on building, deploying, and managing flows.
• Scenario-based exercises conducted in a live laboratory environment.

Customization Options

• To arrange a customized version of this training, please contact us.

This course offers a hands-on introduction to creating scalable data processing and Machine Learning workflows with PySpark. Attendees will explore how Apache Spark functions within contemporary Big Data ecosystems and learn to efficiently process extensive datasets using distributed computing principles.

This instructor-led live training in Turkey (online or onsite) 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.

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

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

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

Although the initial learning curve for Apache Spark can be steep, requiring significant effort to see results, this course is designed to help you navigate that challenging phase quickly. Upon completion, participants will grasp the fundamentals of Apache Spark, clearly distinguish between RDDs and DataFrames, and master both the Python and Scala APIs. You will also gain a deep understanding of executors, tasks, and other core concepts. Aligned with industry best practices, the curriculum places strong emphasis on cloud deployment, particularly within Databricks and AWS environments. Students will learn to differentiate between AWS EMR and AWS Glue, one of AWS’s latest Spark services.

AUDIENCE:

Data Engineers, DevOps Professionals, Data Scientists

In this instructor-led, live training in Turkey, participants will learn how to use Python and Spark together to analyze big data as they work on hands-on exercises.

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

• Learn how to use Spark with Python to analyze Big Data.
• Work on exercises that mimic real world cases.
• Use different tools and techniques for big data analysis using PySpark.

This instructor-led, live training in Turkey (online or onsite) is designed for developers who wish to use and integrate Spark, Hadoop, and Python to process, analyze, and transform large and complex datasets.

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

• Configure the necessary environment to begin processing big data using Spark, Hadoop, and Python.
• Gain a comprehensive understanding of the features, core components, and architecture of Spark and Hadoop.
• Master the integration of Spark, Hadoop, and Python for efficient big data processing.
• Explore key tools within the Spark ecosystem, including Spark MLlib, Spark Streaming, Kafka, Sqoop, and Flume.
• Develop collaborative filtering recommendation systems akin to those used by Netflix, YouTube, Amazon, Spotify, and Google.
• Utilize Apache Mahout to scale machine learning algorithms.

Stratio is 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 environments.

This instructor-led live training, available both online and onsite, is designed for intermediate-level data professionals aiming to master the Rocket and Intelligence modules in Stratio using PySpark. The curriculum emphasizes looping structures, user-defined functions, and sophisticated data logic.

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

• Navigate and operate effectively within the Stratio platform utilizing the Rocket and Intelligence modules.
• Apply PySpark techniques for data ingestion, transformation, and analysis.
• Utilize loops and conditional logic to manage data workflows and execute feature engineering tasks.
• Create and manage user-defined functions (UDFs) to facilitate reusable data operations within PySpark.

Course Format

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

Customization Options

• For organizations seeking customized training for this course, please contact us to arrange a tailored program.