Data Streaming and Real Time Data Processing Training Course

This instructor-led, live training in Serbia (online or onsite) targets 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 Serbia (online or onsite) is intended for beginner-level data professionals who want to acquire the knowledge and skills needed to effectively use 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 various 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 Serbia (available online or onsite) is targeted at intermediate-level data scientists and engineers interested in employing Google Colab and Apache Spark for big data processing and analytics.

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

• Configure a big data environment using Google Colab and Spark.
• Efficiently process and analyze large datasets via Apache Spark.
• Integrate Apache Spark with cloud-based tools.

Technological advancements and the exponential growth of information are reshaping business operations across numerous sectors, including government. Government data creation and digital archiving are accelerating, driven by the proliferation of mobile devices and apps, smart sensors, cloud computing solutions, and citizen-facing portals. As digital information becomes more voluminous and complex, the management, processing, storage, security, and retention of this data become increasingly challenging. New tools for capture, search, discovery, and analysis are enabling organizations to derive valuable insights from unstructured data. The government sector is at a critical juncture, recognizing information as a strategic asset. Governments must protect, leverage, and analyze both structured and unstructured information to better serve citizens and meet mission requirements. As government leaders strive to evolve into data-driven organizations, they are establishing the foundation to correlate dependencies across events, personnel, processes, and information.

High-impact government solutions are emerging from the integration of disruptive technologies:

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

Big Data serves as an intelligent industry solution, enabling governments to make better decisions by acting on patterns revealed through the analysis of large volumes of data—both related and unrelated, structured and unstructured.

However, achieving these goals requires more than just accumulating vast 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 helping agencies identify these technologies by establishing the National Big Data Research and Development Initiative in 2012. This initiative included over $200 million to capitalize on the Big Data explosion and the tools needed to analyze it.

The challenges posed by Big Data are nearly as daunting as its promise is encouraging. Efficient data storage is one such challenge. With budgets always tight, agencies must minimize the cost per megabyte of storage and ensure data is easily accessible, allowing users to retrieve it when and how they need it. Backing up massive quantities of data further compounds this challenge.

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

Custom-developed Big Data tools are also enabling agencies to meet their analysis needs. For example, the 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 routine tasks, such as screening resumes to connect job candidates with hiring managers.

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

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

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

Informatica with Big Data (BDM) is a program aimed at empowering data professionals to develop, manage, and analyze large datasets by leveraging the most advanced technologies and architectures in the Big Data sector. The curriculum covers the entire lifecycle, from data ingestion, integration, cleansing, and curation, to data analytics and the exposure and consumption of big data services.
Participants will explore solutions that process massive datasets using Big Data technologies and architectures such as Apache Hive, Apache Hadoop, and Apache Spark. The course also offers hands-on experience with Informatica tools like Bloombox, Big Data Management, and iData Fabric to deepen your understanding of big data technologies like MapReduce and Hadoop. Upon completion, learners will be equipped to create end-to-end data solutions using Informatica and its associated Big Data offerings.

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, supported by a web-based UI and granular control mechanisms.

This instructor-led training, available both onsite and remotely, targets 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 across various sources and sinks.
• Implementing logic for flow automation, routing, and data transformation.
• Optimizing performance, monitoring operations, and troubleshooting issues.

Course Format

• Interactive lectures featuring discussions on real-world architecture.
• Hands-on labs focused on building, deploying, and managing flows.
• Scenario-based exercises conducted in a live-lab environment.

Customization Options

• For customized training tailored to your needs, please contact us to arrange.

This course offers a hands-on introduction to developing scalable data processing and Machine Learning workflows using PySpark. Participants will gain insights into 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 Serbia (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 instructor-led, live training in Serbia (online or onsite) is designed for beginner to intermediate-level system administrators who want to deploy, maintain, and optimize Spark clusters.

Upon completing this training, participants will be able to:

• Install and configure Apache Spark across different 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.

The initial learning curve for Apache Spark is steep, requiring significant effort before yielding tangible results. This course is designed to help you navigate that challenging first phase. Upon completion, participants will grasp the fundamental concepts of Apache Spark, clearly distinguish between RDDs and DataFrames, master the Python and Scala APIs, and comprehend the roles of executors and tasks. Adhering to industry best practices, the curriculum places a strong emphasis on cloud deployment, specifically within Databricks and AWS environments. Students will also explore the distinctions between AWS EMR and AWS Glue, one of AWS's most recent Spark services.

AUDIENCE:

Data Engineer, DevOps, Data Scientist

In this instructor-led, live training in Serbia, participants will learn how to combine Python and Spark to analyze big data while engaging in 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 Serbia (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.

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 facilitate rapid data exploration, transformation, and advanced analytics, making it ideal for enterprise environments.

This instructor-led live training, available both online and onsite, is designed for intermediate-level data professionals eager to master the Rocket and Intelligence modules within the Stratio ecosystem using PySpark. The curriculum emphasizes looping structures, user-defined functions (UDFs), and complex data logic.

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

• Efficiently navigate and operate within the Stratio platform, utilizing both Rocket and Intelligence modules.
• Apply PySpark effectively for data ingestion, transformation, and analytical tasks.
• Utilize loops and conditional logic to orchestrate data workflows and execute feature engineering.
• Develop and manage user-defined functions (UDFs) to create reusable data operations in PySpark.

Course Format

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

Course Customization Options

• For customized training tailored to your specific needs, please reach out to us to make arrangements.