IBM Datastage For Administrators and Developers Training Course

This instructor-led, live training in Nigeria (online or onsite) is tailored for advanced-level data professionals who want to optimise data processing workflows, ensure data integrity, and implement robust data lakehouse solutions capable of handling the complexities of modern big data applications.

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

• Gain a thorough understanding of Iceberg’s architecture, including metadata management and file layout.
• Configure Iceberg for optimal performance across 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 optimise 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 Nigeria (online or onsite) is aimed 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 of Iceberg tables.
• Understand the process of migrating data from other table formats to Iceberg.

This instructor-led, live training in Nigeria (online or onsite) is aimed at intermediate-level data scientists and engineers who wish to use 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 business practices across numerous sectors, including the government sector. The rates of government data generation and digital archiving are climbing rapidly, driven by the proliferation of mobile devices and applications, smart sensors and gadgets, cloud computing solutions, and citizen-facing portals. As digital information expands and becomes increasingly complex, the management, processing, storage, security, and disposition of data also grow more intricate. New tools for capture, search, discovery, and analysis are enabling organizations to extract valuable insights from unstructured data. The government sector is at a critical juncture, recognizing information as a strategic asset. Governments must now protect, leverage, and analyze both structured and unstructured information to better serve the public and meet mission objectives. As government leaders strive to evolve into data-driven organizations that successfully achieve their missions, they are laying the groundwork to correlate dependencies across events, people, processes, and information.

High-value 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 serves as an intelligent industry solution that empowers the government to make superior decisions by acting on patterns revealed through the analysis of large volumes of data—whether related or unrelated, structured or unstructured.

However, achieving these results requires far more than simply 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 locate these technologies by establishing the National Big Data Research and Development Initiative in 2012. This initiative allocated over $200 million to capitalize on the explosion of Big Data and the tools necessary to analyze it.

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

Effectively analyzing data is another major hurdle. Many agencies utilize commercial tools that allow them to sift through mountains of data, identifying trends that can enhance operational efficiency. (A recent study by MeriTalk 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 need to analyze data. For instance, 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 connect job candidates with hiring managers.

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

Participants will have the opportunity to put this knowledge into practice through hands-on exercises. Group interaction and instructor feedback make up an important component of the class.

The course starts with an introduction to elemental concepts of Big Data, then progresses into the programming languages and methodologies used to perform Data Analysis. Finally, we discuss the tools and infrastructure that enable Big Data storage, Distributed Processing, and Scalability.

Informatica with Big Data (BDM) is a programme designed to empower data professionals to develop, manage, and analyse large datasets by leveraging the latest technologies and architectures in the Big Data sector. The course covers the entire data lifecycle, including ingestion, integration, cleansing, curation, analytics, and the exposure and consumption of big data services.
Participants will explore solutions for processing large datasets using Big Data technologies and architectures such as Apache Hive, Apache Hadoop, and Apache Spark. The course provides hands-on experience with Informatica tools like Bloombox, Big Data Management, and iData Fabric to grasp big data technologies including MapReduce and Hadoop. By the end of the course, learners will be capable of creating end-to-end data solutions using Informatica and its associated Big Data offerings.

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

This instructor-led live training, available onsite or remotely, targets intermediate-level administrators and engineers aiming 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 operations, and resolve technical issues.

Course Format

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

Customization Options

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

This training offers a hands-on introduction to constructing scalable data processing and Machine Learning workflows using PySpark. Participants will gain insight 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 Nigeria (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 Nigeria (online or onsite) is aimed at beginner-level to intermediate-level system administrators who wish to deploy, maintain, and optimize Spark clusters.

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

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

The learning curve for Apache Spark can be steep at the beginning, requiring significant effort before seeing initial results. This course is designed to help you navigate those early challenges. 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 and tasks. Aligning with best practices, the course places a strong emphasis on cloud deployment, specifically focusing on Databricks and AWS. Students will also learn the key differences 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 Nigeria, participants will learn how to use Python and Spark together to analyse big data while working through hands-on exercises.

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

• Learn how to use Spark with Python to analyse 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 Nigeria (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-driven platform that unifies big data capabilities, artificial intelligence, and governance into a single cohesive solution. Its Rocket and Intelligence modules empower organizations to conduct rapid data exploration, transformation, and advanced analytics within enterprise settings.

This instructor-led training session, available both online and onsite, is tailored 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 (UDFs), and sophisticated data logic.

Upon completing this training, participants will be equipped to:

• Effectively navigate and utilize the Stratio platform, specifically its Rocket and Intelligence modules.
• Apply PySpark for data ingestion, transformation, and analytical tasks.
• Utilize loops and conditional logic to manage data workflows and perform feature engineering.
• Develop and manage user-defined functions (UDFs) to facilitate reusable data operations within PySpark.

Course Delivery Format

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

Options for Course Customization

• For tailored training requirements, please reach out to us to arrange a customized session.