Data Streaming and Real Time Data Processing Training Course

This instructor-led, live training in Kenya (online or onsite) is targeted at advanced-level data professionals who aim to optimize data processing workflows, ensure data integrity, and implement robust data lakehouse solutions capable of handling 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 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 Kenya (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 live, instructor-led training delivered in Kenya (online or on-site) is designed for intermediate-level data scientists and engineers who aim 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 data are reshaping business operations across various sectors, including the public sector. Government entities are generating and digitizing information at unprecedented rates, driven by the proliferation of mobile devices and applications, smart sensors, cloud computing solutions, and citizen-facing digital portals. As digital information becomes more expansive and complex, the management, processing, storage, security, and disposition of this data grow increasingly 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 threshold, recognizing that information is a strategic asset. To better serve citizens and meet mission requirements, governments must protect, leverage, and analyze both structured and unstructured information. As government leaders work to evolve into data-driven organizations capable of achieving their missions, they are establishing the foundations to correlate dependencies across events, people, processes, and information.

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

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

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

However, achieving these outcomes 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," noted 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 significant steps to assist agencies in finding 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 formidable as its promise is encouraging. Efficient data storage remains a key challenge. With budgets often tight, agencies must minimize the per-megabyte cost of storage while ensuring data remains easily accessible for users to retrieve 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 more than $500 billion while also fulfilling mission objectives).

Custom-developed Big Data tools are also enabling agencies to meet their analytical needs. For instance, the Oak Ridge National Laboratory’s Computational Data Analytics Group 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.

Upon completing this instructor-led, live training in Kenya, 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 form a vital part of the learning experience.

The course begins with an introduction to the fundamental concepts of Big Data, then moves on to explore 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.

Informatica with Big Data (BDM) is a specialized course aimed at empowering data professionals to develop, manage, and analyze large datasets by leveraging the latest technologies and architectures in the Big Data landscape. The curriculum covers the entire lifecycle, including data ingestion, integration, cleansing, curation, analytics, and the exposure and consumption of big data services.
During this course, learners will explore solutions that process large datasets using Big Data technologies and architectures such as Apache Hive, Apache Hadoop, and Apache Spark. Practical experience will be provided with Informatica tools like Bloombox, Big Data Management, and iData Fabric to deepen understanding of big data technologies including Map Reduce and Hadoop. Upon completion, learners will be equipped to create end-to-end data solutions using Informatica and its associated Big Data tools.

Apache NiFi is an open-source platform designed for flow-based data integration and event processing. It facilitates the automated, real-time routing, transformation, and mediation of data between diverse systems, featuring a web-based interface and granular control capabilities.

This instructor-led live training, available either onsite or remotely, is tailored for intermediate-level administrators and engineers looking 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 destined for different sinks.
• Implement logic for flow automation, routing, and data transformation.
• Optimize performance, monitor operational metrics, and resolve technical issues.

Course Format

• Interactive lectures incorporating real-world architectural discussions.
• Practical hands-on labs focused on building, deploying, and managing data flows.
• Scenario-based exercises conducted in a live laboratory environment.

Course Customization Options

• To request tailored training for this course, please get in touch with us to arrange the details.

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

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

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

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

The initial learning curve for Apache Spark can be steep, requiring significant effort before yielding results. This course is designed to help you navigate that challenging first phase. Upon completion, participants will grasp the fundamentals of Apache Spark, clearly distinguish between RDDs and DataFrames, and become proficient with the Python and Scala APIs. You will also gain a solid understanding of executors, tasks, and other core concepts. Guided by best practices, the course places a strong emphasis on cloud deployment, with dedicated focus on Databricks and AWS. Additionally, students will explore the differences between AWS EMR and AWS Glue, highlighting one of AWS's latest Spark services.

AUDIENCE:

Data Engineers, DevOps Professionals, Data Scientists

In this instructor-led, live training in Kenya, 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 Kenya (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 serves as a comprehensive, data-centric platform that unifies big data capabilities, artificial intelligence, and governance into one cohesive solution. Through its Rocket and Intelligence modules, organizations can accelerate data exploration, transformation, and advanced analytics within enterprise settings.

This instructor-led training, available either online or onsite, is designed for intermediate data professionals eager to leverage Stratio’s Rocket and Intelligence modules effectively with PySpark. The curriculum emphasizes looping structures, user-defined functions, and sophisticated data logic.

Upon completing this training, participants will be equipped to:

• Navigate the Stratio platform and utilize the Rocket and Intelligence modules proficiently.
• Apply PySpark for data ingestion, transformation, and analytical tasks.
• Employ loops and conditional logic to streamline data workflows and manage feature engineering.
• Develop and manage user-defined functions (UDFs) to enable reusable data operations within PySpark.

Course Format

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

Customization Options

• For personalized training arrangements, please contact us directly.