IBM Datastage For Administrators and Developers 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 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.

Apache Hadoop stands as the leading framework for processing Big Data across server clusters. Over the course of three (or optionally four) days, participants will explore the business advantages and practical applications of Hadoop and its ecosystem. The curriculum covers cluster deployment planning, scaling strategies, as well as installation, maintenance, monitoring, troubleshooting, and optimization techniques. Attendees will engage in hands-on practice with bulk data loading, become acquainted with various Hadoop distributions, and learn to install and manage ecosystem tools. The course concludes with a discussion on securing clusters using Kerberos.

“…The materials were very well prepared and covered thoroughly. The Lab was very helpful and well organized”
— Andrew Nguyen, Principal Integration DW Engineer, Microsoft Online Advertising

Target Audience

Hadoop system administrators

Course Format

A blend of lectures and practical labs, with an approximate split of 60% lectures and 40% hands-on labs.

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.

In this instructor-led, live training in Kenya, participants will learn the fundamentals of flow-based programming as they develop a number of demo extensions, components and processors using Apache NiFi.

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

• Understand NiFi's architecture and dataflow concepts.
• Develop extensions using NiFi and third-party APIs.
• Custom develop their own Apache Nifi processor.
• Ingest and process real-time data from disparate and uncommon file formats and data sources.

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.

Spark SQL serves as Apache Spark's component for handling both structured and unstructured data. It supplies details regarding data structure and the computations executed, enabling performance optimizations. Spark SQL is commonly used to:
- execute SQL queries.
- access data from an existing Hive setup.

During this instructor-led live training (available onsite or remotely), participants will acquire skills to analyze diverse data sets using Spark SQL.

Upon completing this training, participants will be capable of:

• Setting up and configuring Spark SQL.
• Conducting data analysis using Spark SQL.
• Querying data sets in various formats.
• Visualizing data and querying outcomes.

Course Format

• Engaging lectures and discussions.
• Extensive exercises and practical sessions.
• Practical implementation within a live laboratory environment.

Course Customization Options

• For customized training on this course, please contact us to make arrangements.

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.