IBM Datastage For Administrators and Developers Training Course

This instructor-led, live training in Botswana (online or onsite) is tailored for 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.

Upon completion 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 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 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 Botswana (online or onsite) is designed for data scientists and engineers at an intermediate level who wish to utilise Google Colab and Apache Spark for big data processing and analytics.

Upon completing this training, participants will be able to:

• Establish a big data environment using Google Colab and Spark.
• Process and analyse large datasets efficiently with Apache Spark.
• Visualise big data within a collaborative environment.
• Integrate Apache Spark with cloud-based tools.

Apache Hadoop stands as the leading framework for processing Big Data across server clusters. This course, spanning three days with an optional fourth day, equips attendees with a comprehensive understanding of the business advantages and use cases associated with Hadoop and its ecosystem. Participants will learn to plan for cluster deployment and scalability, as well as how to install, maintain, monitor, troubleshoot, and optimise Hadoop systems. Practical skills include performing bulk data loads on clusters, exploring various Hadoop distributions, and installing and managing tools within the Hadoop ecosystem. The course concludes with a focus on securing the cluster using Kerberos.

\u201c...The materials were very well prepared and covered thoroughly. The Lab was very helpful and well organised\u201d
\u2014 Andrew Nguyen, Principal Integration DW Engineer, Microsoft Online Advertising

Audience

Hadoop administrators

Format

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

Informatica with Big Data (BDM) is a specialized course aimed at empowering data professionals to develop, manage, and analyze large datasets by leveraging the most current technologies and architectures in the Big Data domain. The course covers the entire lifecycle, from data ingestion, integration, cleansing, and curation, through to data analytics and the exposure and consumption of big data services.
In this course, participants will explore solutions that process large datasets using Big Data technologies and architectures such as Apache Hive, Apache Hadoop, and Apache Spark. The course also provides hands-on experience with Informatica tools like Bloombox, Big Data Management, and iData Fabric, facilitating a deeper understanding of big data technologies like MapReduce and Hadoop. By the conclusion of this course, 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, flow-based data integration and event-processing platform. It enables automated, real-time data routing, transformation, and system mediation between disparate systems, with a web-based UI and fine-grained control.

This instructor-led, live training (onsite or remote) is aimed at intermediate-level administrators and engineers who wish to deploy, manage, secure, and optimize NiFi dataflows in production environments.

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

• Install, configure, and maintain Apache NiFi clusters.
• Design and manage dataflows from varied sources and sinks.
• Implement flow automation, routing, and transformation logic.
• Optimize performance, monitor operations, and troubleshoot issues.

Format of the Course

• Interactive lecture with real-world architecture discussion.
• Hands-on labs: building, deploying, and managing flows.
• Scenario-based exercises in a live-lab environment.

Course Customization Options

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

In this instructor-led, live training in Botswana, 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 course offers a hands-on introduction to constructing scalable data processing and Machine Learning workflows using PySpark. Attendees will gain insight into how Apache Spark functions within contemporary Big Data ecosystems and learn to process extensive datasets effectively by applying distributed computing principles.

This instructor-led, live training in Botswana (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 Botswana (online or onsite) is aimed at beginner to intermediate system administrators who wish to deploy, maintain, and optimise 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 initial learning curve for Apache Spark can be steep, requiring considerable effort before seeing tangible results. This course is designed to help you surmount that early hurdle. Upon completion, participants will grasp the fundamentals of Apache Spark, clearly distinguish between RDDs and DataFrames, gain proficiency in both Python and Scala APIs, and comprehend the roles of executors and tasks. Furthermore, adhering to industry best practices, the course places strong emphasis on cloud deployment, with specific focus on Databricks and AWS. Students will also learn to differentiate between AWS EMR and AWS Glue, one of AWS's most recent Spark-related services.

AUDIENCE:

Data Engineers, DevOps Professionals, Data Scientists

In this instructor-led, live training in Botswana, participants will learn how to use Python and Spark together to analyze big data while working 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 Botswana (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 dedicated module for handling structured and unstructured data. It exposes information regarding data structure and the computations executed, which facilitates performance optimizations. Spark SQL is primarily utilised for two key purposes:
- Executing SQL queries.
- Reading data from an existing Hive installation.

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

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

• Install and configure Spark SQL.
• Conduct data analysis using Spark SQL.
• Query data sets across various formats.
• Visualise data and query outcomes.

Format of the Course

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

Course Customisation Options

• To request bespoke training for this course, please contact us to make arrangements.

Stratio is a unified platform that brings together big data, artificial intelligence, and governance. Through its Rocket and Intelligence modules, it facilitates swift data exploration, transformation, and advanced analytics for enterprise settings.

This instructor-led live training, available online or onsite, is designed for intermediate data professionals keen on leveraging the Rocket and Intelligence modules within Stratio using PySpark. The focus is on mastering looping structures, user-defined functions, and sophisticated data logic.

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

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

Course Format

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

Customisation Options

• To arrange a tailored training session for this course, please contact us.